update

.dockerignore

@@ -1,19 +1,19 @@
-.github
-.DS_Store
-docs
-kubernetes
-node_modules
-/.svelte-kit
-/package
-.env
-.env.*
-vite.config.js.timestamp-*
-vite.config.ts.timestamp-*
-__pycache__
-.idea
-venv
-_old
-uploads
-.ipynb_checkpoints
-**/*.db
+.github
+.DS_Store
+docs
+kubernetes
+node_modules
+/.svelte-kit
+/package
+.env
+.env.*
+vite.config.js.timestamp-*
+vite.config.ts.timestamp-*
+__pycache__
+.idea
+venv
+_old
+uploads
+.ipynb_checkpoints
+**/*.db
 _test

.env.example

@@ -1,13 +1,13 @@
-# Ollama URL for the backend to connect
-# The path '/ollama' will be redirected to the specified backend URL
-OLLAMA_BASE_URL='http://localhost:11434'
-
-OPENAI_API_BASE_URL=''
-OPENAI_API_KEY=''
-
-# AUTOMATIC1111_BASE_URL="http://localhost:7860"
-
-# DO NOT TRACK
-SCARF_NO_ANALYTICS=true
-DO_NOT_TRACK=true
+# Ollama URL for the backend to connect
+# The path '/ollama' will be redirected to the specified backend URL
+OLLAMA_BASE_URL='http://localhost:11434'
+
+OPENAI_API_BASE_URL=''
+OPENAI_API_KEY=''
+
+# AUTOMATIC1111_BASE_URL="http://localhost:7860"
+
+# DO NOT TRACK
+SCARF_NO_ANALYTICS=true
+DO_NOT_TRACK=true
 ANONYMIZED_TELEMETRY=false

.eslintignore

@@ -1,13 +1,13 @@
-.DS_Store
-node_modules
-/build
-/.svelte-kit
-/package
-.env
-.env.*
-!.env.example
-
-# Ignore files for PNPM, NPM and YARN
-pnpm-lock.yaml
-package-lock.json
-yarn.lock
+.DS_Store
+node_modules
+/build
+/.svelte-kit
+/package
+.env
+.env.*
+!.env.example
+
+# Ignore files for PNPM, NPM and YARN
+pnpm-lock.yaml
+package-lock.json
+yarn.lock

.eslintrc.cjs

@@ -1,31 +1,31 @@
-module.exports = {
-	root: true,
-	extends: [
-		'eslint:recommended',
-		'plugin:@typescript-eslint/recommended',
-		'plugin:svelte/recommended',
-		'plugin:cypress/recommended',
-		'prettier'
-	],
-	parser: '@typescript-eslint/parser',
-	plugins: ['@typescript-eslint'],
-	parserOptions: {
-		sourceType: 'module',
-		ecmaVersion: 2020,
-		extraFileExtensions: ['.svelte']
-	},
-	env: {
-		browser: true,
-		es2017: true,
-		node: true
-	},
-	overrides: [
-		{
-			files: ['*.svelte'],
-			parser: 'svelte-eslint-parser',
-			parserOptions: {
-				parser: '@typescript-eslint/parser'
-			}
-		}
-	]
-};
+module.exports = {
+	root: true,
+	extends: [
+		'eslint:recommended',
+		'plugin:@typescript-eslint/recommended',
+		'plugin:svelte/recommended',
+		'plugin:cypress/recommended',
+		'prettier'
+	],
+	parser: '@typescript-eslint/parser',
+	plugins: ['@typescript-eslint'],
+	parserOptions: {
+		sourceType: 'module',
+		ecmaVersion: 2020,
+		extraFileExtensions: ['.svelte']
+	},
+	env: {
+		browser: true,
+		es2017: true,
+		node: true
+	},
+	overrides: [
+		{
+			files: ['*.svelte'],
+			parser: 'svelte-eslint-parser',
+			parserOptions: {
+				parser: '@typescript-eslint/parser'
+			}
+		}
+	]
+};

.gitattributes

@@ -1,2 +1,2 @@
-*.sh text eol=lf
-*.ttf filter=lfs diff=lfs merge=lfs -text
+*.sh text eol=lf
+*.ttf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,309 +1,309 @@
-.DS_Store
-node_modules
-/build
-/.svelte-kit
-/package
-.env
-.env.*
-!.env.example
-vite.config.js.timestamp-*
-vite.config.ts.timestamp-*
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Pyodide distribution
-static/pyodide/*
-!static/pyodide/pyodide-lock.json
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
-
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-lerna-debug.log*
-.pnpm-debug.log*
-
-# Diagnostic reports (https://nodejs.org/api/report.html)
-report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
-
-# Runtime data
-pids
-*.pid
-*.seed
-*.pid.lock
-
-# Directory for instrumented libs generated by jscoverage/JSCover
-lib-cov
-
-# Coverage directory used by tools like istanbul
-coverage
-*.lcov
-
-# nyc test coverage
-.nyc_output
-
-# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
-.grunt
-
-# Bower dependency directory (https://bower.io/)
-bower_components
-
-# node-waf configuration
-.lock-wscript
-
-# Compiled binary addons (https://nodejs.org/api/addons.html)
-build/Release
-
-# Dependency directories
-node_modules/
-jspm_packages/
-
-# Snowpack dependency directory (https://snowpack.dev/)
-web_modules/
-
-# TypeScript cache
-*.tsbuildinfo
-
-# Optional npm cache directory
-.npm
-
-# Optional eslint cache
-.eslintcache
-
-# Optional stylelint cache
-.stylelintcache
-
-# Microbundle cache
-.rpt2_cache/
-.rts2_cache_cjs/
-.rts2_cache_es/
-.rts2_cache_umd/
-
-# Optional REPL history
-.node_repl_history
-
-# Output of 'npm pack'
-*.tgz
-
-# Yarn Integrity file
-.yarn-integrity
-
-# dotenv environment variable files
-.env
-.env.development.local
-.env.test.local
-.env.production.local
-.env.local
-
-# parcel-bundler cache (https://parceljs.org/)
-.cache
-.parcel-cache
-
-# Next.js build output
-.next
-out
-
-# Nuxt.js build / generate output
-.nuxt
-dist
-
-# Gatsby files
-.cache/
-# Comment in the public line in if your project uses Gatsby and not Next.js
-# https://nextjs.org/blog/next-9-1#public-directory-support
-# public
-
-# vuepress build output
-.vuepress/dist
-
-# vuepress v2.x temp and cache directory
-.temp
-.cache
-
-# Docusaurus cache and generated files
-.docusaurus
-
-# Serverless directories
-.serverless/
-
-# FuseBox cache
-.fusebox/
-
-# DynamoDB Local files
-.dynamodb/
-
-# TernJS port file
-.tern-port
-
-# Stores VSCode versions used for testing VSCode extensions
-.vscode-test
-
-# yarn v2
-.yarn/cache
-.yarn/unplugged
-.yarn/build-state.yml
-.yarn/install-state.gz
-.pnp.*
-
-# cypress artifacts
-cypress/videos
-cypress/screenshots
-.vscode/settings.json
+.DS_Store
+node_modules
+/build
+/.svelte-kit
+/package
+.env
+.env.*
+!.env.example
+vite.config.js.timestamp-*
+vite.config.ts.timestamp-*
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Pyodide distribution
+static/pyodide/*
+!static/pyodide/pyodide-lock.json
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+.pnpm-debug.log*
+
+# Diagnostic reports (https://nodejs.org/api/report.html)
+report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+*.lcov
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# Bower dependency directory (https://bower.io/)
+bower_components
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (https://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules/
+jspm_packages/
+
+# Snowpack dependency directory (https://snowpack.dev/)
+web_modules/
+
+# TypeScript cache
+*.tsbuildinfo
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Optional stylelint cache
+.stylelintcache
+
+# Microbundle cache
+.rpt2_cache/
+.rts2_cache_cjs/
+.rts2_cache_es/
+.rts2_cache_umd/
+
+# Optional REPL history
+.node_repl_history
+
+# Output of 'npm pack'
+*.tgz
+
+# Yarn Integrity file
+.yarn-integrity
+
+# dotenv environment variable files
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# parcel-bundler cache (https://parceljs.org/)
+.cache
+.parcel-cache
+
+# Next.js build output
+.next
+out
+
+# Nuxt.js build / generate output
+.nuxt
+dist
+
+# Gatsby files
+.cache/
+# Comment in the public line in if your project uses Gatsby and not Next.js
+# https://nextjs.org/blog/next-9-1#public-directory-support
+# public
+
+# vuepress build output
+.vuepress/dist
+
+# vuepress v2.x temp and cache directory
+.temp
+.cache
+
+# Docusaurus cache and generated files
+.docusaurus
+
+# Serverless directories
+.serverless/
+
+# FuseBox cache
+.fusebox/
+
+# DynamoDB Local files
+.dynamodb/
+
+# TernJS port file
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+.vscode-test
+
+# yarn v2
+.yarn/cache
+.yarn/unplugged
+.yarn/build-state.yml
+.yarn/install-state.gz
+.pnp.*
+
+# cypress artifacts
+cypress/videos
+cypress/screenshots
+.vscode/settings.json

.npmrc

@@ -1 +1 @@
-engine-strict=true
+engine-strict=true

.prettierignore

@@ -1,316 +1,316 @@
-# Ignore files for PNPM, NPM and YARN
-pnpm-lock.yaml
-package-lock.json
-yarn.lock
-
-kubernetes/
-
-# Copy of .gitignore
-.DS_Store
-node_modules
-/build
-/.svelte-kit
-/package
-.env
-.env.*
-!.env.example
-vite.config.js.timestamp-*
-vite.config.ts.timestamp-*
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
-
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-lerna-debug.log*
-.pnpm-debug.log*
-
-# Diagnostic reports (https://nodejs.org/api/report.html)
-report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
-
-# Runtime data
-pids
-*.pid
-*.seed
-*.pid.lock
-
-# Directory for instrumented libs generated by jscoverage/JSCover
-lib-cov
-
-# Coverage directory used by tools like istanbul
-coverage
-*.lcov
-
-# nyc test coverage
-.nyc_output
-
-# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
-.grunt
-
-# Bower dependency directory (https://bower.io/)
-bower_components
-
-# node-waf configuration
-.lock-wscript
-
-# Compiled binary addons (https://nodejs.org/api/addons.html)
-build/Release
-
-# Dependency directories
-node_modules/
-jspm_packages/
-
-# Snowpack dependency directory (https://snowpack.dev/)
-web_modules/
-
-# TypeScript cache
-*.tsbuildinfo
-
-# Optional npm cache directory
-.npm
-
-# Optional eslint cache
-.eslintcache
-
-# Optional stylelint cache
-.stylelintcache
-
-# Microbundle cache
-.rpt2_cache/
-.rts2_cache_cjs/
-.rts2_cache_es/
-.rts2_cache_umd/
-
-# Optional REPL history
-.node_repl_history
-
-# Output of 'npm pack'
-*.tgz
-
-# Yarn Integrity file
-.yarn-integrity
-
-# dotenv environment variable files
-.env
-.env.development.local
-.env.test.local
-.env.production.local
-.env.local
-
-# parcel-bundler cache (https://parceljs.org/)
-.cache
-.parcel-cache
-
-# Next.js build output
-.next
-out
-
-# Nuxt.js build / generate output
-.nuxt
-dist
-
-# Gatsby files
-.cache/
-# Comment in the public line in if your project uses Gatsby and not Next.js
-# https://nextjs.org/blog/next-9-1#public-directory-support
-# public
-
-# vuepress build output
-.vuepress/dist
-
-# vuepress v2.x temp and cache directory
-.temp
-.cache
-
-# Docusaurus cache and generated files
-.docusaurus
-
-# Serverless directories
-.serverless/
-
-# FuseBox cache
-.fusebox/
-
-# DynamoDB Local files
-.dynamodb/
-
-# TernJS port file
-.tern-port
-
-# Stores VSCode versions used for testing VSCode extensions
-.vscode-test
-
-# yarn v2
-.yarn/cache
-.yarn/unplugged
-.yarn/build-state.yml
-.yarn/install-state.gz
-.pnp.*
-
-# cypress artifacts
-cypress/videos
-cypress/screenshots
-
-
-
+# Ignore files for PNPM, NPM and YARN
+pnpm-lock.yaml
+package-lock.json
+yarn.lock
+
+kubernetes/
+
+# Copy of .gitignore
+.DS_Store
+node_modules
+/build
+/.svelte-kit
+/package
+.env
+.env.*
+!.env.example
+vite.config.js.timestamp-*
+vite.config.ts.timestamp-*
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+.pnpm-debug.log*
+
+# Diagnostic reports (https://nodejs.org/api/report.html)
+report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+*.lcov
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# Bower dependency directory (https://bower.io/)
+bower_components
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (https://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules/
+jspm_packages/
+
+# Snowpack dependency directory (https://snowpack.dev/)
+web_modules/
+
+# TypeScript cache
+*.tsbuildinfo
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Optional stylelint cache
+.stylelintcache
+
+# Microbundle cache
+.rpt2_cache/
+.rts2_cache_cjs/
+.rts2_cache_es/
+.rts2_cache_umd/
+
+# Optional REPL history
+.node_repl_history
+
+# Output of 'npm pack'
+*.tgz
+
+# Yarn Integrity file
+.yarn-integrity
+
+# dotenv environment variable files
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# parcel-bundler cache (https://parceljs.org/)
+.cache
+.parcel-cache
+
+# Next.js build output
+.next
+out
+
+# Nuxt.js build / generate output
+.nuxt
+dist
+
+# Gatsby files
+.cache/
+# Comment in the public line in if your project uses Gatsby and not Next.js
+# https://nextjs.org/blog/next-9-1#public-directory-support
+# public
+
+# vuepress build output
+.vuepress/dist
+
+# vuepress v2.x temp and cache directory
+.temp
+.cache
+
+# Docusaurus cache and generated files
+.docusaurus
+
+# Serverless directories
+.serverless/
+
+# FuseBox cache
+.fusebox/
+
+# DynamoDB Local files
+.dynamodb/
+
+# TernJS port file
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+.vscode-test
+
+# yarn v2
+.yarn/cache
+.yarn/unplugged
+.yarn/build-state.yml
+.yarn/install-state.gz
+.pnp.*
+
+# cypress artifacts
+cypress/videos
+cypress/screenshots
+
+
+
 /static/*

.prettierrc

@@ -1,9 +1,9 @@
-{
-	"useTabs": true,
-	"singleQuote": true,
-	"trailingComma": "none",
-	"printWidth": 100,
-	"plugins": ["prettier-plugin-svelte"],
-	"pluginSearchDirs": ["."],
-	"overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }]
-}
+{
+	"useTabs": true,
+	"singleQuote": true,
+	"trailingComma": "none",
+	"printWidth": 100,
+	"plugins": ["prettier-plugin-svelte"],
+	"pluginSearchDirs": ["."],
+	"overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }]
+}

CODE_OF_CONDUCT.md

@@ -1,77 +1,77 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contribute to a positive environment for our community include:
-
-- Demonstrating empathy and kindness toward other people
-- Being respectful of differing opinions, viewpoints, and experiences
-- Giving and gracefully accepting constructive feedback
-- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
-- Focusing on what is best not just for us as individuals, but for the overall community
-
-Examples of unacceptable behavior include:
-
-- The use of sexualized language or imagery, and sexual attention or advances of any kind
-- Trolling, insulting or derogatory comments, and personal or political attacks
-- Public or private harassment
-- Publishing others' private information, such as a physical or email address, without their explicit permission
-- **Spamming of any kind**
-- Aggressive sales tactics targeting our community members are strictly prohibited. You can mention your product if it's relevant to the discussion, but under no circumstances should you push it forcefully
-- Other conduct which could reasonably be considered inappropriate in a professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all community spaces and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, spamming, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at hello@openwebui.com. All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Temporary Ban
-
-**Community Impact**: Any violation of community standards, including but not limited to inappropriate language, unprofessional behavior, harassment, or spamming.
-
-**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
-
-### 2. Permanent Ban
-
-**Community Impact**: Repeated or severe violations of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contribute to a positive environment for our community include:
+
+- Demonstrating empathy and kindness toward other people
+- Being respectful of differing opinions, viewpoints, and experiences
+- Giving and gracefully accepting constructive feedback
+- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
+- Focusing on what is best not just for us as individuals, but for the overall community
+
+Examples of unacceptable behavior include:
+
+- The use of sexualized language or imagery, and sexual attention or advances of any kind
+- Trolling, insulting or derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or email address, without their explicit permission
+- **Spamming of any kind**
+- Aggressive sales tactics targeting our community members are strictly prohibited. You can mention your product if it's relevant to the discussion, but under no circumstances should you push it forcefully
+- Other conduct which could reasonably be considered inappropriate in a professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all community spaces and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, spamming, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at hello@openwebui.com. All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Temporary Ban
+
+**Community Impact**: Any violation of community standards, including but not limited to inappropriate language, unprofessional behavior, harassment, or spamming.
+
+**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
+
+### 2. Permanent Ban
+
+**Community Impact**: Repeated or severe violations of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

Caddyfile.localhost

@@ -1,64 +1,64 @@
-# Run with
-#    caddy run --envfile ./example.env --config ./Caddyfile.localhost
-#
-# This is configured for
-#    - Automatic HTTPS (even for localhost)
-#    - Reverse Proxying to Ollama API Base URL (http://localhost:11434/api)
-#    - CORS
-#    - HTTP Basic Auth API Tokens (uncomment basicauth section)
-
-
-# CORS Preflight (OPTIONS) + Request (GET, POST, PATCH, PUT, DELETE)
-(cors-api) {
-	@match-cors-api-preflight method OPTIONS
-	handle @match-cors-api-preflight {
-		header {
-			Access-Control-Allow-Origin "{http.request.header.origin}"
-			Access-Control-Allow-Methods "GET, POST, PUT, PATCH, DELETE, OPTIONS"
-			Access-Control-Allow-Headers "Origin, Accept, Authorization, Content-Type, X-Requested-With"
-			Access-Control-Allow-Credentials "true"
-			Access-Control-Max-Age "3600"
-			defer
-		}
-		respond "" 204
-	}
-
-	@match-cors-api-request {
-		not {
-			header Origin "{http.request.scheme}://{http.request.host}"
-		}
-		header Origin "{http.request.header.origin}"
-	}
-	handle @match-cors-api-request {
-		header {
-			Access-Control-Allow-Origin "{http.request.header.origin}"
-			Access-Control-Allow-Methods "GET, POST, PUT, PATCH, DELETE, OPTIONS"
-			Access-Control-Allow-Headers "Origin, Accept, Authorization, Content-Type, X-Requested-With"
-			Access-Control-Allow-Credentials "true"
-			Access-Control-Max-Age "3600"
-			defer
-		}
-	}
-}
-
-# replace localhost with example.com or whatever
-localhost {
-	## HTTP Basic Auth
-	## (uncomment to enable)
-	# basicauth {
-	# 	# see .example.env for how to generate tokens
-	# 	{env.OLLAMA_API_ID} {env.OLLAMA_API_TOKEN_DIGEST}
-	# }
-
-	handle /api/* {
-		# Comment to disable CORS
-		import cors-api
-
-		reverse_proxy localhost:11434
-	}
-
-	# Same-Origin Static Web Server
-	file_server {
-		root ./build/
-	}
-}
+# Run with
+#    caddy run --envfile ./example.env --config ./Caddyfile.localhost
+#
+# This is configured for
+#    - Automatic HTTPS (even for localhost)
+#    - Reverse Proxying to Ollama API Base URL (http://localhost:11434/api)
+#    - CORS
+#    - HTTP Basic Auth API Tokens (uncomment basicauth section)
+
+
+# CORS Preflight (OPTIONS) + Request (GET, POST, PATCH, PUT, DELETE)
+(cors-api) {
+	@match-cors-api-preflight method OPTIONS
+	handle @match-cors-api-preflight {
+		header {
+			Access-Control-Allow-Origin "{http.request.header.origin}"
+			Access-Control-Allow-Methods "GET, POST, PUT, PATCH, DELETE, OPTIONS"
+			Access-Control-Allow-Headers "Origin, Accept, Authorization, Content-Type, X-Requested-With"
+			Access-Control-Allow-Credentials "true"
+			Access-Control-Max-Age "3600"
+			defer
+		}
+		respond "" 204
+	}
+
+	@match-cors-api-request {
+		not {
+			header Origin "{http.request.scheme}://{http.request.host}"
+		}
+		header Origin "{http.request.header.origin}"
+	}
+	handle @match-cors-api-request {
+		header {
+			Access-Control-Allow-Origin "{http.request.header.origin}"
+			Access-Control-Allow-Methods "GET, POST, PUT, PATCH, DELETE, OPTIONS"
+			Access-Control-Allow-Headers "Origin, Accept, Authorization, Content-Type, X-Requested-With"
+			Access-Control-Allow-Credentials "true"
+			Access-Control-Max-Age "3600"
+			defer
+		}
+	}
+}
+
+# replace localhost with example.com or whatever
+localhost {
+	## HTTP Basic Auth
+	## (uncomment to enable)
+	# basicauth {
+	# 	# see .example.env for how to generate tokens
+	# 	{env.OLLAMA_API_ID} {env.OLLAMA_API_TOKEN_DIGEST}
+	# }
+
+	handle /api/* {
+		# Comment to disable CORS
+		import cors-api
+
+		reverse_proxy localhost:11434
+	}
+
+	# Same-Origin Static Web Server
+	file_server {
+		root ./build/
+	}
+}

Dockerfile

@@ -1,176 +1,176 @@
-# syntax=docker/dockerfile:1
-# Initialize device type args
-# use build args in the docker build command with --build-arg="BUILDARG=true"
-ARG USE_CUDA=false
-ARG USE_OLLAMA=false
-# Tested with cu117 for CUDA 11 and cu121 for CUDA 12 (default)
-ARG USE_CUDA_VER=cu121
-# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
-# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard 
-# for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
-# IMPORTANT: If you change the embedding model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
-ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
-ARG USE_RERANKING_MODEL=""
-
-# Tiktoken encoding name; models to use can be found at https://huggingface.co/models?library=tiktoken
-ARG USE_TIKTOKEN_ENCODING_NAME="cl100k_base"
-
-ARG BUILD_HASH=dev-build
-# Override at your own risk - non-root configurations are untested
-ARG UID=0
-ARG GID=0
-
-######## WebUI frontend ########
-FROM --platform=$BUILDPLATFORM node:22-alpine3.20 AS build
-ARG BUILD_HASH
-
-WORKDIR /app
-
-COPY package.json package-lock.json ./
-RUN npm ci
-
-COPY . .
-ENV APP_BUILD_HASH=${BUILD_HASH}
-RUN npm run build
-
-######## WebUI backend ########
-FROM python:3.11-slim-bookworm AS base
-
-# Use args
-ARG USE_CUDA
-ARG USE_OLLAMA
-ARG USE_CUDA_VER
-ARG USE_EMBEDDING_MODEL
-ARG USE_RERANKING_MODEL
-ARG UID
-ARG GID
-
-## Basis ##
-ENV ENV=prod \
-    PORT=8080 \
-    # pass build args to the build
-    USE_OLLAMA_DOCKER=${USE_OLLAMA} \
-    USE_CUDA_DOCKER=${USE_CUDA} \
-    USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
-    USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \
-    USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL}
-
-## Basis URL Config ##
-ENV OLLAMA_BASE_URL="/ollama" \
-    OPENAI_API_BASE_URL=""
-
-## API Key and Security Config ##
-ENV OPENAI_API_KEY="" \
-    WEBUI_SECRET_KEY="" \
-    SCARF_NO_ANALYTICS=true \
-    DO_NOT_TRACK=true \
-    ANONYMIZED_TELEMETRY=false
-
-#### Other models #########################################################
-## whisper TTS model settings ##
-ENV WHISPER_MODEL="base" \
-    WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
-
-## RAG Embedding model settings ##
-ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
-    RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
-    SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
-
-## Tiktoken model settings ##
-ENV TIKTOKEN_ENCODING_NAME="$USE_TIKTOKEN_ENCODING_NAME" \
-    TIKTOKEN_CACHE_DIR="/app/backend/data/cache/tiktoken"
-
-## Hugging Face download cache ##
-ENV HF_HOME="/app/backend/data/cache/embedding/models"
-
-## Torch Extensions ##
-# ENV TORCH_EXTENSIONS_DIR="/.cache/torch_extensions"
-
-#### Other models ##########################################################
-
-WORKDIR /app/backend
-
-ENV HOME=/root
-# Create user and group if not root
-RUN if [ $UID -ne 0 ]; then \
-    if [ $GID -ne 0 ]; then \
-    addgroup --gid $GID app; \
-    fi; \
-    adduser --uid $UID --gid $GID --home $HOME --disabled-password --no-create-home app; \
-    fi
-
-RUN mkdir -p $HOME/.cache/chroma
-RUN echo -n 00000000-0000-0000-0000-000000000000 > $HOME/.cache/chroma/telemetry_user_id
-
-# Make sure the user has access to the app and root directory
-RUN chown -R $UID:$GID /app $HOME
-
-RUN if [ "$USE_OLLAMA" = "true" ]; then \
-    apt-get update && \
-    # Install pandoc and netcat
-    apt-get install -y --no-install-recommends git build-essential pandoc netcat-openbsd curl && \
-    apt-get install -y --no-install-recommends gcc python3-dev && \
-    # for RAG OCR
-    apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
-    # install helper tools
-    apt-get install -y --no-install-recommends curl jq && \
-    # install ollama
-    curl -fsSL https://ollama.com/install.sh | sh && \
-    # cleanup
-    rm -rf /var/lib/apt/lists/*; \
-    else \
-    apt-get update && \
-    # Install pandoc, netcat and gcc
-    apt-get install -y --no-install-recommends git build-essential pandoc gcc netcat-openbsd curl jq && \
-    apt-get install -y --no-install-recommends gcc python3-dev && \
-    # for RAG OCR
-    apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
-    # cleanup
-    rm -rf /var/lib/apt/lists/*; \
-    fi
-
-# install python dependencies
-COPY --chown=$UID:$GID ./backend/requirements.txt ./requirements.txt
-
-RUN pip3 install uv && \
-    if [ "$USE_CUDA" = "true" ]; then \
-    # If you use CUDA the whisper and embedding model will be downloaded on first use
-    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
-    uv pip install --system -r requirements.txt --no-cache-dir && \
-    python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
-    python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
-    python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
-    else \
-    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
-    uv pip install --system -r requirements.txt --no-cache-dir && \
-    python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
-    python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
-    python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
-    fi; \
-    chown -R $UID:$GID /app/backend/data/
-
-
-
-# copy embedding weight from build
-# RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
-# COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
-
-# copy built frontend files
-COPY --chown=$UID:$GID --from=build /app/build /app/build
-COPY --chown=$UID:$GID --from=build /app/CHANGELOG.md /app/CHANGELOG.md
-COPY --chown=$UID:$GID --from=build /app/package.json /app/package.json
-
-# copy backend files
-COPY --chown=$UID:$GID ./backend .
-
-EXPOSE 8080
-
-HEALTHCHECK CMD curl --silent --fail http://localhost:${PORT:-8080}/health | jq -ne 'input.status == true' || exit 1
-
-USER $UID:$GID
-
-ARG BUILD_HASH
-ENV WEBUI_BUILD_VERSION=${BUILD_HASH}
-ENV DOCKER=true
-
-CMD [ "bash", "start.sh"]
+# syntax=docker/dockerfile:1
+# Initialize device type args
+# use build args in the docker build command with --build-arg="BUILDARG=true"
+ARG USE_CUDA=false
+ARG USE_OLLAMA=false
+# Tested with cu117 for CUDA 11 and cu121 for CUDA 12 (default)
+ARG USE_CUDA_VER=cu121
+# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
+# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard 
+# for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
+# IMPORTANT: If you change the embedding model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
+ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
+ARG USE_RERANKING_MODEL=""
+
+# Tiktoken encoding name; models to use can be found at https://huggingface.co/models?library=tiktoken
+ARG USE_TIKTOKEN_ENCODING_NAME="cl100k_base"
+
+ARG BUILD_HASH=dev-build
+# Override at your own risk - non-root configurations are untested
+ARG UID=0
+ARG GID=0
+
+######## WebUI frontend ########
+FROM --platform=$BUILDPLATFORM node:22-alpine3.20 AS build
+ARG BUILD_HASH
+
+WORKDIR /app
+
+COPY package.json package-lock.json ./
+RUN npm ci
+
+COPY . .
+ENV APP_BUILD_HASH=${BUILD_HASH}
+RUN npm run build
+
+######## WebUI backend ########
+FROM python:3.11-slim-bookworm AS base
+
+# Use args
+ARG USE_CUDA
+ARG USE_OLLAMA
+ARG USE_CUDA_VER
+ARG USE_EMBEDDING_MODEL
+ARG USE_RERANKING_MODEL
+ARG UID
+ARG GID
+
+## Basis ##
+ENV ENV=prod \
+    PORT=8080 \
+    # pass build args to the build
+    USE_OLLAMA_DOCKER=${USE_OLLAMA} \
+    USE_CUDA_DOCKER=${USE_CUDA} \
+    USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
+    USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \
+    USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL}
+
+## Basis URL Config ##
+ENV OLLAMA_BASE_URL="/ollama" \
+    OPENAI_API_BASE_URL=""
+
+## API Key and Security Config ##
+ENV OPENAI_API_KEY="" \
+    WEBUI_SECRET_KEY="" \
+    SCARF_NO_ANALYTICS=true \
+    DO_NOT_TRACK=true \
+    ANONYMIZED_TELEMETRY=false
+
+#### Other models #########################################################
+## whisper TTS model settings ##
+ENV WHISPER_MODEL="base" \
+    WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
+
+## RAG Embedding model settings ##
+ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
+    RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
+    SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
+
+## Tiktoken model settings ##
+ENV TIKTOKEN_ENCODING_NAME="$USE_TIKTOKEN_ENCODING_NAME" \
+    TIKTOKEN_CACHE_DIR="/app/backend/data/cache/tiktoken"
+
+## Hugging Face download cache ##
+ENV HF_HOME="/app/backend/data/cache/embedding/models"
+
+## Torch Extensions ##
+# ENV TORCH_EXTENSIONS_DIR="/.cache/torch_extensions"
+
+#### Other models ##########################################################
+
+WORKDIR /app/backend
+
+ENV HOME=/root
+# Create user and group if not root
+RUN if [ $UID -ne 0 ]; then \
+    if [ $GID -ne 0 ]; then \
+    addgroup --gid $GID app; \
+    fi; \
+    adduser --uid $UID --gid $GID --home $HOME --disabled-password --no-create-home app; \
+    fi
+
+RUN mkdir -p $HOME/.cache/chroma
+RUN echo -n 00000000-0000-0000-0000-000000000000 > $HOME/.cache/chroma/telemetry_user_id
+
+# Make sure the user has access to the app and root directory
+RUN chown -R $UID:$GID /app $HOME
+
+RUN if [ "$USE_OLLAMA" = "true" ]; then \
+    apt-get update && \
+    # Install pandoc and netcat
+    apt-get install -y --no-install-recommends git build-essential pandoc netcat-openbsd curl && \
+    apt-get install -y --no-install-recommends gcc python3-dev && \
+    # for RAG OCR
+    apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
+    # install helper tools
+    apt-get install -y --no-install-recommends curl jq && \
+    # install ollama
+    curl -fsSL https://ollama.com/install.sh | sh && \
+    # cleanup
+    rm -rf /var/lib/apt/lists/*; \
+    else \
+    apt-get update && \
+    # Install pandoc, netcat and gcc
+    apt-get install -y --no-install-recommends git build-essential pandoc gcc netcat-openbsd curl jq && \
+    apt-get install -y --no-install-recommends gcc python3-dev && \
+    # for RAG OCR
+    apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
+    # cleanup
+    rm -rf /var/lib/apt/lists/*; \
+    fi
+
+# install python dependencies
+COPY --chown=$UID:$GID ./backend/requirements.txt ./requirements.txt
+
+RUN pip3 install uv && \
+    if [ "$USE_CUDA" = "true" ]; then \
+    # If you use CUDA the whisper and embedding model will be downloaded on first use
+    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
+    uv pip install --system -r requirements.txt --no-cache-dir && \
+    python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
+    python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
+    python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
+    else \
+    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
+    uv pip install --system -r requirements.txt --no-cache-dir && \
+    python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
+    python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
+    python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
+    fi; \
+    chown -R $UID:$GID /app/backend/data/
+
+
+
+# copy embedding weight from build
+# RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
+# COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
+
+# copy built frontend files
+COPY --chown=$UID:$GID --from=build /app/build /app/build
+COPY --chown=$UID:$GID --from=build /app/CHANGELOG.md /app/CHANGELOG.md
+COPY --chown=$UID:$GID --from=build /app/package.json /app/package.json
+
+# copy backend files
+COPY --chown=$UID:$GID ./backend .
+
+EXPOSE 8080
+
+HEALTHCHECK CMD curl --silent --fail http://localhost:${PORT:-8080}/health | jq -ne 'input.status == true' || exit 1
+
+USER $UID:$GID
+
+ARG BUILD_HASH
+ENV WEBUI_BUILD_VERSION=${BUILD_HASH}
+ENV DOCKER=true
+
+CMD [ "bash", "start.sh"]

INSTALLATION.md

@@ -1,35 +1,35 @@
-### Installing Both Ollama and Open WebUI Using Kustomize
-
-For cpu-only pod
-
-```bash
-kubectl apply -f ./kubernetes/manifest/base
-```
-
-For gpu-enabled pod
-
-```bash
-kubectl apply -k ./kubernetes/manifest
-```
-
-### Installing Both Ollama and Open WebUI Using Helm
-
-Package Helm file first
-
-```bash
-helm package ./kubernetes/helm/
-```
-
-For cpu-only pod
-
-```bash
-helm install ollama-webui ./ollama-webui-*.tgz
-```
-
-For gpu-enabled pod
-
-```bash
-helm install ollama-webui ./ollama-webui-*.tgz --set ollama.resources.limits.nvidia.com/gpu="1"
-```
-
-Check the `kubernetes/helm/values.yaml` file to know which parameters are available for customization
+### Installing Both Ollama and Open WebUI Using Kustomize
+
+For cpu-only pod
+
+```bash
+kubectl apply -f ./kubernetes/manifest/base
+```
+
+For gpu-enabled pod
+
+```bash
+kubectl apply -k ./kubernetes/manifest
+```
+
+### Installing Both Ollama and Open WebUI Using Helm
+
+Package Helm file first
+
+```bash
+helm package ./kubernetes/helm/
+```
+
+For cpu-only pod
+
+```bash
+helm install ollama-webui ./ollama-webui-*.tgz
+```
+
+For gpu-enabled pod
+
+```bash
+helm install ollama-webui ./ollama-webui-*.tgz --set ollama.resources.limits.nvidia.com/gpu="1"
+```
+
+Check the `kubernetes/helm/values.yaml` file to know which parameters are available for customization

LICENSE

@@ -1,21 +1,21 @@
-MIT License
-
-Copyright (c) 2023 Timothy Jaeryang Baek
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+MIT License
+
+Copyright (c) 2023 Timothy Jaeryang Baek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

@@ -1,33 +1,33 @@
-
-ifneq ($(shell which docker-compose 2>/dev/null),)
-    DOCKER_COMPOSE := docker-compose
-else
-    DOCKER_COMPOSE := docker compose
-endif
-
-install:
-	$(DOCKER_COMPOSE) up -d
-
-remove:
-	@chmod +x confirm_remove.sh
-	@./confirm_remove.sh
-
-start:
-	$(DOCKER_COMPOSE) start
-startAndBuild: 
-	$(DOCKER_COMPOSE) up -d --build
-
-stop:
-	$(DOCKER_COMPOSE) stop
-
-update:
-	# Calls the LLM update script
-	chmod +x update_ollama_models.sh
-	@./update_ollama_models.sh
-	@git pull
-	$(DOCKER_COMPOSE) down
-	# Make sure the ollama-webui container is stopped before rebuilding
-	@docker stop open-webui || true
-	$(DOCKER_COMPOSE) up --build -d
-	$(DOCKER_COMPOSE) start
-
+
+ifneq ($(shell which docker-compose 2>/dev/null),)
+    DOCKER_COMPOSE := docker-compose
+else
+    DOCKER_COMPOSE := docker compose
+endif
+
+install:
+	$(DOCKER_COMPOSE) up -d
+
+remove:
+	@chmod +x confirm_remove.sh
+	@./confirm_remove.sh
+
+start:
+	$(DOCKER_COMPOSE) start
+startAndBuild: 
+	$(DOCKER_COMPOSE) up -d --build
+
+stop:
+	$(DOCKER_COMPOSE) stop
+
+update:
+	# Calls the LLM update script
+	chmod +x update_ollama_models.sh
+	@./update_ollama_models.sh
+	@git pull
+	$(DOCKER_COMPOSE) down
+	# Make sure the ollama-webui container is stopped before rebuilding
+	@docker stop open-webui || true
+	$(DOCKER_COMPOSE) up --build -d
+	$(DOCKER_COMPOSE) start
+

README.md

@@ -1,231 +1,231 @@
----
-title: Open WebUI
-emoji: 🐳
-colorFrom: purple
-colorTo: gray
-sdk: docker
-app_port: 8080
----
-# Open WebUI 👋
-
-![GitHub stars](https://img.shields.io/github/stars/open-webui/open-webui?style=social)
-![GitHub forks](https://img.shields.io/github/forks/open-webui/open-webui?style=social)
-![GitHub watchers](https://img.shields.io/github/watchers/open-webui/open-webui?style=social)
-![GitHub repo size](https://img.shields.io/github/repo-size/open-webui/open-webui)
-![GitHub language count](https://img.shields.io/github/languages/count/open-webui/open-webui)
-![GitHub top language](https://img.shields.io/github/languages/top/open-webui/open-webui)
-![GitHub last commit](https://img.shields.io/github/last-commit/open-webui/open-webui?color=red)
-![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Follama-webui%2Follama-wbui&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)
-[![Discord](https://img.shields.io/badge/Discord-Open_WebUI-blue?logo=discord&logoColor=white)](https://discord.gg/5rJgQTnV4s)
-[![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/tjbck)
-
-Open WebUI is an [extensible](https://github.com/open-webui/pipelines), feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. For more information, be sure to check out our [Open WebUI Documentation](https://docs.openwebui.com/).
-
-![Open WebUI Demo](./demo.gif)
-
-## Key Features of Open WebUI ⭐
-
-- 🚀 **Effortless Setup**: Install seamlessly using Docker or Kubernetes (kubectl, kustomize or helm) for a hassle-free experience with support for both `:ollama` and `:cuda` tagged images.
-
-- 🤝 **Ollama/OpenAI API Integration**: Effortlessly integrate OpenAI-compatible APIs for versatile conversations alongside Ollama models. Customize the OpenAI API URL to link with **LMStudio, GroqCloud, Mistral, OpenRouter, and more**.
-
-- 🧩 **Pipelines, Open WebUI Plugin Support**: Seamlessly integrate custom logic and Python libraries into Open WebUI using [Pipelines Plugin Framework](https://github.com/open-webui/pipelines). Launch your Pipelines instance, set the OpenAI URL to the Pipelines URL, and explore endless possibilities. [Examples](https://github.com/open-webui/pipelines/tree/main/examples) include **Function Calling**, User **Rate Limiting** to control access, **Usage Monitoring** with tools like Langfuse, **Live Translation with LibreTranslate** for multilingual support, **Toxic Message Filtering** and much more.
-
-- 📱 **Responsive Design**: Enjoy a seamless experience across Desktop PC, Laptop, and Mobile devices.
-
-- 📱 **Progressive Web App (PWA) for Mobile**: Enjoy a native app-like experience on your mobile device with our PWA, providing offline access on localhost and a seamless user interface.
-
-- ✒️🔢 **Full Markdown and LaTeX Support**: Elevate your LLM experience with comprehensive Markdown and LaTeX capabilities for enriched interaction.
-
-- 🎤📹 **Hands-Free Voice/Video Call**: Experience seamless communication with integrated hands-free voice and video call features, allowing for a more dynamic and interactive chat environment.
-
-- 🛠️ **Model Builder**: Easily create Ollama models via the Web UI. Create and add custom characters/agents, customize chat elements, and import models effortlessly through [Open WebUI Community](https://openwebui.com/) integration.
-
-- 🐍 **Native Python Function Calling Tool**: Enhance your LLMs with built-in code editor support in the tools workspace. Bring Your Own Function (BYOF) by simply adding your pure Python functions, enabling seamless integration with LLMs.
-
-- 📚 **Local RAG Integration**: Dive into the future of chat interactions with groundbreaking Retrieval Augmented Generation (RAG) support. This feature seamlessly integrates document interactions into your chat experience. You can load documents directly into the chat or add files to your document library, effortlessly accessing them using the `#` command before a query.
-
-- 🔍 **Web Search for RAG**: Perform web searches using providers like `SearXNG`, `Google PSE`, `Brave Search`, `serpstack`, `serper`, `Serply`, `DuckDuckGo`, `TavilySearch` and `SearchApi` and inject the results directly into your chat experience.
-
-- 🌐 **Web Browsing Capability**: Seamlessly integrate websites into your chat experience using the `#` command followed by a URL. This feature allows you to incorporate web content directly into your conversations, enhancing the richness and depth of your interactions.
-
-- 🎨 **Image Generation Integration**: Seamlessly incorporate image generation capabilities using options such as AUTOMATIC1111 API or ComfyUI (local), and OpenAI's DALL-E (external), enriching your chat experience with dynamic visual content.
-
-- ⚙️ **Many Models Conversations**: Effortlessly engage with various models simultaneously, harnessing their unique strengths for optimal responses. Enhance your experience by leveraging a diverse set of models in parallel.
-
-- 🔐 **Role-Based Access Control (RBAC)**: Ensure secure access with restricted permissions; only authorized individuals can access your Ollama, and exclusive model creation/pulling rights are reserved for administrators.
-
-- 🌐🌍 **Multilingual Support**: Experience Open WebUI in your preferred language with our internationalization (i18n) support. Join us in expanding our supported languages! We're actively seeking contributors!
-
-- 🌟 **Continuous Updates**: We are committed to improving Open WebUI with regular updates, fixes, and new features.
-
-Want to learn more about Open WebUI's features? Check out our [Open WebUI documentation](https://docs.openwebui.com/features) for a comprehensive overview!
-
-## 🔗 Also Check Out Open WebUI Community!
-
-Don't forget to explore our sibling project, [Open WebUI Community](https://openwebui.com/), where you can discover, download, and explore customized Modelfiles. Open WebUI Community offers a wide range of exciting possibilities for enhancing your chat interactions with Open WebUI! 🚀
-
-## How to Install 🚀
-
-### Installation via Python pip 🐍
-
-Open WebUI can be installed using pip, the Python package installer. Before proceeding, ensure you're using **Python 3.11** to avoid compatibility issues.
-
-1. **Install Open WebUI**:
-   Open your terminal and run the following command to install Open WebUI:
-
-   ```bash
-   pip install open-webui
-   ```
-
-2. **Running Open WebUI**:
-   After installation, you can start Open WebUI by executing:
-
-   ```bash
-   open-webui serve
-   ```
-
-This will start the Open WebUI server, which you can access at [http://localhost:8080](http://localhost:8080)
-
-### Quick Start with Docker 🐳
-
-> [!NOTE]  
-> Please note that for certain Docker environments, additional configurations might be needed. If you encounter any connection issues, our detailed guide on [Open WebUI Documentation](https://docs.openwebui.com/) is ready to assist you.
-
-> [!WARNING]
-> When using Docker to install Open WebUI, make sure to include the `-v open-webui:/app/backend/data` in your Docker command. This step is crucial as it ensures your database is properly mounted and prevents any loss of data.
-
-> [!TIP]  
-> If you wish to utilize Open WebUI with Ollama included or CUDA acceleration, we recommend utilizing our official images tagged with either `:cuda` or `:ollama`. To enable CUDA, you must install the [Nvidia CUDA container toolkit](https://docs.nvidia.com/dgx/nvidia-container-runtime-upgrade/) on your Linux/WSL system.
-
-### Installation with Default Configuration
-
-- **If Ollama is on your computer**, use this command:
-
-  ```bash
-  docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
-  ```
-
-- **If Ollama is on a Different Server**, use this command:
-
-  To connect to Ollama on another server, change the `OLLAMA_BASE_URL` to the server's URL:
-
-  ```bash
-  docker run -d -p 3000:8080 -e OLLAMA_BASE_URL=https://example.com -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
-  ```
-
-- **To run Open WebUI with Nvidia GPU support**, use this command:
-
-  ```bash
-  docker run -d -p 3000:8080 --gpus all --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:cuda
-  ```
-
-### Installation for OpenAI API Usage Only
-
-- **If you're only using OpenAI API**, use this command:
-
-  ```bash
-  docker run -d -p 3000:8080 -e OPENAI_API_KEY=your_secret_key -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
-  ```
-
-### Installing Open WebUI with Bundled Ollama Support
-
-This installation method uses a single container image that bundles Open WebUI with Ollama, allowing for a streamlined setup via a single command. Choose the appropriate command based on your hardware setup:
-
-- **With GPU Support**:
-  Utilize GPU resources by running the following command:
-
-  ```bash
-  docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
-  ```
-
-- **For CPU Only**:
-  If you're not using a GPU, use this command instead:
-
-  ```bash
-  docker run -d -p 3000:8080 -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
-  ```
-
-Both commands facilitate a built-in, hassle-free installation of both Open WebUI and Ollama, ensuring that you can get everything up and running swiftly.
-
-After installation, you can access Open WebUI at [http://localhost:3000](http://localhost:3000). Enjoy! 😄
-
-### Other Installation Methods
-
-We offer various installation alternatives, including non-Docker native installation methods, Docker Compose, Kustomize, and Helm. Visit our [Open WebUI Documentation](https://docs.openwebui.com/getting-started/) or join our [Discord community](https://discord.gg/5rJgQTnV4s) for comprehensive guidance.
-
-### Troubleshooting
-
-Encountering connection issues? Our [Open WebUI Documentation](https://docs.openwebui.com/troubleshooting/) has got you covered. For further assistance and to join our vibrant community, visit the [Open WebUI Discord](https://discord.gg/5rJgQTnV4s).
-
-#### Open WebUI: Server Connection Error
-
-If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the `--network=host` flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: `http://localhost:8080`.
-
-**Example Docker Command**:
-
-```bash
-docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main
-```
-
-### Keeping Your Docker Installation Up-to-Date
-
-In case you want to update your local Docker installation to the latest version, you can do it with [Watchtower](https://containrrr.dev/watchtower/):
-
-```bash
-docker run --rm --volume /var/run/docker.sock:/var/run/docker.sock containrrr/watchtower --run-once open-webui
-```
-
-In the last part of the command, replace `open-webui` with your container name if it is different.
-
-Check our Migration Guide available in our [Open WebUI Documentation](https://docs.openwebui.com/tutorials/migration/).
-
-### Using the Dev Branch 🌙
-
-> [!WARNING]
-> The `:dev` branch contains the latest unstable features and changes. Use it at your own risk as it may have bugs or incomplete features.
-
-If you want to try out the latest bleeding-edge features and are okay with occasional instability, you can use the `:dev` tag like this:
-
-```bash
-docker run -d -p 3000:8080 -v open-webui:/app/backend/data --name open-webui --add-host=host.docker.internal:host-gateway --restart always ghcr.io/open-webui/open-webui:dev
-```
-
-## What's Next? 🌟
-
-Discover upcoming features on our roadmap in the [Open WebUI Documentation](https://docs.openwebui.com/roadmap/).
-
-## Supporters ✨
-
-A big shoutout to our amazing supporters who's helping to make this project possible! 🙏
-
-### Platinum Sponsors 🤍
-
-- We're looking for Sponsors!
-
-### Acknowledgments
-
-Special thanks to [Prof. Lawrence Kim](https://www.lhkim.com/) and [Prof. Nick Vincent](https://www.nickmvincent.com/) for their invaluable support and guidance in shaping this project into a research endeavor. Grateful for your mentorship throughout the journey! 🙌
-
-## License 📜
-
-This project is licensed under the [MIT License](LICENSE) - see the [LICENSE](LICENSE) file for details. 📄
-
-## Support 💬
-
-If you have any questions, suggestions, or need assistance, please open an issue or join our
-[Open WebUI Discord community](https://discord.gg/5rJgQTnV4s) to connect with us! 🤝
-
-## Star History
-
-<a href="https://star-history.com/#open-webui/open-webui&Date">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=open-webui/open-webui&type=Date&theme=dark" />
-    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=open-webui/open-webui&type=Date" />
-    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=open-webui/open-webui&type=Date" />
-  </picture>
-</a>
-
----
-
-Created by [Timothy Jaeryang Baek](https://github.com/tjbck) - Let's make Open WebUI even more amazing together! 💪
+---
+title: Open WebUI
+emoji: 🐳
+colorFrom: purple
+colorTo: gray
+sdk: docker
+app_port: 8080
+---
+# Open WebUI 👋
+
+![GitHub stars](https://img.shields.io/github/stars/open-webui/open-webui?style=social)
+![GitHub forks](https://img.shields.io/github/forks/open-webui/open-webui?style=social)
+![GitHub watchers](https://img.shields.io/github/watchers/open-webui/open-webui?style=social)
+![GitHub repo size](https://img.shields.io/github/repo-size/open-webui/open-webui)
+![GitHub language count](https://img.shields.io/github/languages/count/open-webui/open-webui)
+![GitHub top language](https://img.shields.io/github/languages/top/open-webui/open-webui)
+![GitHub last commit](https://img.shields.io/github/last-commit/open-webui/open-webui?color=red)
+![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Follama-webui%2Follama-wbui&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)
+[![Discord](https://img.shields.io/badge/Discord-Open_WebUI-blue?logo=discord&logoColor=white)](https://discord.gg/5rJgQTnV4s)
+[![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/tjbck)
+
+Open WebUI is an [extensible](https://github.com/open-webui/pipelines), feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. For more information, be sure to check out our [Open WebUI Documentation](https://docs.openwebui.com/).
+
+![Open WebUI Demo](./demo.gif)
+
+## Key Features of Open WebUI ⭐
+
+- 🚀 **Effortless Setup**: Install seamlessly using Docker or Kubernetes (kubectl, kustomize or helm) for a hassle-free experience with support for both `:ollama` and `:cuda` tagged images.
+
+- 🤝 **Ollama/OpenAI API Integration**: Effortlessly integrate OpenAI-compatible APIs for versatile conversations alongside Ollama models. Customize the OpenAI API URL to link with **LMStudio, GroqCloud, Mistral, OpenRouter, and more**.
+
+- 🧩 **Pipelines, Open WebUI Plugin Support**: Seamlessly integrate custom logic and Python libraries into Open WebUI using [Pipelines Plugin Framework](https://github.com/open-webui/pipelines). Launch your Pipelines instance, set the OpenAI URL to the Pipelines URL, and explore endless possibilities. [Examples](https://github.com/open-webui/pipelines/tree/main/examples) include **Function Calling**, User **Rate Limiting** to control access, **Usage Monitoring** with tools like Langfuse, **Live Translation with LibreTranslate** for multilingual support, **Toxic Message Filtering** and much more.
+
+- 📱 **Responsive Design**: Enjoy a seamless experience across Desktop PC, Laptop, and Mobile devices.
+
+- 📱 **Progressive Web App (PWA) for Mobile**: Enjoy a native app-like experience on your mobile device with our PWA, providing offline access on localhost and a seamless user interface.
+
+- ✒️🔢 **Full Markdown and LaTeX Support**: Elevate your LLM experience with comprehensive Markdown and LaTeX capabilities for enriched interaction.
+
+- 🎤📹 **Hands-Free Voice/Video Call**: Experience seamless communication with integrated hands-free voice and video call features, allowing for a more dynamic and interactive chat environment.
+
+- 🛠️ **Model Builder**: Easily create Ollama models via the Web UI. Create and add custom characters/agents, customize chat elements, and import models effortlessly through [Open WebUI Community](https://openwebui.com/) integration.
+
+- 🐍 **Native Python Function Calling Tool**: Enhance your LLMs with built-in code editor support in the tools workspace. Bring Your Own Function (BYOF) by simply adding your pure Python functions, enabling seamless integration with LLMs.
+
+- 📚 **Local RAG Integration**: Dive into the future of chat interactions with groundbreaking Retrieval Augmented Generation (RAG) support. This feature seamlessly integrates document interactions into your chat experience. You can load documents directly into the chat or add files to your document library, effortlessly accessing them using the `#` command before a query.
+
+- 🔍 **Web Search for RAG**: Perform web searches using providers like `SearXNG`, `Google PSE`, `Brave Search`, `serpstack`, `serper`, `Serply`, `DuckDuckGo`, `TavilySearch` and `SearchApi` and inject the results directly into your chat experience.
+
+- 🌐 **Web Browsing Capability**: Seamlessly integrate websites into your chat experience using the `#` command followed by a URL. This feature allows you to incorporate web content directly into your conversations, enhancing the richness and depth of your interactions.
+
+- 🎨 **Image Generation Integration**: Seamlessly incorporate image generation capabilities using options such as AUTOMATIC1111 API or ComfyUI (local), and OpenAI's DALL-E (external), enriching your chat experience with dynamic visual content.
+
+- ⚙️ **Many Models Conversations**: Effortlessly engage with various models simultaneously, harnessing their unique strengths for optimal responses. Enhance your experience by leveraging a diverse set of models in parallel.
+
+- 🔐 **Role-Based Access Control (RBAC)**: Ensure secure access with restricted permissions; only authorized individuals can access your Ollama, and exclusive model creation/pulling rights are reserved for administrators.
+
+- 🌐🌍 **Multilingual Support**: Experience Open WebUI in your preferred language with our internationalization (i18n) support. Join us in expanding our supported languages! We're actively seeking contributors!
+
+- 🌟 **Continuous Updates**: We are committed to improving Open WebUI with regular updates, fixes, and new features.
+
+Want to learn more about Open WebUI's features? Check out our [Open WebUI documentation](https://docs.openwebui.com/features) for a comprehensive overview!
+
+## 🔗 Also Check Out Open WebUI Community!
+
+Don't forget to explore our sibling project, [Open WebUI Community](https://openwebui.com/), where you can discover, download, and explore customized Modelfiles. Open WebUI Community offers a wide range of exciting possibilities for enhancing your chat interactions with Open WebUI! 🚀
+
+## How to Install 🚀
+
+### Installation via Python pip 🐍
+
+Open WebUI can be installed using pip, the Python package installer. Before proceeding, ensure you're using **Python 3.11** to avoid compatibility issues.
+
+1. **Install Open WebUI**:
+   Open your terminal and run the following command to install Open WebUI:
+
+   ```bash
+   pip install open-webui
+   ```
+
+2. **Running Open WebUI**:
+   After installation, you can start Open WebUI by executing:
+
+   ```bash
+   open-webui serve
+   ```
+
+This will start the Open WebUI server, which you can access at [http://localhost:8080](http://localhost:8080)
+
+### Quick Start with Docker 🐳
+
+> [!NOTE]  
+> Please note that for certain Docker environments, additional configurations might be needed. If you encounter any connection issues, our detailed guide on [Open WebUI Documentation](https://docs.openwebui.com/) is ready to assist you.
+
+> [!WARNING]
+> When using Docker to install Open WebUI, make sure to include the `-v open-webui:/app/backend/data` in your Docker command. This step is crucial as it ensures your database is properly mounted and prevents any loss of data.
+
+> [!TIP]  
+> If you wish to utilize Open WebUI with Ollama included or CUDA acceleration, we recommend utilizing our official images tagged with either `:cuda` or `:ollama`. To enable CUDA, you must install the [Nvidia CUDA container toolkit](https://docs.nvidia.com/dgx/nvidia-container-runtime-upgrade/) on your Linux/WSL system.
+
+### Installation with Default Configuration
+
+- **If Ollama is on your computer**, use this command:
+
+  ```bash
+  docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+  ```
+
+- **If Ollama is on a Different Server**, use this command:
+
+  To connect to Ollama on another server, change the `OLLAMA_BASE_URL` to the server's URL:
+
+  ```bash
+  docker run -d -p 3000:8080 -e OLLAMA_BASE_URL=https://example.com -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+  ```
+
+- **To run Open WebUI with Nvidia GPU support**, use this command:
+
+  ```bash
+  docker run -d -p 3000:8080 --gpus all --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:cuda
+  ```
+
+### Installation for OpenAI API Usage Only
+
+- **If you're only using OpenAI API**, use this command:
+
+  ```bash
+  docker run -d -p 3000:8080 -e OPENAI_API_KEY=your_secret_key -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+  ```
+
+### Installing Open WebUI with Bundled Ollama Support
+
+This installation method uses a single container image that bundles Open WebUI with Ollama, allowing for a streamlined setup via a single command. Choose the appropriate command based on your hardware setup:
+
+- **With GPU Support**:
+  Utilize GPU resources by running the following command:
+
+  ```bash
+  docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
+  ```
+
+- **For CPU Only**:
+  If you're not using a GPU, use this command instead:
+
+  ```bash
+  docker run -d -p 3000:8080 -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
+  ```
+
+Both commands facilitate a built-in, hassle-free installation of both Open WebUI and Ollama, ensuring that you can get everything up and running swiftly.
+
+After installation, you can access Open WebUI at [http://localhost:3000](http://localhost:3000). Enjoy! 😄
+
+### Other Installation Methods
+
+We offer various installation alternatives, including non-Docker native installation methods, Docker Compose, Kustomize, and Helm. Visit our [Open WebUI Documentation](https://docs.openwebui.com/getting-started/) or join our [Discord community](https://discord.gg/5rJgQTnV4s) for comprehensive guidance.
+
+### Troubleshooting
+
+Encountering connection issues? Our [Open WebUI Documentation](https://docs.openwebui.com/troubleshooting/) has got you covered. For further assistance and to join our vibrant community, visit the [Open WebUI Discord](https://discord.gg/5rJgQTnV4s).
+
+#### Open WebUI: Server Connection Error
+
+If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the `--network=host` flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: `http://localhost:8080`.
+
+**Example Docker Command**:
+
+```bash
+docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+```
+
+### Keeping Your Docker Installation Up-to-Date
+
+In case you want to update your local Docker installation to the latest version, you can do it with [Watchtower](https://containrrr.dev/watchtower/):
+
+```bash
+docker run --rm --volume /var/run/docker.sock:/var/run/docker.sock containrrr/watchtower --run-once open-webui
+```
+
+In the last part of the command, replace `open-webui` with your container name if it is different.
+
+Check our Migration Guide available in our [Open WebUI Documentation](https://docs.openwebui.com/tutorials/migration/).
+
+### Using the Dev Branch 🌙
+
+> [!WARNING]
+> The `:dev` branch contains the latest unstable features and changes. Use it at your own risk as it may have bugs or incomplete features.
+
+If you want to try out the latest bleeding-edge features and are okay with occasional instability, you can use the `:dev` tag like this:
+
+```bash
+docker run -d -p 3000:8080 -v open-webui:/app/backend/data --name open-webui --add-host=host.docker.internal:host-gateway --restart always ghcr.io/open-webui/open-webui:dev
+```
+
+## What's Next? 🌟
+
+Discover upcoming features on our roadmap in the [Open WebUI Documentation](https://docs.openwebui.com/roadmap/).
+
+## Supporters ✨
+
+A big shoutout to our amazing supporters who's helping to make this project possible! 🙏
+
+### Platinum Sponsors 🤍
+
+- We're looking for Sponsors!
+
+### Acknowledgments
+
+Special thanks to [Prof. Lawrence Kim](https://www.lhkim.com/) and [Prof. Nick Vincent](https://www.nickmvincent.com/) for their invaluable support and guidance in shaping this project into a research endeavor. Grateful for your mentorship throughout the journey! 🙌
+
+## License 📜
+
+This project is licensed under the [MIT License](LICENSE) - see the [LICENSE](LICENSE) file for details. 📄
+
+## Support 💬
+
+If you have any questions, suggestions, or need assistance, please open an issue or join our
+[Open WebUI Discord community](https://discord.gg/5rJgQTnV4s) to connect with us! 🤝
+
+## Star History
+
+<a href="https://star-history.com/#open-webui/open-webui&Date">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=open-webui/open-webui&type=Date&theme=dark" />
+    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=open-webui/open-webui&type=Date" />
+    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=open-webui/open-webui&type=Date" />
+  </picture>
+</a>
+
+---
+
+Created by [Timothy Jaeryang Baek](https://github.com/tjbck) - Let's make Open WebUI even more amazing together! 💪

TROUBLESHOOTING.md

@@ -1,36 +1,36 @@
-# Open WebUI Troubleshooting Guide
-
-## Understanding the Open WebUI Architecture
-
-The Open WebUI system is designed to streamline interactions between the client (your browser) and the Ollama API. At the heart of this design is a backend reverse proxy, enhancing security and resolving CORS issues.
-
-- **How it Works**: The Open WebUI is designed to interact with the Ollama API through a specific route. When a request is made from the WebUI to Ollama, it is not directly sent to the Ollama API. Initially, the request is sent to the Open WebUI backend via `/ollama` route. From there, the backend is responsible for forwarding the request to the Ollama API. This forwarding is accomplished by using the route specified in the `OLLAMA_BASE_URL` environment variable. Therefore, a request made to `/ollama` in the WebUI is effectively the same as making a request to `OLLAMA_BASE_URL` in the backend. For instance, a request to `/ollama/api/tags` in the WebUI is equivalent to `OLLAMA_BASE_URL/api/tags` in the backend.
-
-- **Security Benefits**: This design prevents direct exposure of the Ollama API to the frontend, safeguarding against potential CORS (Cross-Origin Resource Sharing) issues and unauthorized access. Requiring authentication to access the Ollama API further enhances this security layer.
-
-## Open WebUI: Server Connection Error
-
-If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the `--network=host` flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: `http://localhost:8080`.
-
-**Example Docker Command**:
-
-```bash
-docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main
-```
-
-### Error on Slow Responses for Ollama
-
-Open WebUI has a default timeout of 5 minutes for Ollama to finish generating the response. If needed, this can be adjusted via the environment variable AIOHTTP_CLIENT_TIMEOUT, which sets the timeout in seconds.
-
-### General Connection Errors
-
-**Ensure Ollama Version is Up-to-Date**: Always start by checking that you have the latest version of Ollama. Visit [Ollama's official site](https://ollama.com/) for the latest updates.
-
-**Troubleshooting Steps**:
-
-1. **Verify Ollama URL Format**:
-   - When running the Web UI container, ensure the `OLLAMA_BASE_URL` is correctly set. (e.g., `http://192.168.1.1:11434` for different host setups).
-   - In the Open WebUI, navigate to "Settings" > "General".
-   - Confirm that the Ollama Server URL is correctly set to `[OLLAMA URL]` (e.g., `http://localhost:11434`).
-
-By following these enhanced troubleshooting steps, connection issues should be effectively resolved. For further assistance or queries, feel free to reach out to us on our community Discord.
+# Open WebUI Troubleshooting Guide
+
+## Understanding the Open WebUI Architecture
+
+The Open WebUI system is designed to streamline interactions between the client (your browser) and the Ollama API. At the heart of this design is a backend reverse proxy, enhancing security and resolving CORS issues.
+
+- **How it Works**: The Open WebUI is designed to interact with the Ollama API through a specific route. When a request is made from the WebUI to Ollama, it is not directly sent to the Ollama API. Initially, the request is sent to the Open WebUI backend via `/ollama` route. From there, the backend is responsible for forwarding the request to the Ollama API. This forwarding is accomplished by using the route specified in the `OLLAMA_BASE_URL` environment variable. Therefore, a request made to `/ollama` in the WebUI is effectively the same as making a request to `OLLAMA_BASE_URL` in the backend. For instance, a request to `/ollama/api/tags` in the WebUI is equivalent to `OLLAMA_BASE_URL/api/tags` in the backend.
+
+- **Security Benefits**: This design prevents direct exposure of the Ollama API to the frontend, safeguarding against potential CORS (Cross-Origin Resource Sharing) issues and unauthorized access. Requiring authentication to access the Ollama API further enhances this security layer.
+
+## Open WebUI: Server Connection Error
+
+If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the `--network=host` flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: `http://localhost:8080`.
+
+**Example Docker Command**:
+
+```bash
+docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+```
+
+### Error on Slow Responses for Ollama
+
+Open WebUI has a default timeout of 5 minutes for Ollama to finish generating the response. If needed, this can be adjusted via the environment variable AIOHTTP_CLIENT_TIMEOUT, which sets the timeout in seconds.
+
+### General Connection Errors
+
+**Ensure Ollama Version is Up-to-Date**: Always start by checking that you have the latest version of Ollama. Visit [Ollama's official site](https://ollama.com/) for the latest updates.
+
+**Troubleshooting Steps**:
+
+1. **Verify Ollama URL Format**:
+   - When running the Web UI container, ensure the `OLLAMA_BASE_URL` is correctly set. (e.g., `http://192.168.1.1:11434` for different host setups).
+   - In the Open WebUI, navigate to "Settings" > "General".
+   - Confirm that the Ollama Server URL is correctly set to `[OLLAMA URL]` (e.g., `http://localhost:11434`).
+
+By following these enhanced troubleshooting steps, connection issues should be effectively resolved. For further assistance or queries, feel free to reach out to us on our community Discord.

backend/.dockerignore

@@ -1,14 +1,14 @@
-__pycache__
-.env
-_old
-uploads
-.ipynb_checkpoints
-*.db
-_test
-!/data
-/data/*
-!/data/litellm
-/data/litellm/*
-!data/litellm/config.yaml
-
+__pycache__
+.env
+_old
+uploads
+.ipynb_checkpoints
+*.db
+_test
+!/data
+/data/*
+!/data/litellm
+/data/litellm/*
+!data/litellm/config.yaml
+
 !data/config.json

backend/.gitignore

@@ -1,12 +1,12 @@
-__pycache__
-.env
-_old
-uploads
-.ipynb_checkpoints
-*.db
-_test
-Pipfile
-!/data
-/data/*
-/open_webui/data/*
+__pycache__
+.env
+_old
+uploads
+.ipynb_checkpoints
+*.db
+_test
+Pipfile
+!/data
+/data/*
+/open_webui/data/*
 .webui_secret_key

backend/open_webui/__init__.py

@@ -1,77 +1,77 @@
-import base64
-import os
-import random
-from pathlib import Path
-
-import typer
-import uvicorn
-
-app = typer.Typer()
-
-KEY_FILE = Path.cwd() / ".webui_secret_key"
-
-
-@app.command()
-def serve(
-    host: str = "0.0.0.0",
-    port: int = 8080,
-):
-    os.environ["FROM_INIT_PY"] = "true"
-    if os.getenv("WEBUI_SECRET_KEY") is None:
-        typer.echo(
-            "Loading WEBUI_SECRET_KEY from file, not provided as an environment variable."
-        )
-        if not KEY_FILE.exists():
-            typer.echo(f"Generating a new secret key and saving it to {KEY_FILE}")
-            KEY_FILE.write_bytes(base64.b64encode(random.randbytes(12)))
-        typer.echo(f"Loading WEBUI_SECRET_KEY from {KEY_FILE}")
-        os.environ["WEBUI_SECRET_KEY"] = KEY_FILE.read_text()
-
-    if os.getenv("USE_CUDA_DOCKER", "false") == "true":
-        typer.echo(
-            "CUDA is enabled, appending LD_LIBRARY_PATH to include torch/cudnn & cublas libraries."
-        )
-        LD_LIBRARY_PATH = os.getenv("LD_LIBRARY_PATH", "").split(":")
-        os.environ["LD_LIBRARY_PATH"] = ":".join(
-            LD_LIBRARY_PATH
-            + [
-                "/usr/local/lib/python3.11/site-packages/torch/lib",
-                "/usr/local/lib/python3.11/site-packages/nvidia/cudnn/lib",
-            ]
-        )
-        try:
-            import torch
-
-            assert torch.cuda.is_available(), "CUDA not available"
-            typer.echo("CUDA seems to be working")
-        except Exception as e:
-            typer.echo(
-                "Error when testing CUDA but USE_CUDA_DOCKER is true. "
-                "Resetting USE_CUDA_DOCKER to false and removing "
-                f"LD_LIBRARY_PATH modifications: {e}"
-            )
-            os.environ["USE_CUDA_DOCKER"] = "false"
-            os.environ["LD_LIBRARY_PATH"] = ":".join(LD_LIBRARY_PATH)
-
-    import open_webui.main  # we need set environment variables before importing main
-
-    uvicorn.run(open_webui.main.app, host=host, port=port, forwarded_allow_ips="*")
-
-
-@app.command()
-def dev(
-    host: str = "0.0.0.0",
-    port: int = 8080,
-    reload: bool = True,
-):
-    uvicorn.run(
-        "open_webui.main:app",
-        host=host,
-        port=port,
-        reload=reload,
-        forwarded_allow_ips="*",
-    )
-
-
-if __name__ == "__main__":
-    app()
+import base64
+import os
+import random
+from pathlib import Path
+
+import typer
+import uvicorn
+
+app = typer.Typer()
+
+KEY_FILE = Path.cwd() / ".webui_secret_key"
+
+
+@app.command()
+def serve(
+    host: str = "0.0.0.0",
+    port: int = 8080,
+):
+    os.environ["FROM_INIT_PY"] = "true"
+    if os.getenv("WEBUI_SECRET_KEY") is None:
+        typer.echo(
+            "Loading WEBUI_SECRET_KEY from file, not provided as an environment variable."
+        )
+        if not KEY_FILE.exists():
+            typer.echo(f"Generating a new secret key and saving it to {KEY_FILE}")
+            KEY_FILE.write_bytes(base64.b64encode(random.randbytes(12)))
+        typer.echo(f"Loading WEBUI_SECRET_KEY from {KEY_FILE}")
+        os.environ["WEBUI_SECRET_KEY"] = KEY_FILE.read_text()
+
+    if os.getenv("USE_CUDA_DOCKER", "false") == "true":
+        typer.echo(
+            "CUDA is enabled, appending LD_LIBRARY_PATH to include torch/cudnn & cublas libraries."
+        )
+        LD_LIBRARY_PATH = os.getenv("LD_LIBRARY_PATH", "").split(":")
+        os.environ["LD_LIBRARY_PATH"] = ":".join(
+            LD_LIBRARY_PATH
+            + [
+                "/usr/local/lib/python3.11/site-packages/torch/lib",
+                "/usr/local/lib/python3.11/site-packages/nvidia/cudnn/lib",
+            ]
+        )
+        try:
+            import torch
+
+            assert torch.cuda.is_available(), "CUDA not available"
+            typer.echo("CUDA seems to be working")
+        except Exception as e:
+            typer.echo(
+                "Error when testing CUDA but USE_CUDA_DOCKER is true. "
+                "Resetting USE_CUDA_DOCKER to false and removing "
+                f"LD_LIBRARY_PATH modifications: {e}"
+            )
+            os.environ["USE_CUDA_DOCKER"] = "false"
+            os.environ["LD_LIBRARY_PATH"] = ":".join(LD_LIBRARY_PATH)
+
+    import open_webui.main  # we need set environment variables before importing main
+
+    uvicorn.run(open_webui.main.app, host=host, port=port, forwarded_allow_ips="*")
+
+
+@app.command()
+def dev(
+    host: str = "0.0.0.0",
+    port: int = 8080,
+    reload: bool = True,
+):
+    uvicorn.run(
+        "open_webui.main:app",
+        host=host,
+        port=port,
+        reload=reload,
+        forwarded_allow_ips="*",
+    )
+
+
+if __name__ == "__main__":
+    app()

backend/open_webui/alembic.ini

@@ -1,114 +1,114 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-script_location = migrations
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the
-# "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to migrations/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:migrations/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-version_path_separator = os  # Use os.pathsep. Default configuration used for new projects.
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-# sqlalchemy.url = REPLACE_WITH_DATABASE_URL
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = --fix REVISION_SCRIPT_FILENAME
-
-# Logging configuration
-[loggers]
-keys = root,sqlalchemy,alembic
-
-[handlers]
-keys = console
-
-[formatters]
-keys = generic
-
-[logger_root]
-level = WARN
-handlers = console
-qualname =
-
-[logger_sqlalchemy]
-level = WARN
-handlers =
-qualname = sqlalchemy.engine
-
-[logger_alembic]
-level = INFO
-handlers =
-qualname = alembic
-
-[handler_console]
-class = StreamHandler
-args = (sys.stderr,)
-level = NOTSET
-formatter = generic
-
-[formatter_generic]
-format = %(levelname)-5.5s [%(name)s] %(message)s
-datefmt = %H:%M:%S
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts
+script_location = migrations
+
+# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
+# Uncomment the line below if you want the files to be prepended with date and time
+# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
+
+# sys.path path, will be prepended to sys.path if present.
+# defaults to the current working directory.
+prepend_sys_path = .
+
+# timezone to use when rendering the date within the migration file
+# as well as the filename.
+# If specified, requires the python>=3.9 or backports.zoneinfo library.
+# Any required deps can installed by adding `alembic[tz]` to the pip requirements
+# string value is passed to ZoneInfo()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the
+# "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; This defaults
+# to migrations/versions.  When using multiple version
+# directories, initial revisions must be specified with --version-path.
+# The path separator used here should be the separator specified by "version_path_separator" below.
+# version_locations = %(here)s/bar:%(here)s/bat:migrations/versions
+
+# version path separator; As mentioned above, this is the character used to split
+# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
+# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
+# Valid values for version_path_separator are:
+#
+# version_path_separator = :
+# version_path_separator = ;
+# version_path_separator = space
+version_path_separator = os  # Use os.pathsep. Default configuration used for new projects.
+
+# set to 'true' to search source files recursively
+# in each "version_locations" directory
+# new in Alembic version 1.10
+# recursive_version_locations = false
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+# sqlalchemy.url = REPLACE_WITH_DATABASE_URL
+
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts.  See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks = black
+# black.type = console_scripts
+# black.entrypoint = black
+# black.options = -l 79 REVISION_SCRIPT_FILENAME
+
+# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
+# hooks = ruff
+# ruff.type = exec
+# ruff.executable = %(here)s/.venv/bin/ruff
+# ruff.options = --fix REVISION_SCRIPT_FILENAME
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARN
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARN
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

backend/open_webui/apps/audio/main.py

@@ -1,639 +1,639 @@
-import hashlib
-import json
-import logging
-import os
-import uuid
-from functools import lru_cache
-from pathlib import Path
-from pydub import AudioSegment
-from pydub.silence import split_on_silence
-
-import requests
-from open_webui.config import (
-    AUDIO_STT_ENGINE,
-    AUDIO_STT_MODEL,
-    AUDIO_STT_OPENAI_API_BASE_URL,
-    AUDIO_STT_OPENAI_API_KEY,
-    AUDIO_TTS_API_KEY,
-    AUDIO_TTS_ENGINE,
-    AUDIO_TTS_MODEL,
-    AUDIO_TTS_OPENAI_API_BASE_URL,
-    AUDIO_TTS_OPENAI_API_KEY,
-    AUDIO_TTS_SPLIT_ON,
-    AUDIO_TTS_VOICE,
-    AUDIO_TTS_AZURE_SPEECH_REGION,
-    AUDIO_TTS_AZURE_SPEECH_OUTPUT_FORMAT,
-    CACHE_DIR,
-    CORS_ALLOW_ORIGIN,
-    WHISPER_MODEL,
-    WHISPER_MODEL_AUTO_UPDATE,
-    WHISPER_MODEL_DIR,
-    AppConfig,
-)
-
-from open_webui.constants import ERROR_MESSAGES
-from open_webui.env import SRC_LOG_LEVELS, DEVICE_TYPE
-from fastapi import Depends, FastAPI, File, HTTPException, Request, UploadFile, status
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import FileResponse
-from pydantic import BaseModel
-from open_webui.utils.utils import get_admin_user, get_verified_user
-
-# Constants
-MAX_FILE_SIZE_MB = 25
-MAX_FILE_SIZE = MAX_FILE_SIZE_MB * 1024 * 1024  # Convert MB to bytes
-
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["AUDIO"])
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=CORS_ALLOW_ORIGIN,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.state.config = AppConfig()
-
-app.state.config.STT_OPENAI_API_BASE_URL = AUDIO_STT_OPENAI_API_BASE_URL
-app.state.config.STT_OPENAI_API_KEY = AUDIO_STT_OPENAI_API_KEY
-app.state.config.STT_ENGINE = AUDIO_STT_ENGINE
-app.state.config.STT_MODEL = AUDIO_STT_MODEL
-
-app.state.config.WHISPER_MODEL = WHISPER_MODEL
-app.state.faster_whisper_model = None
-
-app.state.config.TTS_OPENAI_API_BASE_URL = AUDIO_TTS_OPENAI_API_BASE_URL
-app.state.config.TTS_OPENAI_API_KEY = AUDIO_TTS_OPENAI_API_KEY
-app.state.config.TTS_ENGINE = AUDIO_TTS_ENGINE
-app.state.config.TTS_MODEL = AUDIO_TTS_MODEL
-app.state.config.TTS_VOICE = AUDIO_TTS_VOICE
-app.state.config.TTS_API_KEY = AUDIO_TTS_API_KEY
-app.state.config.TTS_SPLIT_ON = AUDIO_TTS_SPLIT_ON
-
-app.state.config.TTS_AZURE_SPEECH_REGION = AUDIO_TTS_AZURE_SPEECH_REGION
-app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT = AUDIO_TTS_AZURE_SPEECH_OUTPUT_FORMAT
-
-# setting device type for whisper model
-whisper_device_type = DEVICE_TYPE if DEVICE_TYPE and DEVICE_TYPE == "cuda" else "cpu"
-log.info(f"whisper_device_type: {whisper_device_type}")
-
-SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
-SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)
-
-
-def set_faster_whisper_model(model: str, auto_update: bool = False):
-    if model and app.state.config.STT_ENGINE == "":
-        from faster_whisper import WhisperModel
-
-        faster_whisper_kwargs = {
-            "model_size_or_path": model,
-            "device": whisper_device_type,
-            "compute_type": "int8",
-            "download_root": WHISPER_MODEL_DIR,
-            "local_files_only": not auto_update,
-        }
-
-        try:
-            app.state.faster_whisper_model = WhisperModel(**faster_whisper_kwargs)
-        except Exception:
-            log.warning(
-                "WhisperModel initialization failed, attempting download with local_files_only=False"
-            )
-            faster_whisper_kwargs["local_files_only"] = False
-            app.state.faster_whisper_model = WhisperModel(**faster_whisper_kwargs)
-
-    else:
-        app.state.faster_whisper_model = None
-
-
-class TTSConfigForm(BaseModel):
-    OPENAI_API_BASE_URL: str
-    OPENAI_API_KEY: str
-    API_KEY: str
-    ENGINE: str
-    MODEL: str
-    VOICE: str
-    SPLIT_ON: str
-    AZURE_SPEECH_REGION: str
-    AZURE_SPEECH_OUTPUT_FORMAT: str
-
-
-class STTConfigForm(BaseModel):
-    OPENAI_API_BASE_URL: str
-    OPENAI_API_KEY: str
-    ENGINE: str
-    MODEL: str
-    WHISPER_MODEL: str
-
-
-class AudioConfigUpdateForm(BaseModel):
-    tts: TTSConfigForm
-    stt: STTConfigForm
-
-
-from pydub import AudioSegment
-from pydub.utils import mediainfo
-
-
-def is_mp4_audio(file_path):
-    """Check if the given file is an MP4 audio file."""
-    if not os.path.isfile(file_path):
-        print(f"File not found: {file_path}")
-        return False
-
-    info = mediainfo(file_path)
-    if (
-        info.get("codec_name") == "aac"
-        and info.get("codec_type") == "audio"
-        and info.get("codec_tag_string") == "mp4a"
-    ):
-        return True
-    return False
-
-
-def convert_mp4_to_wav(file_path, output_path):
-    """Convert MP4 audio file to WAV format."""
-    audio = AudioSegment.from_file(file_path, format="mp4")
-    audio.export(output_path, format="wav")
-    print(f"Converted {file_path} to {output_path}")
-
-
-@app.get("/config")
-async def get_audio_config(user=Depends(get_admin_user)):
-    return {
-        "tts": {
-            "OPENAI_API_BASE_URL": app.state.config.TTS_OPENAI_API_BASE_URL,
-            "OPENAI_API_KEY": app.state.config.TTS_OPENAI_API_KEY,
-            "API_KEY": app.state.config.TTS_API_KEY,
-            "ENGINE": app.state.config.TTS_ENGINE,
-            "MODEL": app.state.config.TTS_MODEL,
-            "VOICE": app.state.config.TTS_VOICE,
-            "SPLIT_ON": app.state.config.TTS_SPLIT_ON,
-            "AZURE_SPEECH_REGION": app.state.config.TTS_AZURE_SPEECH_REGION,
-            "AZURE_SPEECH_OUTPUT_FORMAT": app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT,
-        },
-        "stt": {
-            "OPENAI_API_BASE_URL": app.state.config.STT_OPENAI_API_BASE_URL,
-            "OPENAI_API_KEY": app.state.config.STT_OPENAI_API_KEY,
-            "ENGINE": app.state.config.STT_ENGINE,
-            "MODEL": app.state.config.STT_MODEL,
-            "WHISPER_MODEL": app.state.config.WHISPER_MODEL,
-        },
-    }
-
-
-@app.post("/config/update")
-async def update_audio_config(
-    form_data: AudioConfigUpdateForm, user=Depends(get_admin_user)
-):
-    app.state.config.TTS_OPENAI_API_BASE_URL = form_data.tts.OPENAI_API_BASE_URL
-    app.state.config.TTS_OPENAI_API_KEY = form_data.tts.OPENAI_API_KEY
-    app.state.config.TTS_API_KEY = form_data.tts.API_KEY
-    app.state.config.TTS_ENGINE = form_data.tts.ENGINE
-    app.state.config.TTS_MODEL = form_data.tts.MODEL
-    app.state.config.TTS_VOICE = form_data.tts.VOICE
-    app.state.config.TTS_SPLIT_ON = form_data.tts.SPLIT_ON
-    app.state.config.TTS_AZURE_SPEECH_REGION = form_data.tts.AZURE_SPEECH_REGION
-    app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT = (
-        form_data.tts.AZURE_SPEECH_OUTPUT_FORMAT
-    )
-
-    app.state.config.STT_OPENAI_API_BASE_URL = form_data.stt.OPENAI_API_BASE_URL
-    app.state.config.STT_OPENAI_API_KEY = form_data.stt.OPENAI_API_KEY
-    app.state.config.STT_ENGINE = form_data.stt.ENGINE
-    app.state.config.STT_MODEL = form_data.stt.MODEL
-    app.state.config.WHISPER_MODEL = form_data.stt.WHISPER_MODEL
-    set_faster_whisper_model(form_data.stt.WHISPER_MODEL, WHISPER_MODEL_AUTO_UPDATE)
-
-    return {
-        "tts": {
-            "OPENAI_API_BASE_URL": app.state.config.TTS_OPENAI_API_BASE_URL,
-            "OPENAI_API_KEY": app.state.config.TTS_OPENAI_API_KEY,
-            "API_KEY": app.state.config.TTS_API_KEY,
-            "ENGINE": app.state.config.TTS_ENGINE,
-            "MODEL": app.state.config.TTS_MODEL,
-            "VOICE": app.state.config.TTS_VOICE,
-            "SPLIT_ON": app.state.config.TTS_SPLIT_ON,
-            "AZURE_SPEECH_REGION": app.state.config.TTS_AZURE_SPEECH_REGION,
-            "AZURE_SPEECH_OUTPUT_FORMAT": app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT,
-        },
-        "stt": {
-            "OPENAI_API_BASE_URL": app.state.config.STT_OPENAI_API_BASE_URL,
-            "OPENAI_API_KEY": app.state.config.STT_OPENAI_API_KEY,
-            "ENGINE": app.state.config.STT_ENGINE,
-            "MODEL": app.state.config.STT_MODEL,
-            "WHISPER_MODEL": app.state.config.WHISPER_MODEL,
-        },
-    }
-
-
-@app.post("/speech")
-async def speech(request: Request, user=Depends(get_verified_user)):
-    body = await request.body()
-    name = hashlib.sha256(body).hexdigest()
-
-    file_path = SPEECH_CACHE_DIR.joinpath(f"{name}.mp3")
-    file_body_path = SPEECH_CACHE_DIR.joinpath(f"{name}.json")
-
-    # Check if the file already exists in the cache
-    if file_path.is_file():
-        return FileResponse(file_path)
-
-    if app.state.config.TTS_ENGINE == "openai":
-        headers = {}
-        headers["Authorization"] = f"Bearer {app.state.config.TTS_OPENAI_API_KEY}"
-        headers["Content-Type"] = "application/json"
-
-        try:
-            body = body.decode("utf-8")
-            body = json.loads(body)
-            body["model"] = app.state.config.TTS_MODEL
-            body = json.dumps(body).encode("utf-8")
-        except Exception:
-            pass
-
-        r = None
-        try:
-            r = requests.post(
-                url=f"{app.state.config.TTS_OPENAI_API_BASE_URL}/audio/speech",
-                data=body,
-                headers=headers,
-                stream=True,
-            )
-
-            r.raise_for_status()
-
-            # Save the streaming content to a file
-            with open(file_path, "wb") as f:
-                for chunk in r.iter_content(chunk_size=8192):
-                    f.write(chunk)
-
-            with open(file_body_path, "w") as f:
-                json.dump(json.loads(body.decode("utf-8")), f)
-
-            # Return the saved file
-            return FileResponse(file_path)
-
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"External: {res['error']['message']}"
-                except Exception:
-                    error_detail = f"External: {e}"
-
-            raise HTTPException(
-                status_code=r.status_code if r != None else 500,
-                detail=error_detail,
-            )
-
-    elif app.state.config.TTS_ENGINE == "elevenlabs":
-        payload = None
-        try:
-            payload = json.loads(body.decode("utf-8"))
-        except Exception as e:
-            log.exception(e)
-            raise HTTPException(status_code=400, detail="Invalid JSON payload")
-
-        voice_id = payload.get("voice", "")
-
-        if voice_id not in get_available_voices():
-            raise HTTPException(
-                status_code=400,
-                detail="Invalid voice id",
-            )
-
-        url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
-
-        headers = {
-            "Accept": "audio/mpeg",
-            "Content-Type": "application/json",
-            "xi-api-key": app.state.config.TTS_API_KEY,
-        }
-
-        data = {
-            "text": payload["input"],
-            "model_id": app.state.config.TTS_MODEL,
-            "voice_settings": {"stability": 0.5, "similarity_boost": 0.5},
-        }
-
-        try:
-            r = requests.post(url, json=data, headers=headers)
-
-            r.raise_for_status()
-
-            # Save the streaming content to a file
-            with open(file_path, "wb") as f:
-                for chunk in r.iter_content(chunk_size=8192):
-                    f.write(chunk)
-
-            with open(file_body_path, "w") as f:
-                json.dump(json.loads(body.decode("utf-8")), f)
-
-            # Return the saved file
-            return FileResponse(file_path)
-
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"External: {res['error']['message']}"
-                except Exception:
-                    error_detail = f"External: {e}"
-
-            raise HTTPException(
-                status_code=r.status_code if r != None else 500,
-                detail=error_detail,
-            )
-
-    elif app.state.config.TTS_ENGINE == "azure":
-        payload = None
-        try:
-            payload = json.loads(body.decode("utf-8"))
-        except Exception as e:
-            log.exception(e)
-            raise HTTPException(status_code=400, detail="Invalid JSON payload")
-
-        region = app.state.config.TTS_AZURE_SPEECH_REGION
-        language = app.state.config.TTS_VOICE
-        locale = "-".join(app.state.config.TTS_VOICE.split("-")[:1])
-        output_format = app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT
-        url = f"https://{region}.tts.speech.microsoft.com/cognitiveservices/v1"
-
-        headers = {
-            "Ocp-Apim-Subscription-Key": app.state.config.TTS_API_KEY,
-            "Content-Type": "application/ssml+xml",
-            "X-Microsoft-OutputFormat": output_format,
-        }
-
-        data = f"""<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="{locale}">
-                <voice name="{language}">{payload["input"]}</voice>
-            </speak>"""
-
-        response = requests.post(url, headers=headers, data=data)
-
-        if response.status_code == 200:
-            with open(file_path, "wb") as f:
-                f.write(response.content)
-            return FileResponse(file_path)
-        else:
-            log.error(f"Error synthesizing speech - {response.reason}")
-            raise HTTPException(
-                status_code=500, detail=f"Error synthesizing speech - {response.reason}"
-            )
-
-
-def transcribe(file_path):
-    print("transcribe", file_path)
-    filename = os.path.basename(file_path)
-    file_dir = os.path.dirname(file_path)
-    id = filename.split(".")[0]
-
-    if app.state.config.STT_ENGINE == "":
-        if app.state.faster_whisper_model is None:
-            set_faster_whisper_model(app.state.config.WHISPER_MODEL)
-
-        model = app.state.faster_whisper_model
-        segments, info = model.transcribe(file_path, beam_size=5)
-        log.info(
-            "Detected language '%s' with probability %f"
-            % (info.language, info.language_probability)
-        )
-
-        transcript = "".join([segment.text for segment in list(segments)])
-        data = {"text": transcript.strip()}
-
-        # save the transcript to a json file
-        transcript_file = f"{file_dir}/{id}.json"
-        with open(transcript_file, "w") as f:
-            json.dump(data, f)
-
-        log.debug(data)
-        return data
-    elif app.state.config.STT_ENGINE == "openai":
-        if is_mp4_audio(file_path):
-            print("is_mp4_audio")
-            os.rename(file_path, file_path.replace(".wav", ".mp4"))
-            # Convert MP4 audio file to WAV format
-            convert_mp4_to_wav(file_path.replace(".wav", ".mp4"), file_path)
-
-        headers = {"Authorization": f"Bearer {app.state.config.STT_OPENAI_API_KEY}"}
-
-        files = {"file": (filename, open(file_path, "rb"))}
-        data = {"model": app.state.config.STT_MODEL}
-
-        log.debug(files, data)
-
-        r = None
-        try:
-            r = requests.post(
-                url=f"{app.state.config.STT_OPENAI_API_BASE_URL}/audio/transcriptions",
-                headers=headers,
-                files=files,
-                data=data,
-            )
-
-            r.raise_for_status()
-
-            data = r.json()
-
-            # save the transcript to a json file
-            transcript_file = f"{file_dir}/{id}.json"
-            with open(transcript_file, "w") as f:
-                json.dump(data, f)
-
-            print(data)
-            return data
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"External: {res['error']['message']}"
-                except Exception:
-                    error_detail = f"External: {e}"
-
-            raise Exception(error_detail)
-
-
-@app.post("/transcriptions")
-def transcription(
-    file: UploadFile = File(...),
-    user=Depends(get_verified_user),
-):
-    log.info(f"file.content_type: {file.content_type}")
-
-    if file.content_type not in ["audio/mpeg", "audio/wav", "audio/ogg", "audio/x-m4a"]:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.FILE_NOT_SUPPORTED,
-        )
-
-    try:
-        ext = file.filename.split(".")[-1]
-        id = uuid.uuid4()
-
-        filename = f"{id}.{ext}"
-        contents = file.file.read()
-
-        file_dir = f"{CACHE_DIR}/audio/transcriptions"
-        os.makedirs(file_dir, exist_ok=True)
-        file_path = f"{file_dir}/{filename}"
-
-        with open(file_path, "wb") as f:
-            f.write(contents)
-
-        try:
-            if os.path.getsize(file_path) > MAX_FILE_SIZE:  # file is bigger than 25MB
-                log.debug(f"File size is larger than {MAX_FILE_SIZE_MB}MB")
-                audio = AudioSegment.from_file(file_path)
-                audio = audio.set_frame_rate(16000).set_channels(1)  # Compress audio
-                compressed_path = f"{file_dir}/{id}_compressed.opus"
-                audio.export(compressed_path, format="opus", bitrate="32k")
-                log.debug(f"Compressed audio to {compressed_path}")
-                file_path = compressed_path
-
-                if (
-                    os.path.getsize(file_path) > MAX_FILE_SIZE
-                ):  # Still larger than 25MB after compression
-                    log.debug(
-                        f"Compressed file size is still larger than {MAX_FILE_SIZE_MB}MB: {os.path.getsize(file_path)}"
-                    )
-                    raise HTTPException(
-                        status_code=status.HTTP_400_BAD_REQUEST,
-                        detail=ERROR_MESSAGES.FILE_TOO_LARGE(
-                            size=f"{MAX_FILE_SIZE_MB}MB"
-                        ),
-                    )
-
-                data = transcribe(file_path)
-            else:
-                data = transcribe(file_path)
-
-            return data
-        except Exception as e:
-            log.exception(e)
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=ERROR_MESSAGES.DEFAULT(e),
-            )
-
-    except Exception as e:
-        log.exception(e)
-
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-def get_available_models() -> list[dict]:
-    if app.state.config.TTS_ENGINE == "openai":
-        return [{"id": "tts-1"}, {"id": "tts-1-hd"}]
-    elif app.state.config.TTS_ENGINE == "elevenlabs":
-        headers = {
-            "xi-api-key": app.state.config.TTS_API_KEY,
-            "Content-Type": "application/json",
-        }
-
-        try:
-            response = requests.get(
-                "https://api.elevenlabs.io/v1/models", headers=headers, timeout=5
-            )
-            response.raise_for_status()
-            models = response.json()
-            return [
-                {"name": model["name"], "id": model["model_id"]} for model in models
-            ]
-        except requests.RequestException as e:
-            log.error(f"Error fetching voices: {str(e)}")
-    return []
-
-
-@app.get("/models")
-async def get_models(user=Depends(get_verified_user)):
-    return {"models": get_available_models()}
-
-
-def get_available_voices() -> dict:
-    """Returns {voice_id: voice_name} dict"""
-    ret = {}
-    if app.state.config.TTS_ENGINE == "openai":
-        ret = {
-            "alloy": "alloy",
-            "echo": "echo",
-            "fable": "fable",
-            "onyx": "onyx",
-            "nova": "nova",
-            "shimmer": "shimmer",
-        }
-    elif app.state.config.TTS_ENGINE == "elevenlabs":
-        try:
-            ret = get_elevenlabs_voices()
-        except Exception:
-            # Avoided @lru_cache with exception
-            pass
-    elif app.state.config.TTS_ENGINE == "azure":
-        try:
-            region = app.state.config.TTS_AZURE_SPEECH_REGION
-            url = f"https://{region}.tts.speech.microsoft.com/cognitiveservices/voices/list"
-            headers = {"Ocp-Apim-Subscription-Key": app.state.config.TTS_API_KEY}
-
-            response = requests.get(url, headers=headers)
-            response.raise_for_status()
-            voices = response.json()
-            for voice in voices:
-                ret[voice["ShortName"]] = (
-                    f"{voice['DisplayName']} ({voice['ShortName']})"
-                )
-        except requests.RequestException as e:
-            log.error(f"Error fetching voices: {str(e)}")
-
-    return ret
-
-
-@lru_cache
-def get_elevenlabs_voices() -> dict:
-    """
-    Note, set the following in your .env file to use Elevenlabs:
-    AUDIO_TTS_ENGINE=elevenlabs
-    AUDIO_TTS_API_KEY=sk_...  # Your Elevenlabs API key
-    AUDIO_TTS_VOICE=EXAVITQu4vr4xnSDxMaL  # From https://api.elevenlabs.io/v1/voices
-    AUDIO_TTS_MODEL=eleven_multilingual_v2
-    """
-    headers = {
-        "xi-api-key": app.state.config.TTS_API_KEY,
-        "Content-Type": "application/json",
-    }
-    try:
-        # TODO: Add retries
-        response = requests.get("https://api.elevenlabs.io/v1/voices", headers=headers)
-        response.raise_for_status()
-        voices_data = response.json()
-
-        voices = {}
-        for voice in voices_data.get("voices", []):
-            voices[voice["voice_id"]] = voice["name"]
-    except requests.RequestException as e:
-        # Avoid @lru_cache with exception
-        log.error(f"Error fetching voices: {str(e)}")
-        raise RuntimeError(f"Error fetching voices: {str(e)}")
-
-    return voices
-
-
-@app.get("/voices")
-async def get_voices(user=Depends(get_verified_user)):
-    return {"voices": [{"id": k, "name": v} for k, v in get_available_voices().items()]}
+import hashlib
+import json
+import logging
+import os
+import uuid
+from functools import lru_cache
+from pathlib import Path
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+
+import requests
+from open_webui.config import (
+    AUDIO_STT_ENGINE,
+    AUDIO_STT_MODEL,
+    AUDIO_STT_OPENAI_API_BASE_URL,
+    AUDIO_STT_OPENAI_API_KEY,
+    AUDIO_TTS_API_KEY,
+    AUDIO_TTS_ENGINE,
+    AUDIO_TTS_MODEL,
+    AUDIO_TTS_OPENAI_API_BASE_URL,
+    AUDIO_TTS_OPENAI_API_KEY,
+    AUDIO_TTS_SPLIT_ON,
+    AUDIO_TTS_VOICE,
+    AUDIO_TTS_AZURE_SPEECH_REGION,
+    AUDIO_TTS_AZURE_SPEECH_OUTPUT_FORMAT,
+    CACHE_DIR,
+    CORS_ALLOW_ORIGIN,
+    WHISPER_MODEL,
+    WHISPER_MODEL_AUTO_UPDATE,
+    WHISPER_MODEL_DIR,
+    AppConfig,
+)
+
+from open_webui.constants import ERROR_MESSAGES
+from open_webui.env import SRC_LOG_LEVELS, DEVICE_TYPE
+from fastapi import Depends, FastAPI, File, HTTPException, Request, UploadFile, status
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+from open_webui.utils.utils import get_admin_user, get_verified_user
+
+# Constants
+MAX_FILE_SIZE_MB = 25
+MAX_FILE_SIZE = MAX_FILE_SIZE_MB * 1024 * 1024  # Convert MB to bytes
+
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["AUDIO"])
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ALLOW_ORIGIN,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.state.config = AppConfig()
+
+app.state.config.STT_OPENAI_API_BASE_URL = AUDIO_STT_OPENAI_API_BASE_URL
+app.state.config.STT_OPENAI_API_KEY = AUDIO_STT_OPENAI_API_KEY
+app.state.config.STT_ENGINE = AUDIO_STT_ENGINE
+app.state.config.STT_MODEL = AUDIO_STT_MODEL
+
+app.state.config.WHISPER_MODEL = WHISPER_MODEL
+app.state.faster_whisper_model = None
+
+app.state.config.TTS_OPENAI_API_BASE_URL = AUDIO_TTS_OPENAI_API_BASE_URL
+app.state.config.TTS_OPENAI_API_KEY = AUDIO_TTS_OPENAI_API_KEY
+app.state.config.TTS_ENGINE = AUDIO_TTS_ENGINE
+app.state.config.TTS_MODEL = AUDIO_TTS_MODEL
+app.state.config.TTS_VOICE = AUDIO_TTS_VOICE
+app.state.config.TTS_API_KEY = AUDIO_TTS_API_KEY
+app.state.config.TTS_SPLIT_ON = AUDIO_TTS_SPLIT_ON
+
+app.state.config.TTS_AZURE_SPEECH_REGION = AUDIO_TTS_AZURE_SPEECH_REGION
+app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT = AUDIO_TTS_AZURE_SPEECH_OUTPUT_FORMAT
+
+# setting device type for whisper model
+whisper_device_type = DEVICE_TYPE if DEVICE_TYPE and DEVICE_TYPE == "cuda" else "cpu"
+log.info(f"whisper_device_type: {whisper_device_type}")
+
+SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
+SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def set_faster_whisper_model(model: str, auto_update: bool = False):
+    if model and app.state.config.STT_ENGINE == "":
+        from faster_whisper import WhisperModel
+
+        faster_whisper_kwargs = {
+            "model_size_or_path": model,
+            "device": whisper_device_type,
+            "compute_type": "int8",
+            "download_root": WHISPER_MODEL_DIR,
+            "local_files_only": not auto_update,
+        }
+
+        try:
+            app.state.faster_whisper_model = WhisperModel(**faster_whisper_kwargs)
+        except Exception:
+            log.warning(
+                "WhisperModel initialization failed, attempting download with local_files_only=False"
+            )
+            faster_whisper_kwargs["local_files_only"] = False
+            app.state.faster_whisper_model = WhisperModel(**faster_whisper_kwargs)
+
+    else:
+        app.state.faster_whisper_model = None
+
+
+class TTSConfigForm(BaseModel):
+    OPENAI_API_BASE_URL: str
+    OPENAI_API_KEY: str
+    API_KEY: str
+    ENGINE: str
+    MODEL: str
+    VOICE: str
+    SPLIT_ON: str
+    AZURE_SPEECH_REGION: str
+    AZURE_SPEECH_OUTPUT_FORMAT: str
+
+
+class STTConfigForm(BaseModel):
+    OPENAI_API_BASE_URL: str
+    OPENAI_API_KEY: str
+    ENGINE: str
+    MODEL: str
+    WHISPER_MODEL: str
+
+
+class AudioConfigUpdateForm(BaseModel):
+    tts: TTSConfigForm
+    stt: STTConfigForm
+
+
+from pydub import AudioSegment
+from pydub.utils import mediainfo
+
+
+def is_mp4_audio(file_path):
+    """Check if the given file is an MP4 audio file."""
+    if not os.path.isfile(file_path):
+        print(f"File not found: {file_path}")
+        return False
+
+    info = mediainfo(file_path)
+    if (
+        info.get("codec_name") == "aac"
+        and info.get("codec_type") == "audio"
+        and info.get("codec_tag_string") == "mp4a"
+    ):
+        return True
+    return False
+
+
+def convert_mp4_to_wav(file_path, output_path):
+    """Convert MP4 audio file to WAV format."""
+    audio = AudioSegment.from_file(file_path, format="mp4")
+    audio.export(output_path, format="wav")
+    print(f"Converted {file_path} to {output_path}")
+
+
+@app.get("/config")
+async def get_audio_config(user=Depends(get_admin_user)):
+    return {
+        "tts": {
+            "OPENAI_API_BASE_URL": app.state.config.TTS_OPENAI_API_BASE_URL,
+            "OPENAI_API_KEY": app.state.config.TTS_OPENAI_API_KEY,
+            "API_KEY": app.state.config.TTS_API_KEY,
+            "ENGINE": app.state.config.TTS_ENGINE,
+            "MODEL": app.state.config.TTS_MODEL,
+            "VOICE": app.state.config.TTS_VOICE,
+            "SPLIT_ON": app.state.config.TTS_SPLIT_ON,
+            "AZURE_SPEECH_REGION": app.state.config.TTS_AZURE_SPEECH_REGION,
+            "AZURE_SPEECH_OUTPUT_FORMAT": app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT,
+        },
+        "stt": {
+            "OPENAI_API_BASE_URL": app.state.config.STT_OPENAI_API_BASE_URL,
+            "OPENAI_API_KEY": app.state.config.STT_OPENAI_API_KEY,
+            "ENGINE": app.state.config.STT_ENGINE,
+            "MODEL": app.state.config.STT_MODEL,
+            "WHISPER_MODEL": app.state.config.WHISPER_MODEL,
+        },
+    }
+
+
+@app.post("/config/update")
+async def update_audio_config(
+    form_data: AudioConfigUpdateForm, user=Depends(get_admin_user)
+):
+    app.state.config.TTS_OPENAI_API_BASE_URL = form_data.tts.OPENAI_API_BASE_URL
+    app.state.config.TTS_OPENAI_API_KEY = form_data.tts.OPENAI_API_KEY
+    app.state.config.TTS_API_KEY = form_data.tts.API_KEY
+    app.state.config.TTS_ENGINE = form_data.tts.ENGINE
+    app.state.config.TTS_MODEL = form_data.tts.MODEL
+    app.state.config.TTS_VOICE = form_data.tts.VOICE
+    app.state.config.TTS_SPLIT_ON = form_data.tts.SPLIT_ON
+    app.state.config.TTS_AZURE_SPEECH_REGION = form_data.tts.AZURE_SPEECH_REGION
+    app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT = (
+        form_data.tts.AZURE_SPEECH_OUTPUT_FORMAT
+    )
+
+    app.state.config.STT_OPENAI_API_BASE_URL = form_data.stt.OPENAI_API_BASE_URL
+    app.state.config.STT_OPENAI_API_KEY = form_data.stt.OPENAI_API_KEY
+    app.state.config.STT_ENGINE = form_data.stt.ENGINE
+    app.state.config.STT_MODEL = form_data.stt.MODEL
+    app.state.config.WHISPER_MODEL = form_data.stt.WHISPER_MODEL
+    set_faster_whisper_model(form_data.stt.WHISPER_MODEL, WHISPER_MODEL_AUTO_UPDATE)
+
+    return {
+        "tts": {
+            "OPENAI_API_BASE_URL": app.state.config.TTS_OPENAI_API_BASE_URL,
+            "OPENAI_API_KEY": app.state.config.TTS_OPENAI_API_KEY,
+            "API_KEY": app.state.config.TTS_API_KEY,
+            "ENGINE": app.state.config.TTS_ENGINE,
+            "MODEL": app.state.config.TTS_MODEL,
+            "VOICE": app.state.config.TTS_VOICE,
+            "SPLIT_ON": app.state.config.TTS_SPLIT_ON,
+            "AZURE_SPEECH_REGION": app.state.config.TTS_AZURE_SPEECH_REGION,
+            "AZURE_SPEECH_OUTPUT_FORMAT": app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT,
+        },
+        "stt": {
+            "OPENAI_API_BASE_URL": app.state.config.STT_OPENAI_API_BASE_URL,
+            "OPENAI_API_KEY": app.state.config.STT_OPENAI_API_KEY,
+            "ENGINE": app.state.config.STT_ENGINE,
+            "MODEL": app.state.config.STT_MODEL,
+            "WHISPER_MODEL": app.state.config.WHISPER_MODEL,
+        },
+    }
+
+
+@app.post("/speech")
+async def speech(request: Request, user=Depends(get_verified_user)):
+    body = await request.body()
+    name = hashlib.sha256(body).hexdigest()
+
+    file_path = SPEECH_CACHE_DIR.joinpath(f"{name}.mp3")
+    file_body_path = SPEECH_CACHE_DIR.joinpath(f"{name}.json")
+
+    # Check if the file already exists in the cache
+    if file_path.is_file():
+        return FileResponse(file_path)
+
+    if app.state.config.TTS_ENGINE == "openai":
+        headers = {}
+        headers["Authorization"] = f"Bearer {app.state.config.TTS_OPENAI_API_KEY}"
+        headers["Content-Type"] = "application/json"
+
+        try:
+            body = body.decode("utf-8")
+            body = json.loads(body)
+            body["model"] = app.state.config.TTS_MODEL
+            body = json.dumps(body).encode("utf-8")
+        except Exception:
+            pass
+
+        r = None
+        try:
+            r = requests.post(
+                url=f"{app.state.config.TTS_OPENAI_API_BASE_URL}/audio/speech",
+                data=body,
+                headers=headers,
+                stream=True,
+            )
+
+            r.raise_for_status()
+
+            # Save the streaming content to a file
+            with open(file_path, "wb") as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    f.write(chunk)
+
+            with open(file_body_path, "w") as f:
+                json.dump(json.loads(body.decode("utf-8")), f)
+
+            # Return the saved file
+            return FileResponse(file_path)
+
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"External: {res['error']['message']}"
+                except Exception:
+                    error_detail = f"External: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r != None else 500,
+                detail=error_detail,
+            )
+
+    elif app.state.config.TTS_ENGINE == "elevenlabs":
+        payload = None
+        try:
+            payload = json.loads(body.decode("utf-8"))
+        except Exception as e:
+            log.exception(e)
+            raise HTTPException(status_code=400, detail="Invalid JSON payload")
+
+        voice_id = payload.get("voice", "")
+
+        if voice_id not in get_available_voices():
+            raise HTTPException(
+                status_code=400,
+                detail="Invalid voice id",
+            )
+
+        url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
+
+        headers = {
+            "Accept": "audio/mpeg",
+            "Content-Type": "application/json",
+            "xi-api-key": app.state.config.TTS_API_KEY,
+        }
+
+        data = {
+            "text": payload["input"],
+            "model_id": app.state.config.TTS_MODEL,
+            "voice_settings": {"stability": 0.5, "similarity_boost": 0.5},
+        }
+
+        try:
+            r = requests.post(url, json=data, headers=headers)
+
+            r.raise_for_status()
+
+            # Save the streaming content to a file
+            with open(file_path, "wb") as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    f.write(chunk)
+
+            with open(file_body_path, "w") as f:
+                json.dump(json.loads(body.decode("utf-8")), f)
+
+            # Return the saved file
+            return FileResponse(file_path)
+
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"External: {res['error']['message']}"
+                except Exception:
+                    error_detail = f"External: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r != None else 500,
+                detail=error_detail,
+            )
+
+    elif app.state.config.TTS_ENGINE == "azure":
+        payload = None
+        try:
+            payload = json.loads(body.decode("utf-8"))
+        except Exception as e:
+            log.exception(e)
+            raise HTTPException(status_code=400, detail="Invalid JSON payload")
+
+        region = app.state.config.TTS_AZURE_SPEECH_REGION
+        language = app.state.config.TTS_VOICE
+        locale = "-".join(app.state.config.TTS_VOICE.split("-")[:1])
+        output_format = app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT
+        url = f"https://{region}.tts.speech.microsoft.com/cognitiveservices/v1"
+
+        headers = {
+            "Ocp-Apim-Subscription-Key": app.state.config.TTS_API_KEY,
+            "Content-Type": "application/ssml+xml",
+            "X-Microsoft-OutputFormat": output_format,
+        }
+
+        data = f"""<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="{locale}">
+                <voice name="{language}">{payload["input"]}</voice>
+            </speak>"""
+
+        response = requests.post(url, headers=headers, data=data)
+
+        if response.status_code == 200:
+            with open(file_path, "wb") as f:
+                f.write(response.content)
+            return FileResponse(file_path)
+        else:
+            log.error(f"Error synthesizing speech - {response.reason}")
+            raise HTTPException(
+                status_code=500, detail=f"Error synthesizing speech - {response.reason}"
+            )
+
+
+def transcribe(file_path):
+    print("transcribe", file_path)
+    filename = os.path.basename(file_path)
+    file_dir = os.path.dirname(file_path)
+    id = filename.split(".")[0]
+
+    if app.state.config.STT_ENGINE == "":
+        if app.state.faster_whisper_model is None:
+            set_faster_whisper_model(app.state.config.WHISPER_MODEL)
+
+        model = app.state.faster_whisper_model
+        segments, info = model.transcribe(file_path, beam_size=5)
+        log.info(
+            "Detected language '%s' with probability %f"
+            % (info.language, info.language_probability)
+        )
+
+        transcript = "".join([segment.text for segment in list(segments)])
+        data = {"text": transcript.strip()}
+
+        # save the transcript to a json file
+        transcript_file = f"{file_dir}/{id}.json"
+        with open(transcript_file, "w") as f:
+            json.dump(data, f)
+
+        log.debug(data)
+        return data
+    elif app.state.config.STT_ENGINE == "openai":
+        if is_mp4_audio(file_path):
+            print("is_mp4_audio")
+            os.rename(file_path, file_path.replace(".wav", ".mp4"))
+            # Convert MP4 audio file to WAV format
+            convert_mp4_to_wav(file_path.replace(".wav", ".mp4"), file_path)
+
+        headers = {"Authorization": f"Bearer {app.state.config.STT_OPENAI_API_KEY}"}
+
+        files = {"file": (filename, open(file_path, "rb"))}
+        data = {"model": app.state.config.STT_MODEL}
+
+        log.debug(files, data)
+
+        r = None
+        try:
+            r = requests.post(
+                url=f"{app.state.config.STT_OPENAI_API_BASE_URL}/audio/transcriptions",
+                headers=headers,
+                files=files,
+                data=data,
+            )
+
+            r.raise_for_status()
+
+            data = r.json()
+
+            # save the transcript to a json file
+            transcript_file = f"{file_dir}/{id}.json"
+            with open(transcript_file, "w") as f:
+                json.dump(data, f)
+
+            print(data)
+            return data
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"External: {res['error']['message']}"
+                except Exception:
+                    error_detail = f"External: {e}"
+
+            raise Exception(error_detail)
+
+
+@app.post("/transcriptions")
+def transcription(
+    file: UploadFile = File(...),
+    user=Depends(get_verified_user),
+):
+    log.info(f"file.content_type: {file.content_type}")
+
+    if file.content_type not in ["audio/mpeg", "audio/wav", "audio/ogg", "audio/x-m4a"]:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.FILE_NOT_SUPPORTED,
+        )
+
+    try:
+        ext = file.filename.split(".")[-1]
+        id = uuid.uuid4()
+
+        filename = f"{id}.{ext}"
+        contents = file.file.read()
+
+        file_dir = f"{CACHE_DIR}/audio/transcriptions"
+        os.makedirs(file_dir, exist_ok=True)
+        file_path = f"{file_dir}/{filename}"
+
+        with open(file_path, "wb") as f:
+            f.write(contents)
+
+        try:
+            if os.path.getsize(file_path) > MAX_FILE_SIZE:  # file is bigger than 25MB
+                log.debug(f"File size is larger than {MAX_FILE_SIZE_MB}MB")
+                audio = AudioSegment.from_file(file_path)
+                audio = audio.set_frame_rate(16000).set_channels(1)  # Compress audio
+                compressed_path = f"{file_dir}/{id}_compressed.opus"
+                audio.export(compressed_path, format="opus", bitrate="32k")
+                log.debug(f"Compressed audio to {compressed_path}")
+                file_path = compressed_path
+
+                if (
+                    os.path.getsize(file_path) > MAX_FILE_SIZE
+                ):  # Still larger than 25MB after compression
+                    log.debug(
+                        f"Compressed file size is still larger than {MAX_FILE_SIZE_MB}MB: {os.path.getsize(file_path)}"
+                    )
+                    raise HTTPException(
+                        status_code=status.HTTP_400_BAD_REQUEST,
+                        detail=ERROR_MESSAGES.FILE_TOO_LARGE(
+                            size=f"{MAX_FILE_SIZE_MB}MB"
+                        ),
+                    )
+
+                data = transcribe(file_path)
+            else:
+                data = transcribe(file_path)
+
+            return data
+        except Exception as e:
+            log.exception(e)
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=ERROR_MESSAGES.DEFAULT(e),
+            )
+
+    except Exception as e:
+        log.exception(e)
+
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+def get_available_models() -> list[dict]:
+    if app.state.config.TTS_ENGINE == "openai":
+        return [{"id": "tts-1"}, {"id": "tts-1-hd"}]
+    elif app.state.config.TTS_ENGINE == "elevenlabs":
+        headers = {
+            "xi-api-key": app.state.config.TTS_API_KEY,
+            "Content-Type": "application/json",
+        }
+
+        try:
+            response = requests.get(
+                "https://api.elevenlabs.io/v1/models", headers=headers, timeout=5
+            )
+            response.raise_for_status()
+            models = response.json()
+            return [
+                {"name": model["name"], "id": model["model_id"]} for model in models
+            ]
+        except requests.RequestException as e:
+            log.error(f"Error fetching voices: {str(e)}")
+    return []
+
+
+@app.get("/models")
+async def get_models(user=Depends(get_verified_user)):
+    return {"models": get_available_models()}
+
+
+def get_available_voices() -> dict:
+    """Returns {voice_id: voice_name} dict"""
+    ret = {}
+    if app.state.config.TTS_ENGINE == "openai":
+        ret = {
+            "alloy": "alloy",
+            "echo": "echo",
+            "fable": "fable",
+            "onyx": "onyx",
+            "nova": "nova",
+            "shimmer": "shimmer",
+        }
+    elif app.state.config.TTS_ENGINE == "elevenlabs":
+        try:
+            ret = get_elevenlabs_voices()
+        except Exception:
+            # Avoided @lru_cache with exception
+            pass
+    elif app.state.config.TTS_ENGINE == "azure":
+        try:
+            region = app.state.config.TTS_AZURE_SPEECH_REGION
+            url = f"https://{region}.tts.speech.microsoft.com/cognitiveservices/voices/list"
+            headers = {"Ocp-Apim-Subscription-Key": app.state.config.TTS_API_KEY}
+
+            response = requests.get(url, headers=headers)
+            response.raise_for_status()
+            voices = response.json()
+            for voice in voices:
+                ret[voice["ShortName"]] = (
+                    f"{voice['DisplayName']} ({voice['ShortName']})"
+                )
+        except requests.RequestException as e:
+            log.error(f"Error fetching voices: {str(e)}")
+
+    return ret
+
+
+@lru_cache
+def get_elevenlabs_voices() -> dict:
+    """
+    Note, set the following in your .env file to use Elevenlabs:
+    AUDIO_TTS_ENGINE=elevenlabs
+    AUDIO_TTS_API_KEY=sk_...  # Your Elevenlabs API key
+    AUDIO_TTS_VOICE=EXAVITQu4vr4xnSDxMaL  # From https://api.elevenlabs.io/v1/voices
+    AUDIO_TTS_MODEL=eleven_multilingual_v2
+    """
+    headers = {
+        "xi-api-key": app.state.config.TTS_API_KEY,
+        "Content-Type": "application/json",
+    }
+    try:
+        # TODO: Add retries
+        response = requests.get("https://api.elevenlabs.io/v1/voices", headers=headers)
+        response.raise_for_status()
+        voices_data = response.json()
+
+        voices = {}
+        for voice in voices_data.get("voices", []):
+            voices[voice["voice_id"]] = voice["name"]
+    except requests.RequestException as e:
+        # Avoid @lru_cache with exception
+        log.error(f"Error fetching voices: {str(e)}")
+        raise RuntimeError(f"Error fetching voices: {str(e)}")
+
+    return voices
+
+
+@app.get("/voices")
+async def get_voices(user=Depends(get_verified_user)):
+    return {"voices": [{"id": k, "name": v} for k, v in get_available_voices().items()]}

backend/open_webui/apps/images/main.py

@@ -1,597 +1,597 @@
-import asyncio
-import base64
-import json
-import logging
-import mimetypes
-import re
-import uuid
-from pathlib import Path
-from typing import Optional
-
-import requests
-from open_webui.apps.images.utils.comfyui import (
-    ComfyUIGenerateImageForm,
-    ComfyUIWorkflow,
-    comfyui_generate_image,
-)
-from open_webui.config import (
-    AUTOMATIC1111_API_AUTH,
-    AUTOMATIC1111_BASE_URL,
-    AUTOMATIC1111_CFG_SCALE,
-    AUTOMATIC1111_SAMPLER,
-    AUTOMATIC1111_SCHEDULER,
-    CACHE_DIR,
-    COMFYUI_BASE_URL,
-    COMFYUI_WORKFLOW,
-    COMFYUI_WORKFLOW_NODES,
-    CORS_ALLOW_ORIGIN,
-    ENABLE_IMAGE_GENERATION,
-    IMAGE_GENERATION_ENGINE,
-    IMAGE_GENERATION_MODEL,
-    IMAGE_SIZE,
-    IMAGE_STEPS,
-    IMAGES_OPENAI_API_BASE_URL,
-    IMAGES_OPENAI_API_KEY,
-    AppConfig,
-)
-from open_webui.constants import ERROR_MESSAGES
-from open_webui.env import SRC_LOG_LEVELS
-from fastapi import Depends, FastAPI, HTTPException, Request
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-from open_webui.utils.utils import get_admin_user, get_verified_user
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["IMAGES"])
-
-IMAGE_CACHE_DIR = Path(CACHE_DIR).joinpath("./image/generations/")
-IMAGE_CACHE_DIR.mkdir(parents=True, exist_ok=True)
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=CORS_ALLOW_ORIGIN,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.state.config = AppConfig()
-
-app.state.config.ENGINE = IMAGE_GENERATION_ENGINE
-app.state.config.ENABLED = ENABLE_IMAGE_GENERATION
-
-app.state.config.OPENAI_API_BASE_URL = IMAGES_OPENAI_API_BASE_URL
-app.state.config.OPENAI_API_KEY = IMAGES_OPENAI_API_KEY
-
-app.state.config.MODEL = IMAGE_GENERATION_MODEL
-
-app.state.config.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
-app.state.config.AUTOMATIC1111_API_AUTH = AUTOMATIC1111_API_AUTH
-app.state.config.AUTOMATIC1111_CFG_SCALE = AUTOMATIC1111_CFG_SCALE
-app.state.config.AUTOMATIC1111_SAMPLER = AUTOMATIC1111_SAMPLER
-app.state.config.AUTOMATIC1111_SCHEDULER = AUTOMATIC1111_SCHEDULER
-app.state.config.COMFYUI_BASE_URL = COMFYUI_BASE_URL
-app.state.config.COMFYUI_WORKFLOW = COMFYUI_WORKFLOW
-app.state.config.COMFYUI_WORKFLOW_NODES = COMFYUI_WORKFLOW_NODES
-
-app.state.config.IMAGE_SIZE = IMAGE_SIZE
-app.state.config.IMAGE_STEPS = IMAGE_STEPS
-
-
-@app.get("/config")
-async def get_config(request: Request, user=Depends(get_admin_user)):
-    return {
-        "enabled": app.state.config.ENABLED,
-        "engine": app.state.config.ENGINE,
-        "openai": {
-            "OPENAI_API_BASE_URL": app.state.config.OPENAI_API_BASE_URL,
-            "OPENAI_API_KEY": app.state.config.OPENAI_API_KEY,
-        },
-        "automatic1111": {
-            "AUTOMATIC1111_BASE_URL": app.state.config.AUTOMATIC1111_BASE_URL,
-            "AUTOMATIC1111_API_AUTH": app.state.config.AUTOMATIC1111_API_AUTH,
-            "AUTOMATIC1111_CFG_SCALE": app.state.config.AUTOMATIC1111_CFG_SCALE,
-            "AUTOMATIC1111_SAMPLER": app.state.config.AUTOMATIC1111_SAMPLER,
-            "AUTOMATIC1111_SCHEDULER": app.state.config.AUTOMATIC1111_SCHEDULER,
-        },
-        "comfyui": {
-            "COMFYUI_BASE_URL": app.state.config.COMFYUI_BASE_URL,
-            "COMFYUI_WORKFLOW": app.state.config.COMFYUI_WORKFLOW,
-            "COMFYUI_WORKFLOW_NODES": app.state.config.COMFYUI_WORKFLOW_NODES,
-        },
-    }
-
-
-class OpenAIConfigForm(BaseModel):
-    OPENAI_API_BASE_URL: str
-    OPENAI_API_KEY: str
-
-
-class Automatic1111ConfigForm(BaseModel):
-    AUTOMATIC1111_BASE_URL: str
-    AUTOMATIC1111_API_AUTH: str
-    AUTOMATIC1111_CFG_SCALE: Optional[str]
-    AUTOMATIC1111_SAMPLER: Optional[str]
-    AUTOMATIC1111_SCHEDULER: Optional[str]
-
-
-class ComfyUIConfigForm(BaseModel):
-    COMFYUI_BASE_URL: str
-    COMFYUI_WORKFLOW: str
-    COMFYUI_WORKFLOW_NODES: list[dict]
-
-
-class ConfigForm(BaseModel):
-    enabled: bool
-    engine: str
-    openai: OpenAIConfigForm
-    automatic1111: Automatic1111ConfigForm
-    comfyui: ComfyUIConfigForm
-
-
-@app.post("/config/update")
-async def update_config(form_data: ConfigForm, user=Depends(get_admin_user)):
-    app.state.config.ENGINE = form_data.engine
-    app.state.config.ENABLED = form_data.enabled
-
-    app.state.config.OPENAI_API_BASE_URL = form_data.openai.OPENAI_API_BASE_URL
-    app.state.config.OPENAI_API_KEY = form_data.openai.OPENAI_API_KEY
-
-    app.state.config.AUTOMATIC1111_BASE_URL = (
-        form_data.automatic1111.AUTOMATIC1111_BASE_URL
-    )
-    app.state.config.AUTOMATIC1111_API_AUTH = (
-        form_data.automatic1111.AUTOMATIC1111_API_AUTH
-    )
-
-    app.state.config.AUTOMATIC1111_CFG_SCALE = (
-        float(form_data.automatic1111.AUTOMATIC1111_CFG_SCALE)
-        if form_data.automatic1111.AUTOMATIC1111_CFG_SCALE
-        else None
-    )
-    app.state.config.AUTOMATIC1111_SAMPLER = (
-        form_data.automatic1111.AUTOMATIC1111_SAMPLER
-        if form_data.automatic1111.AUTOMATIC1111_SAMPLER
-        else None
-    )
-    app.state.config.AUTOMATIC1111_SCHEDULER = (
-        form_data.automatic1111.AUTOMATIC1111_SCHEDULER
-        if form_data.automatic1111.AUTOMATIC1111_SCHEDULER
-        else None
-    )
-
-    app.state.config.COMFYUI_BASE_URL = form_data.comfyui.COMFYUI_BASE_URL.strip("/")
-    app.state.config.COMFYUI_WORKFLOW = form_data.comfyui.COMFYUI_WORKFLOW
-    app.state.config.COMFYUI_WORKFLOW_NODES = form_data.comfyui.COMFYUI_WORKFLOW_NODES
-
-    return {
-        "enabled": app.state.config.ENABLED,
-        "engine": app.state.config.ENGINE,
-        "openai": {
-            "OPENAI_API_BASE_URL": app.state.config.OPENAI_API_BASE_URL,
-            "OPENAI_API_KEY": app.state.config.OPENAI_API_KEY,
-        },
-        "automatic1111": {
-            "AUTOMATIC1111_BASE_URL": app.state.config.AUTOMATIC1111_BASE_URL,
-            "AUTOMATIC1111_API_AUTH": app.state.config.AUTOMATIC1111_API_AUTH,
-            "AUTOMATIC1111_CFG_SCALE": app.state.config.AUTOMATIC1111_CFG_SCALE,
-            "AUTOMATIC1111_SAMPLER": app.state.config.AUTOMATIC1111_SAMPLER,
-            "AUTOMATIC1111_SCHEDULER": app.state.config.AUTOMATIC1111_SCHEDULER,
-        },
-        "comfyui": {
-            "COMFYUI_BASE_URL": app.state.config.COMFYUI_BASE_URL,
-            "COMFYUI_WORKFLOW": app.state.config.COMFYUI_WORKFLOW,
-            "COMFYUI_WORKFLOW_NODES": app.state.config.COMFYUI_WORKFLOW_NODES,
-        },
-    }
-
-
-def get_automatic1111_api_auth():
-    if app.state.config.AUTOMATIC1111_API_AUTH is None:
-        return ""
-    else:
-        auth1111_byte_string = app.state.config.AUTOMATIC1111_API_AUTH.encode("utf-8")
-        auth1111_base64_encoded_bytes = base64.b64encode(auth1111_byte_string)
-        auth1111_base64_encoded_string = auth1111_base64_encoded_bytes.decode("utf-8")
-        return f"Basic {auth1111_base64_encoded_string}"
-
-
-@app.get("/config/url/verify")
-async def verify_url(user=Depends(get_admin_user)):
-    if app.state.config.ENGINE == "automatic1111":
-        try:
-            r = requests.get(
-                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
-                headers={"authorization": get_automatic1111_api_auth()},
-            )
-            r.raise_for_status()
-            return True
-        except Exception:
-            app.state.config.ENABLED = False
-            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.INVALID_URL)
-    elif app.state.config.ENGINE == "comfyui":
-        try:
-            r = requests.get(url=f"{app.state.config.COMFYUI_BASE_URL}/object_info")
-            r.raise_for_status()
-            return True
-        except Exception:
-            app.state.config.ENABLED = False
-            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.INVALID_URL)
-    else:
-        return True
-
-
-def set_image_model(model: str):
-    log.info(f"Setting image model to {model}")
-    app.state.config.MODEL = model
-    if app.state.config.ENGINE in ["", "automatic1111"]:
-        api_auth = get_automatic1111_api_auth()
-        r = requests.get(
-            url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
-            headers={"authorization": api_auth},
-        )
-        options = r.json()
-        if model != options["sd_model_checkpoint"]:
-            options["sd_model_checkpoint"] = model
-            r = requests.post(
-                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
-                json=options,
-                headers={"authorization": api_auth},
-            )
-    return app.state.config.MODEL
-
-
-def get_image_model():
-    if app.state.config.ENGINE == "openai":
-        return app.state.config.MODEL if app.state.config.MODEL else "dall-e-2"
-    elif app.state.config.ENGINE == "comfyui":
-        return app.state.config.MODEL if app.state.config.MODEL else ""
-    elif app.state.config.ENGINE == "automatic1111" or app.state.config.ENGINE == "":
-        try:
-            r = requests.get(
-                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
-                headers={"authorization": get_automatic1111_api_auth()},
-            )
-            options = r.json()
-            return options["sd_model_checkpoint"]
-        except Exception as e:
-            app.state.config.ENABLED = False
-            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
-
-
-class ImageConfigForm(BaseModel):
-    MODEL: str
-    IMAGE_SIZE: str
-    IMAGE_STEPS: int
-
-
-@app.get("/image/config")
-async def get_image_config(user=Depends(get_admin_user)):
-    return {
-        "MODEL": app.state.config.MODEL,
-        "IMAGE_SIZE": app.state.config.IMAGE_SIZE,
-        "IMAGE_STEPS": app.state.config.IMAGE_STEPS,
-    }
-
-
-@app.post("/image/config/update")
-async def update_image_config(form_data: ImageConfigForm, user=Depends(get_admin_user)):
-
-    set_image_model(form_data.MODEL)
-
-    pattern = r"^\d+x\d+$"
-    if re.match(pattern, form_data.IMAGE_SIZE):
-        app.state.config.IMAGE_SIZE = form_data.IMAGE_SIZE
-    else:
-        raise HTTPException(
-            status_code=400,
-            detail=ERROR_MESSAGES.INCORRECT_FORMAT("  (e.g., 512x512)."),
-        )
-
-    if form_data.IMAGE_STEPS >= 0:
-        app.state.config.IMAGE_STEPS = form_data.IMAGE_STEPS
-    else:
-        raise HTTPException(
-            status_code=400,
-            detail=ERROR_MESSAGES.INCORRECT_FORMAT("  (e.g., 50)."),
-        )
-
-    return {
-        "MODEL": app.state.config.MODEL,
-        "IMAGE_SIZE": app.state.config.IMAGE_SIZE,
-        "IMAGE_STEPS": app.state.config.IMAGE_STEPS,
-    }
-
-
-@app.get("/models")
-def get_models(user=Depends(get_verified_user)):
-    try:
-        if app.state.config.ENGINE == "openai":
-            return [
-                {"id": "dall-e-2", "name": "DALL·E 2"},
-                {"id": "dall-e-3", "name": "DALL·E 3"},
-            ]
-        elif app.state.config.ENGINE == "comfyui":
-            # TODO - get models from comfyui
-            r = requests.get(url=f"{app.state.config.COMFYUI_BASE_URL}/object_info")
-            info = r.json()
-
-            workflow = json.loads(app.state.config.COMFYUI_WORKFLOW)
-            model_node_id = None
-
-            for node in app.state.config.COMFYUI_WORKFLOW_NODES:
-                if node["type"] == "model":
-                    if node["node_ids"]:
-                        model_node_id = node["node_ids"][0]
-                    break
-
-            if model_node_id:
-                model_list_key = None
-
-                print(workflow[model_node_id]["class_type"])
-                for key in info[workflow[model_node_id]["class_type"]]["input"][
-                    "required"
-                ]:
-                    if "_name" in key:
-                        model_list_key = key
-                        break
-
-                if model_list_key:
-                    return list(
-                        map(
-                            lambda model: {"id": model, "name": model},
-                            info[workflow[model_node_id]["class_type"]]["input"][
-                                "required"
-                            ][model_list_key][0],
-                        )
-                    )
-            else:
-                return list(
-                    map(
-                        lambda model: {"id": model, "name": model},
-                        info["CheckpointLoaderSimple"]["input"]["required"][
-                            "ckpt_name"
-                        ][0],
-                    )
-                )
-        elif (
-            app.state.config.ENGINE == "automatic1111" or app.state.config.ENGINE == ""
-        ):
-            r = requests.get(
-                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/sd-models",
-                headers={"authorization": get_automatic1111_api_auth()},
-            )
-            models = r.json()
-            return list(
-                map(
-                    lambda model: {"id": model["title"], "name": model["model_name"]},
-                    models,
-                )
-            )
-    except Exception as e:
-        app.state.config.ENABLED = False
-        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
-
-
-class GenerateImageForm(BaseModel):
-    model: Optional[str] = None
-    prompt: str
-    size: Optional[str] = None
-    n: int = 1
-    negative_prompt: Optional[str] = None
-
-
-def save_b64_image(b64_str):
-    try:
-        image_id = str(uuid.uuid4())
-
-        if "," in b64_str:
-            header, encoded = b64_str.split(",", 1)
-            mime_type = header.split(";")[0]
-
-            img_data = base64.b64decode(encoded)
-            image_format = mimetypes.guess_extension(mime_type)
-
-            image_filename = f"{image_id}{image_format}"
-            file_path = IMAGE_CACHE_DIR / f"{image_filename}"
-            with open(file_path, "wb") as f:
-                f.write(img_data)
-            return image_filename
-        else:
-            image_filename = f"{image_id}.png"
-            file_path = IMAGE_CACHE_DIR.joinpath(image_filename)
-
-            img_data = base64.b64decode(b64_str)
-
-            # Write the image data to a file
-            with open(file_path, "wb") as f:
-                f.write(img_data)
-            return image_filename
-
-    except Exception as e:
-        log.exception(f"Error saving image: {e}")
-        return None
-
-
-def save_url_image(url):
-    image_id = str(uuid.uuid4())
-    try:
-        r = requests.get(url)
-        r.raise_for_status()
-        if r.headers["content-type"].split("/")[0] == "image":
-            mime_type = r.headers["content-type"]
-            image_format = mimetypes.guess_extension(mime_type)
-
-            if not image_format:
-                raise ValueError("Could not determine image type from MIME type")
-
-            image_filename = f"{image_id}{image_format}"
-
-            file_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}")
-            with open(file_path, "wb") as image_file:
-                for chunk in r.iter_content(chunk_size=8192):
-                    image_file.write(chunk)
-            return image_filename
-        else:
-            log.error("Url does not point to an image.")
-            return None
-
-    except Exception as e:
-        log.exception(f"Error saving image: {e}")
-        return None
-
-
-@app.post("/generations")
-async def image_generations(
-    form_data: GenerateImageForm,
-    user=Depends(get_verified_user),
-):
-    width, height = tuple(map(int, app.state.config.IMAGE_SIZE.split("x")))
-
-    r = None
-    try:
-        if app.state.config.ENGINE == "openai":
-            headers = {}
-            headers["Authorization"] = f"Bearer {app.state.config.OPENAI_API_KEY}"
-            headers["Content-Type"] = "application/json"
-
-            data = {
-                "model": (
-                    app.state.config.MODEL
-                    if app.state.config.MODEL != ""
-                    else "dall-e-2"
-                ),
-                "prompt": form_data.prompt,
-                "n": form_data.n,
-                "size": (
-                    form_data.size if form_data.size else app.state.config.IMAGE_SIZE
-                ),
-                "response_format": "b64_json",
-            }
-
-            # Use asyncio.to_thread for the requests.post call
-            r = await asyncio.to_thread(
-                requests.post,
-                url=f"{app.state.config.OPENAI_API_BASE_URL}/images/generations",
-                json=data,
-                headers=headers,
-            )
-
-            r.raise_for_status()
-            res = r.json()
-
-            images = []
-
-            for image in res["data"]:
-                image_filename = save_b64_image(image["b64_json"])
-                images.append({"url": f"/cache/image/generations/{image_filename}"})
-                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}.json")
-
-                with open(file_body_path, "w") as f:
-                    json.dump(data, f)
-
-            return images
-
-        elif app.state.config.ENGINE == "comfyui":
-            data = {
-                "prompt": form_data.prompt,
-                "width": width,
-                "height": height,
-                "n": form_data.n,
-            }
-
-            if app.state.config.IMAGE_STEPS is not None:
-                data["steps"] = app.state.config.IMAGE_STEPS
-
-            if form_data.negative_prompt is not None:
-                data["negative_prompt"] = form_data.negative_prompt
-
-            form_data = ComfyUIGenerateImageForm(
-                **{
-                    "workflow": ComfyUIWorkflow(
-                        **{
-                            "workflow": app.state.config.COMFYUI_WORKFLOW,
-                            "nodes": app.state.config.COMFYUI_WORKFLOW_NODES,
-                        }
-                    ),
-                    **data,
-                }
-            )
-            res = await comfyui_generate_image(
-                app.state.config.MODEL,
-                form_data,
-                user.id,
-                app.state.config.COMFYUI_BASE_URL,
-            )
-            log.debug(f"res: {res}")
-
-            images = []
-
-            for image in res["data"]:
-                image_filename = save_url_image(image["url"])
-                images.append({"url": f"/cache/image/generations/{image_filename}"})
-                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}.json")
-
-                with open(file_body_path, "w") as f:
-                    json.dump(form_data.model_dump(exclude_none=True), f)
-
-            log.debug(f"images: {images}")
-            return images
-        elif (
-            app.state.config.ENGINE == "automatic1111" or app.state.config.ENGINE == ""
-        ):
-            if form_data.model:
-                set_image_model(form_data.model)
-
-            data = {
-                "prompt": form_data.prompt,
-                "batch_size": form_data.n,
-                "width": width,
-                "height": height,
-            }
-
-            if app.state.config.IMAGE_STEPS is not None:
-                data["steps"] = app.state.config.IMAGE_STEPS
-
-            if form_data.negative_prompt is not None:
-                data["negative_prompt"] = form_data.negative_prompt
-
-            if app.state.config.AUTOMATIC1111_CFG_SCALE:
-                data["cfg_scale"] = app.state.config.AUTOMATIC1111_CFG_SCALE
-
-            if app.state.config.AUTOMATIC1111_SAMPLER:
-                data["sampler_name"] = app.state.config.AUTOMATIC1111_SAMPLER
-
-            if app.state.config.AUTOMATIC1111_SCHEDULER:
-                data["scheduler"] = app.state.config.AUTOMATIC1111_SCHEDULER
-
-            # Use asyncio.to_thread for the requests.post call
-            r = await asyncio.to_thread(
-                requests.post,
-                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/txt2img",
-                json=data,
-                headers={"authorization": get_automatic1111_api_auth()},
-            )
-
-            res = r.json()
-            log.debug(f"res: {res}")
-
-            images = []
-
-            for image in res["images"]:
-                image_filename = save_b64_image(image)
-                images.append({"url": f"/cache/image/generations/{image_filename}"})
-                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}.json")
-
-                with open(file_body_path, "w") as f:
-                    json.dump({**data, "info": res["info"]}, f)
-
-            return images
-    except Exception as e:
-        error = e
-        if r != None:
-            data = r.json()
-            if "error" in data:
-                error = data["error"]["message"]
-        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(error))
+import asyncio
+import base64
+import json
+import logging
+import mimetypes
+import re
+import uuid
+from pathlib import Path
+from typing import Optional
+
+import requests
+from open_webui.apps.images.utils.comfyui import (
+    ComfyUIGenerateImageForm,
+    ComfyUIWorkflow,
+    comfyui_generate_image,
+)
+from open_webui.config import (
+    AUTOMATIC1111_API_AUTH,
+    AUTOMATIC1111_BASE_URL,
+    AUTOMATIC1111_CFG_SCALE,
+    AUTOMATIC1111_SAMPLER,
+    AUTOMATIC1111_SCHEDULER,
+    CACHE_DIR,
+    COMFYUI_BASE_URL,
+    COMFYUI_WORKFLOW,
+    COMFYUI_WORKFLOW_NODES,
+    CORS_ALLOW_ORIGIN,
+    ENABLE_IMAGE_GENERATION,
+    IMAGE_GENERATION_ENGINE,
+    IMAGE_GENERATION_MODEL,
+    IMAGE_SIZE,
+    IMAGE_STEPS,
+    IMAGES_OPENAI_API_BASE_URL,
+    IMAGES_OPENAI_API_KEY,
+    AppConfig,
+)
+from open_webui.constants import ERROR_MESSAGES
+from open_webui.env import SRC_LOG_LEVELS
+from fastapi import Depends, FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from open_webui.utils.utils import get_admin_user, get_verified_user
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["IMAGES"])
+
+IMAGE_CACHE_DIR = Path(CACHE_DIR).joinpath("./image/generations/")
+IMAGE_CACHE_DIR.mkdir(parents=True, exist_ok=True)
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ALLOW_ORIGIN,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.state.config = AppConfig()
+
+app.state.config.ENGINE = IMAGE_GENERATION_ENGINE
+app.state.config.ENABLED = ENABLE_IMAGE_GENERATION
+
+app.state.config.OPENAI_API_BASE_URL = IMAGES_OPENAI_API_BASE_URL
+app.state.config.OPENAI_API_KEY = IMAGES_OPENAI_API_KEY
+
+app.state.config.MODEL = IMAGE_GENERATION_MODEL
+
+app.state.config.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
+app.state.config.AUTOMATIC1111_API_AUTH = AUTOMATIC1111_API_AUTH
+app.state.config.AUTOMATIC1111_CFG_SCALE = AUTOMATIC1111_CFG_SCALE
+app.state.config.AUTOMATIC1111_SAMPLER = AUTOMATIC1111_SAMPLER
+app.state.config.AUTOMATIC1111_SCHEDULER = AUTOMATIC1111_SCHEDULER
+app.state.config.COMFYUI_BASE_URL = COMFYUI_BASE_URL
+app.state.config.COMFYUI_WORKFLOW = COMFYUI_WORKFLOW
+app.state.config.COMFYUI_WORKFLOW_NODES = COMFYUI_WORKFLOW_NODES
+
+app.state.config.IMAGE_SIZE = IMAGE_SIZE
+app.state.config.IMAGE_STEPS = IMAGE_STEPS
+
+
+@app.get("/config")
+async def get_config(request: Request, user=Depends(get_admin_user)):
+    return {
+        "enabled": app.state.config.ENABLED,
+        "engine": app.state.config.ENGINE,
+        "openai": {
+            "OPENAI_API_BASE_URL": app.state.config.OPENAI_API_BASE_URL,
+            "OPENAI_API_KEY": app.state.config.OPENAI_API_KEY,
+        },
+        "automatic1111": {
+            "AUTOMATIC1111_BASE_URL": app.state.config.AUTOMATIC1111_BASE_URL,
+            "AUTOMATIC1111_API_AUTH": app.state.config.AUTOMATIC1111_API_AUTH,
+            "AUTOMATIC1111_CFG_SCALE": app.state.config.AUTOMATIC1111_CFG_SCALE,
+            "AUTOMATIC1111_SAMPLER": app.state.config.AUTOMATIC1111_SAMPLER,
+            "AUTOMATIC1111_SCHEDULER": app.state.config.AUTOMATIC1111_SCHEDULER,
+        },
+        "comfyui": {
+            "COMFYUI_BASE_URL": app.state.config.COMFYUI_BASE_URL,
+            "COMFYUI_WORKFLOW": app.state.config.COMFYUI_WORKFLOW,
+            "COMFYUI_WORKFLOW_NODES": app.state.config.COMFYUI_WORKFLOW_NODES,
+        },
+    }
+
+
+class OpenAIConfigForm(BaseModel):
+    OPENAI_API_BASE_URL: str
+    OPENAI_API_KEY: str
+
+
+class Automatic1111ConfigForm(BaseModel):
+    AUTOMATIC1111_BASE_URL: str
+    AUTOMATIC1111_API_AUTH: str
+    AUTOMATIC1111_CFG_SCALE: Optional[str]
+    AUTOMATIC1111_SAMPLER: Optional[str]
+    AUTOMATIC1111_SCHEDULER: Optional[str]
+
+
+class ComfyUIConfigForm(BaseModel):
+    COMFYUI_BASE_URL: str
+    COMFYUI_WORKFLOW: str
+    COMFYUI_WORKFLOW_NODES: list[dict]
+
+
+class ConfigForm(BaseModel):
+    enabled: bool
+    engine: str
+    openai: OpenAIConfigForm
+    automatic1111: Automatic1111ConfigForm
+    comfyui: ComfyUIConfigForm
+
+
+@app.post("/config/update")
+async def update_config(form_data: ConfigForm, user=Depends(get_admin_user)):
+    app.state.config.ENGINE = form_data.engine
+    app.state.config.ENABLED = form_data.enabled
+
+    app.state.config.OPENAI_API_BASE_URL = form_data.openai.OPENAI_API_BASE_URL
+    app.state.config.OPENAI_API_KEY = form_data.openai.OPENAI_API_KEY
+
+    app.state.config.AUTOMATIC1111_BASE_URL = (
+        form_data.automatic1111.AUTOMATIC1111_BASE_URL
+    )
+    app.state.config.AUTOMATIC1111_API_AUTH = (
+        form_data.automatic1111.AUTOMATIC1111_API_AUTH
+    )
+
+    app.state.config.AUTOMATIC1111_CFG_SCALE = (
+        float(form_data.automatic1111.AUTOMATIC1111_CFG_SCALE)
+        if form_data.automatic1111.AUTOMATIC1111_CFG_SCALE
+        else None
+    )
+    app.state.config.AUTOMATIC1111_SAMPLER = (
+        form_data.automatic1111.AUTOMATIC1111_SAMPLER
+        if form_data.automatic1111.AUTOMATIC1111_SAMPLER
+        else None
+    )
+    app.state.config.AUTOMATIC1111_SCHEDULER = (
+        form_data.automatic1111.AUTOMATIC1111_SCHEDULER
+        if form_data.automatic1111.AUTOMATIC1111_SCHEDULER
+        else None
+    )
+
+    app.state.config.COMFYUI_BASE_URL = form_data.comfyui.COMFYUI_BASE_URL.strip("/")
+    app.state.config.COMFYUI_WORKFLOW = form_data.comfyui.COMFYUI_WORKFLOW
+    app.state.config.COMFYUI_WORKFLOW_NODES = form_data.comfyui.COMFYUI_WORKFLOW_NODES
+
+    return {
+        "enabled": app.state.config.ENABLED,
+        "engine": app.state.config.ENGINE,
+        "openai": {
+            "OPENAI_API_BASE_URL": app.state.config.OPENAI_API_BASE_URL,
+            "OPENAI_API_KEY": app.state.config.OPENAI_API_KEY,
+        },
+        "automatic1111": {
+            "AUTOMATIC1111_BASE_URL": app.state.config.AUTOMATIC1111_BASE_URL,
+            "AUTOMATIC1111_API_AUTH": app.state.config.AUTOMATIC1111_API_AUTH,
+            "AUTOMATIC1111_CFG_SCALE": app.state.config.AUTOMATIC1111_CFG_SCALE,
+            "AUTOMATIC1111_SAMPLER": app.state.config.AUTOMATIC1111_SAMPLER,
+            "AUTOMATIC1111_SCHEDULER": app.state.config.AUTOMATIC1111_SCHEDULER,
+        },
+        "comfyui": {
+            "COMFYUI_BASE_URL": app.state.config.COMFYUI_BASE_URL,
+            "COMFYUI_WORKFLOW": app.state.config.COMFYUI_WORKFLOW,
+            "COMFYUI_WORKFLOW_NODES": app.state.config.COMFYUI_WORKFLOW_NODES,
+        },
+    }
+
+
+def get_automatic1111_api_auth():
+    if app.state.config.AUTOMATIC1111_API_AUTH is None:
+        return ""
+    else:
+        auth1111_byte_string = app.state.config.AUTOMATIC1111_API_AUTH.encode("utf-8")
+        auth1111_base64_encoded_bytes = base64.b64encode(auth1111_byte_string)
+        auth1111_base64_encoded_string = auth1111_base64_encoded_bytes.decode("utf-8")
+        return f"Basic {auth1111_base64_encoded_string}"
+
+
+@app.get("/config/url/verify")
+async def verify_url(user=Depends(get_admin_user)):
+    if app.state.config.ENGINE == "automatic1111":
+        try:
+            r = requests.get(
+                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
+                headers={"authorization": get_automatic1111_api_auth()},
+            )
+            r.raise_for_status()
+            return True
+        except Exception:
+            app.state.config.ENABLED = False
+            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.INVALID_URL)
+    elif app.state.config.ENGINE == "comfyui":
+        try:
+            r = requests.get(url=f"{app.state.config.COMFYUI_BASE_URL}/object_info")
+            r.raise_for_status()
+            return True
+        except Exception:
+            app.state.config.ENABLED = False
+            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.INVALID_URL)
+    else:
+        return True
+
+
+def set_image_model(model: str):
+    log.info(f"Setting image model to {model}")
+    app.state.config.MODEL = model
+    if app.state.config.ENGINE in ["", "automatic1111"]:
+        api_auth = get_automatic1111_api_auth()
+        r = requests.get(
+            url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
+            headers={"authorization": api_auth},
+        )
+        options = r.json()
+        if model != options["sd_model_checkpoint"]:
+            options["sd_model_checkpoint"] = model
+            r = requests.post(
+                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
+                json=options,
+                headers={"authorization": api_auth},
+            )
+    return app.state.config.MODEL
+
+
+def get_image_model():
+    if app.state.config.ENGINE == "openai":
+        return app.state.config.MODEL if app.state.config.MODEL else "dall-e-2"
+    elif app.state.config.ENGINE == "comfyui":
+        return app.state.config.MODEL if app.state.config.MODEL else ""
+    elif app.state.config.ENGINE == "automatic1111" or app.state.config.ENGINE == "":
+        try:
+            r = requests.get(
+                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/options",
+                headers={"authorization": get_automatic1111_api_auth()},
+            )
+            options = r.json()
+            return options["sd_model_checkpoint"]
+        except Exception as e:
+            app.state.config.ENABLED = False
+            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
+
+
+class ImageConfigForm(BaseModel):
+    MODEL: str
+    IMAGE_SIZE: str
+    IMAGE_STEPS: int
+
+
+@app.get("/image/config")
+async def get_image_config(user=Depends(get_admin_user)):
+    return {
+        "MODEL": app.state.config.MODEL,
+        "IMAGE_SIZE": app.state.config.IMAGE_SIZE,
+        "IMAGE_STEPS": app.state.config.IMAGE_STEPS,
+    }
+
+
+@app.post("/image/config/update")
+async def update_image_config(form_data: ImageConfigForm, user=Depends(get_admin_user)):
+
+    set_image_model(form_data.MODEL)
+
+    pattern = r"^\d+x\d+$"
+    if re.match(pattern, form_data.IMAGE_SIZE):
+        app.state.config.IMAGE_SIZE = form_data.IMAGE_SIZE
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail=ERROR_MESSAGES.INCORRECT_FORMAT("  (e.g., 512x512)."),
+        )
+
+    if form_data.IMAGE_STEPS >= 0:
+        app.state.config.IMAGE_STEPS = form_data.IMAGE_STEPS
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail=ERROR_MESSAGES.INCORRECT_FORMAT("  (e.g., 50)."),
+        )
+
+    return {
+        "MODEL": app.state.config.MODEL,
+        "IMAGE_SIZE": app.state.config.IMAGE_SIZE,
+        "IMAGE_STEPS": app.state.config.IMAGE_STEPS,
+    }
+
+
+@app.get("/models")
+def get_models(user=Depends(get_verified_user)):
+    try:
+        if app.state.config.ENGINE == "openai":
+            return [
+                {"id": "dall-e-2", "name": "DALL·E 2"},
+                {"id": "dall-e-3", "name": "DALL·E 3"},
+            ]
+        elif app.state.config.ENGINE == "comfyui":
+            # TODO - get models from comfyui
+            r = requests.get(url=f"{app.state.config.COMFYUI_BASE_URL}/object_info")
+            info = r.json()
+
+            workflow = json.loads(app.state.config.COMFYUI_WORKFLOW)
+            model_node_id = None
+
+            for node in app.state.config.COMFYUI_WORKFLOW_NODES:
+                if node["type"] == "model":
+                    if node["node_ids"]:
+                        model_node_id = node["node_ids"][0]
+                    break
+
+            if model_node_id:
+                model_list_key = None
+
+                print(workflow[model_node_id]["class_type"])
+                for key in info[workflow[model_node_id]["class_type"]]["input"][
+                    "required"
+                ]:
+                    if "_name" in key:
+                        model_list_key = key
+                        break
+
+                if model_list_key:
+                    return list(
+                        map(
+                            lambda model: {"id": model, "name": model},
+                            info[workflow[model_node_id]["class_type"]]["input"][
+                                "required"
+                            ][model_list_key][0],
+                        )
+                    )
+            else:
+                return list(
+                    map(
+                        lambda model: {"id": model, "name": model},
+                        info["CheckpointLoaderSimple"]["input"]["required"][
+                            "ckpt_name"
+                        ][0],
+                    )
+                )
+        elif (
+            app.state.config.ENGINE == "automatic1111" or app.state.config.ENGINE == ""
+        ):
+            r = requests.get(
+                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/sd-models",
+                headers={"authorization": get_automatic1111_api_auth()},
+            )
+            models = r.json()
+            return list(
+                map(
+                    lambda model: {"id": model["title"], "name": model["model_name"]},
+                    models,
+                )
+            )
+    except Exception as e:
+        app.state.config.ENABLED = False
+        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
+
+
+class GenerateImageForm(BaseModel):
+    model: Optional[str] = None
+    prompt: str
+    size: Optional[str] = None
+    n: int = 1
+    negative_prompt: Optional[str] = None
+
+
+def save_b64_image(b64_str):
+    try:
+        image_id = str(uuid.uuid4())
+
+        if "," in b64_str:
+            header, encoded = b64_str.split(",", 1)
+            mime_type = header.split(";")[0]
+
+            img_data = base64.b64decode(encoded)
+            image_format = mimetypes.guess_extension(mime_type)
+
+            image_filename = f"{image_id}{image_format}"
+            file_path = IMAGE_CACHE_DIR / f"{image_filename}"
+            with open(file_path, "wb") as f:
+                f.write(img_data)
+            return image_filename
+        else:
+            image_filename = f"{image_id}.png"
+            file_path = IMAGE_CACHE_DIR.joinpath(image_filename)
+
+            img_data = base64.b64decode(b64_str)
+
+            # Write the image data to a file
+            with open(file_path, "wb") as f:
+                f.write(img_data)
+            return image_filename
+
+    except Exception as e:
+        log.exception(f"Error saving image: {e}")
+        return None
+
+
+def save_url_image(url):
+    image_id = str(uuid.uuid4())
+    try:
+        r = requests.get(url)
+        r.raise_for_status()
+        if r.headers["content-type"].split("/")[0] == "image":
+            mime_type = r.headers["content-type"]
+            image_format = mimetypes.guess_extension(mime_type)
+
+            if not image_format:
+                raise ValueError("Could not determine image type from MIME type")
+
+            image_filename = f"{image_id}{image_format}"
+
+            file_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}")
+            with open(file_path, "wb") as image_file:
+                for chunk in r.iter_content(chunk_size=8192):
+                    image_file.write(chunk)
+            return image_filename
+        else:
+            log.error("Url does not point to an image.")
+            return None
+
+    except Exception as e:
+        log.exception(f"Error saving image: {e}")
+        return None
+
+
+@app.post("/generations")
+async def image_generations(
+    form_data: GenerateImageForm,
+    user=Depends(get_verified_user),
+):
+    width, height = tuple(map(int, app.state.config.IMAGE_SIZE.split("x")))
+
+    r = None
+    try:
+        if app.state.config.ENGINE == "openai":
+            headers = {}
+            headers["Authorization"] = f"Bearer {app.state.config.OPENAI_API_KEY}"
+            headers["Content-Type"] = "application/json"
+
+            data = {
+                "model": (
+                    app.state.config.MODEL
+                    if app.state.config.MODEL != ""
+                    else "dall-e-2"
+                ),
+                "prompt": form_data.prompt,
+                "n": form_data.n,
+                "size": (
+                    form_data.size if form_data.size else app.state.config.IMAGE_SIZE
+                ),
+                "response_format": "b64_json",
+            }
+
+            # Use asyncio.to_thread for the requests.post call
+            r = await asyncio.to_thread(
+                requests.post,
+                url=f"{app.state.config.OPENAI_API_BASE_URL}/images/generations",
+                json=data,
+                headers=headers,
+            )
+
+            r.raise_for_status()
+            res = r.json()
+
+            images = []
+
+            for image in res["data"]:
+                image_filename = save_b64_image(image["b64_json"])
+                images.append({"url": f"/cache/image/generations/{image_filename}"})
+                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}.json")
+
+                with open(file_body_path, "w") as f:
+                    json.dump(data, f)
+
+            return images
+
+        elif app.state.config.ENGINE == "comfyui":
+            data = {
+                "prompt": form_data.prompt,
+                "width": width,
+                "height": height,
+                "n": form_data.n,
+            }
+
+            if app.state.config.IMAGE_STEPS is not None:
+                data["steps"] = app.state.config.IMAGE_STEPS
+
+            if form_data.negative_prompt is not None:
+                data["negative_prompt"] = form_data.negative_prompt
+
+            form_data = ComfyUIGenerateImageForm(
+                **{
+                    "workflow": ComfyUIWorkflow(
+                        **{
+                            "workflow": app.state.config.COMFYUI_WORKFLOW,
+                            "nodes": app.state.config.COMFYUI_WORKFLOW_NODES,
+                        }
+                    ),
+                    **data,
+                }
+            )
+            res = await comfyui_generate_image(
+                app.state.config.MODEL,
+                form_data,
+                user.id,
+                app.state.config.COMFYUI_BASE_URL,
+            )
+            log.debug(f"res: {res}")
+
+            images = []
+
+            for image in res["data"]:
+                image_filename = save_url_image(image["url"])
+                images.append({"url": f"/cache/image/generations/{image_filename}"})
+                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}.json")
+
+                with open(file_body_path, "w") as f:
+                    json.dump(form_data.model_dump(exclude_none=True), f)
+
+            log.debug(f"images: {images}")
+            return images
+        elif (
+            app.state.config.ENGINE == "automatic1111" or app.state.config.ENGINE == ""
+        ):
+            if form_data.model:
+                set_image_model(form_data.model)
+
+            data = {
+                "prompt": form_data.prompt,
+                "batch_size": form_data.n,
+                "width": width,
+                "height": height,
+            }
+
+            if app.state.config.IMAGE_STEPS is not None:
+                data["steps"] = app.state.config.IMAGE_STEPS
+
+            if form_data.negative_prompt is not None:
+                data["negative_prompt"] = form_data.negative_prompt
+
+            if app.state.config.AUTOMATIC1111_CFG_SCALE:
+                data["cfg_scale"] = app.state.config.AUTOMATIC1111_CFG_SCALE
+
+            if app.state.config.AUTOMATIC1111_SAMPLER:
+                data["sampler_name"] = app.state.config.AUTOMATIC1111_SAMPLER
+
+            if app.state.config.AUTOMATIC1111_SCHEDULER:
+                data["scheduler"] = app.state.config.AUTOMATIC1111_SCHEDULER
+
+            # Use asyncio.to_thread for the requests.post call
+            r = await asyncio.to_thread(
+                requests.post,
+                url=f"{app.state.config.AUTOMATIC1111_BASE_URL}/sdapi/v1/txt2img",
+                json=data,
+                headers={"authorization": get_automatic1111_api_auth()},
+            )
+
+            res = r.json()
+            log.debug(f"res: {res}")
+
+            images = []
+
+            for image in res["images"]:
+                image_filename = save_b64_image(image)
+                images.append({"url": f"/cache/image/generations/{image_filename}"})
+                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_filename}.json")
+
+                with open(file_body_path, "w") as f:
+                    json.dump({**data, "info": res["info"]}, f)
+
+            return images
+    except Exception as e:
+        error = e
+        if r != None:
+            data = r.json()
+            if "error" in data:
+                error = data["error"]["message"]
+        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(error))

backend/open_webui/apps/images/utils/comfyui.py

@@ -1,186 +1,186 @@
-import asyncio
-import json
-import logging
-import random
-import urllib.parse
-import urllib.request
-from typing import Optional
-
-import websocket  # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
-from open_webui.env import SRC_LOG_LEVELS
-from pydantic import BaseModel
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["COMFYUI"])
-
-default_headers = {"User-Agent": "Mozilla/5.0"}
-
-
-def queue_prompt(prompt, client_id, base_url):
-    log.info("queue_prompt")
-    p = {"prompt": prompt, "client_id": client_id}
-    data = json.dumps(p).encode("utf-8")
-    log.debug(f"queue_prompt data: {data}")
-    try:
-        req = urllib.request.Request(
-            f"{base_url}/prompt", data=data, headers=default_headers
-        )
-        response = urllib.request.urlopen(req).read()
-        return json.loads(response)
-    except Exception as e:
-        log.exception(f"Error while queuing prompt: {e}")
-        raise e
-
-
-def get_image(filename, subfolder, folder_type, base_url):
-    log.info("get_image")
-    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
-    url_values = urllib.parse.urlencode(data)
-    req = urllib.request.Request(
-        f"{base_url}/view?{url_values}", headers=default_headers
-    )
-    with urllib.request.urlopen(req) as response:
-        return response.read()
-
-
-def get_image_url(filename, subfolder, folder_type, base_url):
-    log.info("get_image")
-    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
-    url_values = urllib.parse.urlencode(data)
-    return f"{base_url}/view?{url_values}"
-
-
-def get_history(prompt_id, base_url):
-    log.info("get_history")
-
-    req = urllib.request.Request(
-        f"{base_url}/history/{prompt_id}", headers=default_headers
-    )
-    with urllib.request.urlopen(req) as response:
-        return json.loads(response.read())
-
-
-def get_images(ws, prompt, client_id, base_url):
-    prompt_id = queue_prompt(prompt, client_id, base_url)["prompt_id"]
-    output_images = []
-    while True:
-        out = ws.recv()
-        if isinstance(out, str):
-            message = json.loads(out)
-            if message["type"] == "executing":
-                data = message["data"]
-                if data["node"] is None and data["prompt_id"] == prompt_id:
-                    break  # Execution is done
-        else:
-            continue  # previews are binary data
-
-    history = get_history(prompt_id, base_url)[prompt_id]
-    for o in history["outputs"]:
-        for node_id in history["outputs"]:
-            node_output = history["outputs"][node_id]
-            if "images" in node_output:
-                for image in node_output["images"]:
-                    url = get_image_url(
-                        image["filename"], image["subfolder"], image["type"], base_url
-                    )
-                    output_images.append({"url": url})
-    return {"data": output_images}
-
-
-class ComfyUINodeInput(BaseModel):
-    type: Optional[str] = None
-    node_ids: list[str] = []
-    key: Optional[str] = "text"
-    value: Optional[str] = None
-
-
-class ComfyUIWorkflow(BaseModel):
-    workflow: str
-    nodes: list[ComfyUINodeInput]
-
-
-class ComfyUIGenerateImageForm(BaseModel):
-    workflow: ComfyUIWorkflow
-
-    prompt: str
-    negative_prompt: Optional[str] = None
-    width: int
-    height: int
-    n: int = 1
-
-    steps: Optional[int] = None
-    seed: Optional[int] = None
-
-
-async def comfyui_generate_image(
-    model: str, payload: ComfyUIGenerateImageForm, client_id, base_url
-):
-    ws_url = base_url.replace("http://", "ws://").replace("https://", "wss://")
-    workflow = json.loads(payload.workflow.workflow)
-
-    for node in payload.workflow.nodes:
-        if node.type:
-            if node.type == "model":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][node.key] = model
-            elif node.type == "prompt":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][
-                        node.key if node.key else "text"
-                    ] = payload.prompt
-            elif node.type == "negative_prompt":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][
-                        node.key if node.key else "text"
-                    ] = payload.negative_prompt
-            elif node.type == "width":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][
-                        node.key if node.key else "width"
-                    ] = payload.width
-            elif node.type == "height":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][
-                        node.key if node.key else "height"
-                    ] = payload.height
-            elif node.type == "n":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][
-                        node.key if node.key else "batch_size"
-                    ] = payload.n
-            elif node.type == "steps":
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][
-                        node.key if node.key else "steps"
-                    ] = payload.steps
-            elif node.type == "seed":
-                seed = (
-                    payload.seed
-                    if payload.seed
-                    else random.randint(0, 18446744073709551614)
-                )
-                for node_id in node.node_ids:
-                    workflow[node_id]["inputs"][node.key] = seed
-        else:
-            for node_id in node.node_ids:
-                workflow[node_id]["inputs"][node.key] = node.value
-
-    try:
-        ws = websocket.WebSocket()
-        ws.connect(f"{ws_url}/ws?clientId={client_id}")
-        log.info("WebSocket connection established.")
-    except Exception as e:
-        log.exception(f"Failed to connect to WebSocket server: {e}")
-        return None
-
-    try:
-        log.info("Sending workflow to WebSocket server.")
-        log.info(f"Workflow: {workflow}")
-        images = await asyncio.to_thread(get_images, ws, workflow, client_id, base_url)
-    except Exception as e:
-        log.exception(f"Error while receiving images: {e}")
-        images = None
-
-    ws.close()
-
-    return images
+import asyncio
+import json
+import logging
+import random
+import urllib.parse
+import urllib.request
+from typing import Optional
+
+import websocket  # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+from open_webui.env import SRC_LOG_LEVELS
+from pydantic import BaseModel
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["COMFYUI"])
+
+default_headers = {"User-Agent": "Mozilla/5.0"}
+
+
+def queue_prompt(prompt, client_id, base_url):
+    log.info("queue_prompt")
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode("utf-8")
+    log.debug(f"queue_prompt data: {data}")
+    try:
+        req = urllib.request.Request(
+            f"{base_url}/prompt", data=data, headers=default_headers
+        )
+        response = urllib.request.urlopen(req).read()
+        return json.loads(response)
+    except Exception as e:
+        log.exception(f"Error while queuing prompt: {e}")
+        raise e
+
+
+def get_image(filename, subfolder, folder_type, base_url):
+    log.info("get_image")
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    req = urllib.request.Request(
+        f"{base_url}/view?{url_values}", headers=default_headers
+    )
+    with urllib.request.urlopen(req) as response:
+        return response.read()
+
+
+def get_image_url(filename, subfolder, folder_type, base_url):
+    log.info("get_image")
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    return f"{base_url}/view?{url_values}"
+
+
+def get_history(prompt_id, base_url):
+    log.info("get_history")
+
+    req = urllib.request.Request(
+        f"{base_url}/history/{prompt_id}", headers=default_headers
+    )
+    with urllib.request.urlopen(req) as response:
+        return json.loads(response.read())
+
+
+def get_images(ws, prompt, client_id, base_url):
+    prompt_id = queue_prompt(prompt, client_id, base_url)["prompt_id"]
+    output_images = []
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message["type"] == "executing":
+                data = message["data"]
+                if data["node"] is None and data["prompt_id"] == prompt_id:
+                    break  # Execution is done
+        else:
+            continue  # previews are binary data
+
+    history = get_history(prompt_id, base_url)[prompt_id]
+    for o in history["outputs"]:
+        for node_id in history["outputs"]:
+            node_output = history["outputs"][node_id]
+            if "images" in node_output:
+                for image in node_output["images"]:
+                    url = get_image_url(
+                        image["filename"], image["subfolder"], image["type"], base_url
+                    )
+                    output_images.append({"url": url})
+    return {"data": output_images}
+
+
+class ComfyUINodeInput(BaseModel):
+    type: Optional[str] = None
+    node_ids: list[str] = []
+    key: Optional[str] = "text"
+    value: Optional[str] = None
+
+
+class ComfyUIWorkflow(BaseModel):
+    workflow: str
+    nodes: list[ComfyUINodeInput]
+
+
+class ComfyUIGenerateImageForm(BaseModel):
+    workflow: ComfyUIWorkflow
+
+    prompt: str
+    negative_prompt: Optional[str] = None
+    width: int
+    height: int
+    n: int = 1
+
+    steps: Optional[int] = None
+    seed: Optional[int] = None
+
+
+async def comfyui_generate_image(
+    model: str, payload: ComfyUIGenerateImageForm, client_id, base_url
+):
+    ws_url = base_url.replace("http://", "ws://").replace("https://", "wss://")
+    workflow = json.loads(payload.workflow.workflow)
+
+    for node in payload.workflow.nodes:
+        if node.type:
+            if node.type == "model":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][node.key] = model
+            elif node.type == "prompt":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][
+                        node.key if node.key else "text"
+                    ] = payload.prompt
+            elif node.type == "negative_prompt":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][
+                        node.key if node.key else "text"
+                    ] = payload.negative_prompt
+            elif node.type == "width":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][
+                        node.key if node.key else "width"
+                    ] = payload.width
+            elif node.type == "height":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][
+                        node.key if node.key else "height"
+                    ] = payload.height
+            elif node.type == "n":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][
+                        node.key if node.key else "batch_size"
+                    ] = payload.n
+            elif node.type == "steps":
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][
+                        node.key if node.key else "steps"
+                    ] = payload.steps
+            elif node.type == "seed":
+                seed = (
+                    payload.seed
+                    if payload.seed
+                    else random.randint(0, 18446744073709551614)
+                )
+                for node_id in node.node_ids:
+                    workflow[node_id]["inputs"][node.key] = seed
+        else:
+            for node_id in node.node_ids:
+                workflow[node_id]["inputs"][node.key] = node.value
+
+    try:
+        ws = websocket.WebSocket()
+        ws.connect(f"{ws_url}/ws?clientId={client_id}")
+        log.info("WebSocket connection established.")
+    except Exception as e:
+        log.exception(f"Failed to connect to WebSocket server: {e}")
+        return None
+
+    try:
+        log.info("Sending workflow to WebSocket server.")
+        log.info(f"Workflow: {workflow}")
+        images = await asyncio.to_thread(get_images, ws, workflow, client_id, base_url)
+    except Exception as e:
+        log.exception(f"Error while receiving images: {e}")
+        images = None
+
+    ws.close()
+
+    return images

backend/open_webui/apps/ollama/main.py

@@ -1,1120 +1,1121 @@
-import asyncio
-import json
-import logging
-import os
-import random
-import re
-import time
-from typing import Optional, Union
-from urllib.parse import urlparse
-
-import aiohttp
-import requests
-from open_webui.apps.webui.models.models import Models
-from open_webui.config import (
-    CORS_ALLOW_ORIGIN,
-    ENABLE_MODEL_FILTER,
-    ENABLE_OLLAMA_API,
-    MODEL_FILTER_LIST,
-    OLLAMA_BASE_URLS,
-    UPLOAD_DIR,
-    AppConfig,
-)
-from open_webui.env import AIOHTTP_CLIENT_TIMEOUT
-
-
-from open_webui.constants import ERROR_MESSAGES
-from open_webui.env import SRC_LOG_LEVELS
-from fastapi import Depends, FastAPI, File, HTTPException, Request, UploadFile
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel, ConfigDict
-from starlette.background import BackgroundTask
-
-
-from open_webui.utils.misc import (
-    calculate_sha256,
-)
-from open_webui.utils.payload import (
-    apply_model_params_to_body_ollama,
-    apply_model_params_to_body_openai,
-    apply_model_system_prompt_to_body,
-)
-from open_webui.utils.utils import get_admin_user, get_verified_user
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["OLLAMA"])
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=CORS_ALLOW_ORIGIN,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.state.config = AppConfig()
-
-app.state.config.ENABLE_MODEL_FILTER = ENABLE_MODEL_FILTER
-app.state.config.MODEL_FILTER_LIST = MODEL_FILTER_LIST
-
-app.state.config.ENABLE_OLLAMA_API = ENABLE_OLLAMA_API
-app.state.config.OLLAMA_BASE_URLS = OLLAMA_BASE_URLS
-app.state.MODELS = {}
-
-
-# TODO: Implement a more intelligent load balancing mechanism for distributing requests among multiple backend instances.
-# Current implementation uses a simple round-robin approach (random.choice). Consider incorporating algorithms like weighted round-robin,
-# least connections, or least response time for better resource utilization and performance optimization.
-
-
-@app.middleware("http")
-async def check_url(request: Request, call_next):
-    if len(app.state.MODELS) == 0:
-        await get_all_models()
-    else:
-        pass
-
-    response = await call_next(request)
-    return response
-
-
-@app.head("/")
-@app.get("/")
-async def get_status():
-    return {"status": True}
-
-
-@app.get("/config")
-async def get_config(user=Depends(get_admin_user)):
-    return {"ENABLE_OLLAMA_API": app.state.config.ENABLE_OLLAMA_API}
-
-
-class OllamaConfigForm(BaseModel):
-    enable_ollama_api: Optional[bool] = None
-
-
-@app.post("/config/update")
-async def update_config(form_data: OllamaConfigForm, user=Depends(get_admin_user)):
-    app.state.config.ENABLE_OLLAMA_API = form_data.enable_ollama_api
-    return {"ENABLE_OLLAMA_API": app.state.config.ENABLE_OLLAMA_API}
-
-
-@app.get("/urls")
-async def get_ollama_api_urls(user=Depends(get_admin_user)):
-    return {"OLLAMA_BASE_URLS": app.state.config.OLLAMA_BASE_URLS}
-
-
-class UrlUpdateForm(BaseModel):
-    urls: list[str]
-
-
-@app.post("/urls/update")
-async def update_ollama_api_url(form_data: UrlUpdateForm, user=Depends(get_admin_user)):
-    app.state.config.OLLAMA_BASE_URLS = form_data.urls
-
-    log.info(f"app.state.config.OLLAMA_BASE_URLS: {app.state.config.OLLAMA_BASE_URLS}")
-    return {"OLLAMA_BASE_URLS": app.state.config.OLLAMA_BASE_URLS}
-
-
-async def fetch_url(url):
-    timeout = aiohttp.ClientTimeout(total=3)
-    try:
-        async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
-            async with session.get(url) as response:
-                return await response.json()
-    except Exception as e:
-        # Handle connection error here
-        log.error(f"Connection error: {e}")
-        return None
-
-
-async def cleanup_response(
-    response: Optional[aiohttp.ClientResponse],
-    session: Optional[aiohttp.ClientSession],
-):
-    if response:
-        response.close()
-    if session:
-        await session.close()
-
-
-async def post_streaming_url(
-    url: str, payload: Union[str, bytes], stream: bool = True, content_type=None
-):
-    r = None
-    try:
-        session = aiohttp.ClientSession(
-            trust_env=True, timeout=aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
-        )
-        r = await session.post(
-            url,
-            data=payload,
-            headers={"Content-Type": "application/json"},
-        )
-        r.raise_for_status()
-
-        if stream:
-            headers = dict(r.headers)
-            if content_type:
-                headers["Content-Type"] = content_type
-            return StreamingResponse(
-                r.content,
-                status_code=r.status,
-                headers=headers,
-                background=BackgroundTask(
-                    cleanup_response, response=r, session=session
-                ),
-            )
-        else:
-            res = await r.json()
-            await cleanup_response(r, session)
-            return res
-
-    except Exception as e:
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = await r.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except Exception:
-                error_detail = f"Ollama: {e}"
-
-        raise HTTPException(
-            status_code=r.status if r else 500,
-            detail=error_detail,
-        )
-
-
-def merge_models_lists(model_lists):
-    merged_models = {}
-
-    for idx, model_list in enumerate(model_lists):
-        if model_list is not None:
-            for model in model_list:
-                digest = model["digest"]
-                if digest not in merged_models:
-                    model["urls"] = [idx]
-                    merged_models[digest] = model
-                else:
-                    merged_models[digest]["urls"].append(idx)
-
-    return list(merged_models.values())
-
-
-async def get_all_models():
-    log.info("get_all_models()")
-
-    if app.state.config.ENABLE_OLLAMA_API:
-        tasks = [
-            fetch_url(f"{url}/api/tags") for url in app.state.config.OLLAMA_BASE_URLS
-        ]
-        responses = await asyncio.gather(*tasks)
-
-        models = {
-            "models": merge_models_lists(
-                map(
-                    lambda response: response["models"] if response else None, responses
-                )
-            )
-        }
-
-    else:
-        models = {"models": []}
-
-    app.state.MODELS = {model["model"]: model for model in models["models"]}
-
-    return models
-
-
-@app.get("/api/tags")
-@app.get("/api/tags/{url_idx}")
-async def get_ollama_tags(
-    url_idx: Optional[int] = None, user=Depends(get_verified_user)
-):
-    if url_idx is None:
-        models = await get_all_models()
-
-        if app.state.config.ENABLE_MODEL_FILTER:
-            if user.role == "user":
-                models["models"] = list(
-                    filter(
-                        lambda model: model["name"]
-                        in app.state.config.MODEL_FILTER_LIST,
-                        models["models"],
-                    )
-                )
-                return models
-        return models
-    else:
-        url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-
-        r = None
-        try:
-            r = requests.request(method="GET", url=f"{url}/api/tags")
-            r.raise_for_status()
-
-            return r.json()
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"Ollama: {res['error']}"
-                except Exception:
-                    error_detail = f"Ollama: {e}"
-
-            raise HTTPException(
-                status_code=r.status_code if r else 500,
-                detail=error_detail,
-            )
-
-
-@app.get("/api/version")
-@app.get("/api/version/{url_idx}")
-async def get_ollama_versions(url_idx: Optional[int] = None):
-    if app.state.config.ENABLE_OLLAMA_API:
-        if url_idx is None:
-            # returns lowest version
-            tasks = [
-                fetch_url(f"{url}/api/version")
-                for url in app.state.config.OLLAMA_BASE_URLS
-            ]
-            responses = await asyncio.gather(*tasks)
-            responses = list(filter(lambda x: x is not None, responses))
-
-            if len(responses) > 0:
-                lowest_version = min(
-                    responses,
-                    key=lambda x: tuple(
-                        map(int, re.sub(r"^v|-.*", "", x["version"]).split("."))
-                    ),
-                )
-
-                return {"version": lowest_version["version"]}
-            else:
-                raise HTTPException(
-                    status_code=500,
-                    detail=ERROR_MESSAGES.OLLAMA_NOT_FOUND,
-                )
-        else:
-            url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-
-            r = None
-            try:
-                r = requests.request(method="GET", url=f"{url}/api/version")
-                r.raise_for_status()
-
-                return r.json()
-            except Exception as e:
-                log.exception(e)
-                error_detail = "Open WebUI: Server Connection Error"
-                if r is not None:
-                    try:
-                        res = r.json()
-                        if "error" in res:
-                            error_detail = f"Ollama: {res['error']}"
-                    except Exception:
-                        error_detail = f"Ollama: {e}"
-
-                raise HTTPException(
-                    status_code=r.status_code if r else 500,
-                    detail=error_detail,
-                )
-    else:
-        return {"version": False}
-
-
-class ModelNameForm(BaseModel):
-    name: str
-
-
-@app.post("/api/pull")
-@app.post("/api/pull/{url_idx}")
-async def pull_model(
-    form_data: ModelNameForm, url_idx: int = 0, user=Depends(get_admin_user)
-):
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    # Admin should be able to pull models from any source
-    payload = {**form_data.model_dump(exclude_none=True), "insecure": True}
-
-    return await post_streaming_url(f"{url}/api/pull", json.dumps(payload))
-
-
-class PushModelForm(BaseModel):
-    name: str
-    insecure: Optional[bool] = None
-    stream: Optional[bool] = None
-
-
-@app.delete("/api/push")
-@app.delete("/api/push/{url_idx}")
-async def push_model(
-    form_data: PushModelForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_admin_user),
-):
-    if url_idx is None:
-        if form_data.name in app.state.MODELS:
-            url_idx = app.state.MODELS[form_data.name]["urls"][0]
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.name),
-            )
-
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.debug(f"url: {url}")
-
-    return await post_streaming_url(
-        f"{url}/api/push", form_data.model_dump_json(exclude_none=True).encode()
-    )
-
-
-class CreateModelForm(BaseModel):
-    name: str
-    modelfile: Optional[str] = None
-    stream: Optional[bool] = None
-    path: Optional[str] = None
-
-
-@app.post("/api/create")
-@app.post("/api/create/{url_idx}")
-async def create_model(
-    form_data: CreateModelForm, url_idx: int = 0, user=Depends(get_admin_user)
-):
-    log.debug(f"form_data: {form_data}")
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    return await post_streaming_url(
-        f"{url}/api/create", form_data.model_dump_json(exclude_none=True).encode()
-    )
-
-
-class CopyModelForm(BaseModel):
-    source: str
-    destination: str
-
-
-@app.post("/api/copy")
-@app.post("/api/copy/{url_idx}")
-async def copy_model(
-    form_data: CopyModelForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_admin_user),
-):
-    if url_idx is None:
-        if form_data.source in app.state.MODELS:
-            url_idx = app.state.MODELS[form_data.source]["urls"][0]
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.source),
-            )
-
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-    r = requests.request(
-        method="POST",
-        url=f"{url}/api/copy",
-        headers={"Content-Type": "application/json"},
-        data=form_data.model_dump_json(exclude_none=True).encode(),
-    )
-
-    try:
-        r.raise_for_status()
-
-        log.debug(f"r.text: {r.text}")
-
-        return True
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = r.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except Exception:
-                error_detail = f"Ollama: {e}"
-
-        raise HTTPException(
-            status_code=r.status_code if r else 500,
-            detail=error_detail,
-        )
-
-
-@app.delete("/api/delete")
-@app.delete("/api/delete/{url_idx}")
-async def delete_model(
-    form_data: ModelNameForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_admin_user),
-):
-    if url_idx is None:
-        if form_data.name in app.state.MODELS:
-            url_idx = app.state.MODELS[form_data.name]["urls"][0]
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.name),
-            )
-
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    r = requests.request(
-        method="DELETE",
-        url=f"{url}/api/delete",
-        headers={"Content-Type": "application/json"},
-        data=form_data.model_dump_json(exclude_none=True).encode(),
-    )
-    try:
-        r.raise_for_status()
-
-        log.debug(f"r.text: {r.text}")
-
-        return True
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = r.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except Exception:
-                error_detail = f"Ollama: {e}"
-
-        raise HTTPException(
-            status_code=r.status_code if r else 500,
-            detail=error_detail,
-        )
-
-
-@app.post("/api/show")
-async def show_model_info(form_data: ModelNameForm, user=Depends(get_verified_user)):
-    if form_data.name not in app.state.MODELS:
-        raise HTTPException(
-            status_code=400,
-            detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.name),
-        )
-
-    url_idx = random.choice(app.state.MODELS[form_data.name]["urls"])
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    r = requests.request(
-        method="POST",
-        url=f"{url}/api/show",
-        headers={"Content-Type": "application/json"},
-        data=form_data.model_dump_json(exclude_none=True).encode(),
-    )
-    try:
-        r.raise_for_status()
-
-        return r.json()
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = r.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except Exception:
-                error_detail = f"Ollama: {e}"
-
-        raise HTTPException(
-            status_code=r.status_code if r else 500,
-            detail=error_detail,
-        )
-
-
-class GenerateEmbeddingsForm(BaseModel):
-    model: str
-    prompt: str
-    options: Optional[dict] = None
-    keep_alive: Optional[Union[int, str]] = None
-
-
-class GenerateEmbedForm(BaseModel):
-    model: str
-    input: list[str] | str
-    truncate: Optional[bool] = None
-    options: Optional[dict] = None
-    keep_alive: Optional[Union[int, str]] = None
-
-
-@app.post("/api/embed")
-@app.post("/api/embed/{url_idx}")
-async def generate_embeddings(
-    form_data: GenerateEmbedForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    return generate_ollama_batch_embeddings(form_data, url_idx)
-
-
-@app.post("/api/embeddings")
-@app.post("/api/embeddings/{url_idx}")
-async def generate_embeddings(
-    form_data: GenerateEmbeddingsForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    return generate_ollama_embeddings(form_data=form_data, url_idx=url_idx)
-
-
-def generate_ollama_embeddings(
-    form_data: GenerateEmbeddingsForm,
-    url_idx: Optional[int] = None,
-):
-    log.info(f"generate_ollama_embeddings {form_data}")
-
-    if url_idx is None:
-        model = form_data.model
-
-        if ":" not in model:
-            model = f"{model}:latest"
-
-        if model in app.state.MODELS:
-            url_idx = random.choice(app.state.MODELS[model]["urls"])
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
-            )
-
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    r = requests.request(
-        method="POST",
-        url=f"{url}/api/embeddings",
-        headers={"Content-Type": "application/json"},
-        data=form_data.model_dump_json(exclude_none=True).encode(),
-    )
-    try:
-        r.raise_for_status()
-
-        data = r.json()
-
-        log.info(f"generate_ollama_embeddings {data}")
-
-        if "embedding" in data:
-            return data
-        else:
-            raise Exception("Something went wrong :/")
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = r.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except Exception:
-                error_detail = f"Ollama: {e}"
-
-        raise HTTPException(
-            status_code=r.status_code if r else 500,
-            detail=error_detail,
-        )
-
-
-def generate_ollama_batch_embeddings(
-    form_data: GenerateEmbedForm,
-    url_idx: Optional[int] = None,
-):
-    log.info(f"generate_ollama_batch_embeddings {form_data}")
-
-    if url_idx is None:
-        model = form_data.model
-
-        if ":" not in model:
-            model = f"{model}:latest"
-
-        if model in app.state.MODELS:
-            url_idx = random.choice(app.state.MODELS[model]["urls"])
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
-            )
-
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    r = requests.request(
-        method="POST",
-        url=f"{url}/api/embed",
-        headers={"Content-Type": "application/json"},
-        data=form_data.model_dump_json(exclude_none=True).encode(),
-    )
-    try:
-        r.raise_for_status()
-
-        data = r.json()
-
-        log.info(f"generate_ollama_batch_embeddings {data}")
-
-        if "embeddings" in data:
-            return data
-        else:
-            raise Exception("Something went wrong :/")
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = r.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except Exception:
-                error_detail = f"Ollama: {e}"
-
-        raise Exception(error_detail)
-
-
-class GenerateCompletionForm(BaseModel):
-    model: str
-    prompt: str
-    images: Optional[list[str]] = None
-    format: Optional[str] = None
-    options: Optional[dict] = None
-    system: Optional[str] = None
-    template: Optional[str] = None
-    context: Optional[str] = None
-    stream: Optional[bool] = True
-    raw: Optional[bool] = None
-    keep_alive: Optional[Union[int, str]] = None
-
-
-@app.post("/api/generate")
-@app.post("/api/generate/{url_idx}")
-async def generate_completion(
-    form_data: GenerateCompletionForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    if url_idx is None:
-        model = form_data.model
-
-        if ":" not in model:
-            model = f"{model}:latest"
-
-        if model in app.state.MODELS:
-            url_idx = random.choice(app.state.MODELS[model]["urls"])
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
-            )
-
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    log.info(f"url: {url}")
-
-    return await post_streaming_url(
-        f"{url}/api/generate", form_data.model_dump_json(exclude_none=True).encode()
-    )
-
-
-class ChatMessage(BaseModel):
-    role: str
-    content: str
-    images: Optional[list[str]] = None
-
-
-class GenerateChatCompletionForm(BaseModel):
-    model: str
-    messages: list[ChatMessage]
-    format: Optional[str] = None
-    options: Optional[dict] = None
-    template: Optional[str] = None
-    stream: Optional[bool] = None
-    keep_alive: Optional[Union[int, str]] = None
-
-
-def get_ollama_url(url_idx: Optional[int], model: str):
-    if url_idx is None:
-        if model not in app.state.MODELS:
-            raise HTTPException(
-                status_code=400,
-                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(model),
-            )
-        url_idx = random.choice(app.state.MODELS[model]["urls"])
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-    return url
-
-
-@app.post("/api/chat")
-@app.post("/api/chat/{url_idx}")
-async def generate_chat_completion(
-    form_data: GenerateChatCompletionForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    payload = {**form_data.model_dump(exclude_none=True)}
-    log.debug(f"generate_chat_completion() - 1.payload = {payload}")
-    if "metadata" in payload:
-        del payload["metadata"]
-
-    model_id = form_data.model
-
-    if app.state.config.ENABLE_MODEL_FILTER:
-        if user.role == "user" and model_id not in app.state.config.MODEL_FILTER_LIST:
-            raise HTTPException(
-                status_code=403,
-                detail="Model not found",
-            )
-
-    model_info = Models.get_model_by_id(model_id)
-
-    if model_info:
-        if model_info.base_model_id:
-            payload["model"] = model_info.base_model_id
-
-        params = model_info.params.model_dump()
-
-        if params:
-            if payload.get("options") is None:
-                payload["options"] = {}
-
-            payload["options"] = apply_model_params_to_body_ollama(
-                params, payload["options"]
-            )
-            payload = apply_model_system_prompt_to_body(params, payload, user)
-
-    if ":" not in payload["model"]:
-        payload["model"] = f"{payload['model']}:latest"
-
-    url = get_ollama_url(url_idx, payload["model"])
-    log.info(f"url: {url}")
-    log.debug(f"generate_chat_completion() - 2.payload = {payload}")
-
-    return await post_streaming_url(
-        f"{url}/api/chat",
-        json.dumps(payload),
-        stream=form_data.stream,
-        content_type="application/x-ndjson",
-    )
-
-
-# TODO: we should update this part once Ollama supports other types
-class OpenAIChatMessageContent(BaseModel):
-    type: str
-    model_config = ConfigDict(extra="allow")
-
-
-class OpenAIChatMessage(BaseModel):
-    role: str
-    content: Union[str, OpenAIChatMessageContent]
-
-    model_config = ConfigDict(extra="allow")
-
-
-class OpenAIChatCompletionForm(BaseModel):
-    model: str
-    messages: list[OpenAIChatMessage]
-
-    model_config = ConfigDict(extra="allow")
-
-
-@app.post("/v1/chat/completions")
-@app.post("/v1/chat/completions/{url_idx}")
-async def generate_openai_chat_completion(
-    form_data: dict,
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    completion_form = OpenAIChatCompletionForm(**form_data)
-    payload = {**completion_form.model_dump(exclude_none=True, exclude=["metadata"])}
-    if "metadata" in payload:
-        del payload["metadata"]
-
-    model_id = completion_form.model
-
-    if app.state.config.ENABLE_MODEL_FILTER:
-        if user.role == "user" and model_id not in app.state.config.MODEL_FILTER_LIST:
-            raise HTTPException(
-                status_code=403,
-                detail="Model not found",
-            )
-
-    model_info = Models.get_model_by_id(model_id)
-
-    if model_info:
-        if model_info.base_model_id:
-            payload["model"] = model_info.base_model_id
-
-        params = model_info.params.model_dump()
-
-        if params:
-            payload = apply_model_params_to_body_openai(params, payload)
-            payload = apply_model_system_prompt_to_body(params, payload, user)
-
-    if ":" not in payload["model"]:
-        payload["model"] = f"{payload['model']}:latest"
-
-    url = get_ollama_url(url_idx, payload["model"])
-    log.info(f"url: {url}")
-
-    return await post_streaming_url(
-        f"{url}/v1/chat/completions",
-        json.dumps(payload),
-        stream=payload.get("stream", False),
-    )
-
-
-@app.get("/v1/models")
-@app.get("/v1/models/{url_idx}")
-async def get_openai_models(
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    if url_idx is None:
-        models = await get_all_models()
-
-        if app.state.config.ENABLE_MODEL_FILTER:
-            if user.role == "user":
-                models["models"] = list(
-                    filter(
-                        lambda model: model["name"]
-                        in app.state.config.MODEL_FILTER_LIST,
-                        models["models"],
-                    )
-                )
-
-        return {
-            "data": [
-                {
-                    "id": model["model"],
-                    "object": "model",
-                    "created": int(time.time()),
-                    "owned_by": "openai",
-                }
-                for model in models["models"]
-            ],
-            "object": "list",
-        }
-
-    else:
-        url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-        try:
-            r = requests.request(method="GET", url=f"{url}/api/tags")
-            r.raise_for_status()
-
-            models = r.json()
-
-            return {
-                "data": [
-                    {
-                        "id": model["model"],
-                        "object": "model",
-                        "created": int(time.time()),
-                        "owned_by": "openai",
-                    }
-                    for model in models["models"]
-                ],
-                "object": "list",
-            }
-
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"Ollama: {res['error']}"
-                except Exception:
-                    error_detail = f"Ollama: {e}"
-
-            raise HTTPException(
-                status_code=r.status_code if r else 500,
-                detail=error_detail,
-            )
-
-
-class UrlForm(BaseModel):
-    url: str
-
-
-class UploadBlobForm(BaseModel):
-    filename: str
-
-
-def parse_huggingface_url(hf_url):
-    try:
-        # Parse the URL
-        parsed_url = urlparse(hf_url)
-
-        # Get the path and split it into components
-        path_components = parsed_url.path.split("/")
-
-        # Extract the desired output
-        model_file = path_components[-1]
-
-        return model_file
-    except ValueError:
-        return None
-
-
-async def download_file_stream(
-    ollama_url, file_url, file_path, file_name, chunk_size=1024 * 1024
-):
-    done = False
-
-    if os.path.exists(file_path):
-        current_size = os.path.getsize(file_path)
-    else:
-        current_size = 0
-
-    headers = {"Range": f"bytes={current_size}-"} if current_size > 0 else {}
-
-    timeout = aiohttp.ClientTimeout(total=600)  # Set the timeout
-
-    async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
-        async with session.get(file_url, headers=headers) as response:
-            total_size = int(response.headers.get("content-length", 0)) + current_size
-
-            with open(file_path, "ab+") as file:
-                async for data in response.content.iter_chunked(chunk_size):
-                    current_size += len(data)
-                    file.write(data)
-
-                    done = current_size == total_size
-                    progress = round((current_size / total_size) * 100, 2)
-
-                    yield f'data: {{"progress": {progress}, "completed": {current_size}, "total": {total_size}}}\n\n'
-
-                if done:
-                    file.seek(0)
-                    hashed = calculate_sha256(file)
-                    file.seek(0)
-
-                    url = f"{ollama_url}/api/blobs/sha256:{hashed}"
-                    response = requests.post(url, data=file)
-
-                    if response.ok:
-                        res = {
-                            "done": done,
-                            "blob": f"sha256:{hashed}",
-                            "name": file_name,
-                        }
-                        os.remove(file_path)
-
-                        yield f"data: {json.dumps(res)}\n\n"
-                    else:
-                        raise "Ollama: Could not create blob, Please try again."
-
-
-# url = "https://huggingface.co/TheBloke/stablelm-zephyr-3b-GGUF/resolve/main/stablelm-zephyr-3b.Q2_K.gguf"
-@app.post("/models/download")
-@app.post("/models/download/{url_idx}")
-async def download_model(
-    form_data: UrlForm,
-    url_idx: Optional[int] = None,
-    user=Depends(get_admin_user),
-):
-    allowed_hosts = ["https://huggingface.co/", "https://github.com/"]
-
-    if not any(form_data.url.startswith(host) for host in allowed_hosts):
-        raise HTTPException(
-            status_code=400,
-            detail="Invalid file_url. Only URLs from allowed hosts are permitted.",
-        )
-
-    if url_idx is None:
-        url_idx = 0
-    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-
-    file_name = parse_huggingface_url(form_data.url)
-
-    if file_name:
-        file_path = f"{UPLOAD_DIR}/{file_name}"
-
-        return StreamingResponse(
-            download_file_stream(url, form_data.url, file_path, file_name),
-        )
-    else:
-        return None
-
-
-@app.post("/models/upload")
-@app.post("/models/upload/{url_idx}")
-def upload_model(
-    file: UploadFile = File(...),
-    url_idx: Optional[int] = None,
-    user=Depends(get_admin_user),
-):
-    if url_idx is None:
-        url_idx = 0
-    ollama_url = app.state.config.OLLAMA_BASE_URLS[url_idx]
-
-    file_path = f"{UPLOAD_DIR}/{file.filename}"
-
-    # Save file in chunks
-    with open(file_path, "wb+") as f:
-        for chunk in file.file:
-            f.write(chunk)
-
-    def file_process_stream():
-        nonlocal ollama_url
-        total_size = os.path.getsize(file_path)
-        chunk_size = 1024 * 1024
-        try:
-            with open(file_path, "rb") as f:
-                total = 0
-                done = False
-
-                while not done:
-                    chunk = f.read(chunk_size)
-                    if not chunk:
-                        done = True
-                        continue
-
-                    total += len(chunk)
-                    progress = round((total / total_size) * 100, 2)
-
-                    res = {
-                        "progress": progress,
-                        "total": total_size,
-                        "completed": total,
-                    }
-                    yield f"data: {json.dumps(res)}\n\n"
-
-                if done:
-                    f.seek(0)
-                    hashed = calculate_sha256(f)
-                    f.seek(0)
-
-                    url = f"{ollama_url}/api/blobs/sha256:{hashed}"
-                    response = requests.post(url, data=f)
-
-                    if response.ok:
-                        res = {
-                            "done": done,
-                            "blob": f"sha256:{hashed}",
-                            "name": file.filename,
-                        }
-                        os.remove(file_path)
-                        yield f"data: {json.dumps(res)}\n\n"
-                    else:
-                        raise Exception(
-                            "Ollama: Could not create blob, Please try again."
-                        )
-
-        except Exception as e:
-            res = {"error": str(e)}
-            yield f"data: {json.dumps(res)}\n\n"
-
-    return StreamingResponse(file_process_stream(), media_type="text/event-stream")
+import asyncio
+import json
+import logging
+import os
+import random
+import re
+import time
+from typing import Optional, Union
+from urllib.parse import urlparse
+
+import aiohttp
+import requests
+from open_webui.apps.webui.models.models import Models
+from open_webui.config import (
+    CORS_ALLOW_ORIGIN,
+    ENABLE_MODEL_FILTER,
+    ENABLE_OLLAMA_API,
+    MODEL_FILTER_LIST,
+    OLLAMA_BASE_URLS,
+    UPLOAD_DIR,
+    AppConfig,
+)
+from open_webui.env import AIOHTTP_CLIENT_TIMEOUT
+
+
+from open_webui.constants import ERROR_MESSAGES
+from open_webui.env import SRC_LOG_LEVELS
+from fastapi import Depends, FastAPI, File, HTTPException, Request, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, ConfigDict
+from starlette.background import BackgroundTask
+
+
+from open_webui.utils.misc import (
+    calculate_sha256,
+)
+from open_webui.utils.payload import (
+    apply_model_params_to_body_ollama,
+    apply_model_params_to_body_openai,
+    apply_model_system_prompt_to_body,
+)
+from open_webui.utils.utils import get_admin_user, get_verified_user
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["OLLAMA"])
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ALLOW_ORIGIN,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.state.config = AppConfig()
+
+app.state.config.ENABLE_MODEL_FILTER = ENABLE_MODEL_FILTER
+app.state.config.MODEL_FILTER_LIST = MODEL_FILTER_LIST
+
+app.state.config.ENABLE_OLLAMA_API = ENABLE_OLLAMA_API
+app.state.config.OLLAMA_BASE_URLS = OLLAMA_BASE_URLS
+app.state.MODELS = {}
+
+
+# TODO: Implement a more intelligent load balancing mechanism for distributing requests among multiple backend instances.
+# Current implementation uses a simple round-robin approach (random.choice). Consider incorporating algorithms like weighted round-robin,
+# least connections, or least response time for better resource utilization and performance optimization.
+
+
+@app.middleware("http")
+async def check_url(request: Request, call_next):
+    if len(app.state.MODELS) == 0:
+        await get_all_models()
+    else:
+        pass
+
+    response = await call_next(request)
+    return response
+
+
+@app.head("/")
+@app.get("/")
+async def get_status():
+    return {"status": True}
+
+
+@app.get("/config")
+async def get_config(user=Depends(get_admin_user)):
+    return {"ENABLE_OLLAMA_API": app.state.config.ENABLE_OLLAMA_API}
+
+
+class OllamaConfigForm(BaseModel):
+    enable_ollama_api: Optional[bool] = None
+
+
+@app.post("/config/update")
+async def update_config(form_data: OllamaConfigForm, user=Depends(get_admin_user)):
+    app.state.config.ENABLE_OLLAMA_API = form_data.enable_ollama_api
+    return {"ENABLE_OLLAMA_API": app.state.config.ENABLE_OLLAMA_API}
+
+
+@app.get("/urls")
+async def get_ollama_api_urls(user=Depends(get_admin_user)):
+    return {"OLLAMA_BASE_URLS": app.state.config.OLLAMA_BASE_URLS}
+
+
+class UrlUpdateForm(BaseModel):
+    urls: list[str]
+
+
+@app.post("/urls/update")
+async def update_ollama_api_url(form_data: UrlUpdateForm, user=Depends(get_admin_user)):
+    app.state.config.OLLAMA_BASE_URLS = form_data.urls
+
+    log.info(f"app.state.config.OLLAMA_BASE_URLS: {app.state.config.OLLAMA_BASE_URLS}")
+    return {"OLLAMA_BASE_URLS": app.state.config.OLLAMA_BASE_URLS}
+
+
+async def fetch_url(url):
+    timeout = aiohttp.ClientTimeout(total=3)
+    try:
+        async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
+            async with session.get(url) as response:
+                return await response.json()
+    except Exception as e:
+        # Handle connection error here
+        log.error(f"Connection error: {e}")
+        return None
+
+
+async def cleanup_response(
+    response: Optional[aiohttp.ClientResponse],
+    session: Optional[aiohttp.ClientSession],
+):
+    if response:
+        response.close()
+    if session:
+        await session.close()
+
+
+async def post_streaming_url(
+    url: str, payload: Union[str, bytes], stream: bool = True, content_type=None
+):
+    r = None
+    try:
+        session = aiohttp.ClientSession(
+            trust_env=True, timeout=aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
+        )
+        r = await session.post(
+            url,
+            data=payload,
+            headers={"Content-Type": "application/json"},
+        )
+        r.raise_for_status()
+
+        if stream:
+            headers = dict(r.headers)
+            if content_type:
+                headers["Content-Type"] = content_type
+            return StreamingResponse(
+                r.content,
+                status_code=r.status,
+                headers=headers,
+                background=BackgroundTask(
+                    cleanup_response, response=r, session=session
+                ),
+            )
+        else:
+            res = await r.json()
+            await cleanup_response(r, session)
+            return res
+
+    except Exception as e:
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = await r.json()
+                if "error" in res:
+                    error_detail = f"Ollama: {res['error']}"
+            except Exception:
+                error_detail = f"Ollama: {e}"
+
+        raise HTTPException(
+            status_code=r.status if r else 500,
+            detail=error_detail,
+        )
+
+
+def merge_models_lists(model_lists):
+    merged_models = {}
+
+    for idx, model_list in enumerate(model_lists):
+        if model_list is not None:
+            for model in model_list:
+                digest = model["digest"]
+                if digest not in merged_models:
+                    model["urls"] = [idx]
+                    merged_models[digest] = model
+                else:
+                    merged_models[digest]["urls"].append(idx)
+
+    return list(merged_models.values())
+
+
+async def get_all_models():
+    log.info("get_all_models()")
+
+    if app.state.config.ENABLE_OLLAMA_API:
+        tasks = [
+            fetch_url(f"{url}/api/tags") for url in app.state.config.OLLAMA_BASE_URLS
+        ]
+        responses = await asyncio.gather(*tasks)
+
+        models = {
+            "models": merge_models_lists(
+                map(
+                    lambda response: response["models"] if response else None, responses
+                )
+            )
+        }
+
+    else:
+        models = {"models": []}
+
+    app.state.MODELS = {model["model"]: model for model in models["models"]}
+
+    return models
+
+
+@app.get("/api/tags")
+@app.get("/api/tags/{url_idx}")
+async def get_ollama_tags(
+    url_idx: Optional[int] = None, user=Depends(get_verified_user)
+):
+    if url_idx is None:
+        models = await get_all_models()
+
+        if app.state.config.ENABLE_MODEL_FILTER:
+            if user.role == "user":
+                models["models"] = list(
+                    filter(
+                        lambda model: model["name"]
+                        in app.state.config.MODEL_FILTER_LIST,
+                        models["models"],
+                    )
+                )
+                return models
+        return models
+    else:
+        url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+
+        r = None
+        try:
+            r = requests.request(method="GET", url=f"{url}/api/tags")
+            r.raise_for_status()
+
+            return r.json()
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"Ollama: {res['error']}"
+                except Exception:
+                    error_detail = f"Ollama: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r else 500,
+                detail=error_detail,
+            )
+
+
+@app.get("/api/version")
+@app.get("/api/version/{url_idx}")
+async def get_ollama_versions(url_idx: Optional[int] = None):
+    if app.state.config.ENABLE_OLLAMA_API:
+        if url_idx is None:
+            # returns lowest version
+            tasks = [
+                fetch_url(f"{url}/api/version")
+                for url in app.state.config.OLLAMA_BASE_URLS
+            ]
+            responses = await asyncio.gather(*tasks)
+            responses = list(filter(lambda x: x is not None, responses))
+
+            if len(responses) > 0:
+                lowest_version = min(
+                    responses,
+                    key=lambda x: tuple(
+                        map(int, re.sub(r"^v|-.*", "", x["version"]).split("."))
+                    ),
+                )
+
+                return {"version": lowest_version["version"]}
+            else:
+                raise HTTPException(
+                    status_code=500,
+                    detail=ERROR_MESSAGES.OLLAMA_NOT_FOUND,
+                )
+        else:
+            url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+
+            r = None
+            try:
+                r = requests.request(method="GET", url=f"{url}/api/version")
+                r.raise_for_status()
+
+                return r.json()
+            except Exception as e:
+                log.exception(e)
+                error_detail = "Open WebUI: Server Connection Error"
+                if r is not None:
+                    try:
+                        res = r.json()
+                        if "error" in res:
+                            error_detail = f"Ollama: {res['error']}"
+                    except Exception:
+                        error_detail = f"Ollama: {e}"
+
+                raise HTTPException(
+                    status_code=r.status_code if r else 500,
+                    detail=error_detail,
+                )
+    else:
+        return {"version": False}
+
+
+class ModelNameForm(BaseModel):
+    name: str
+
+
+@app.post("/api/pull")
+@app.post("/api/pull/{url_idx}")
+async def pull_model(
+    form_data: ModelNameForm, url_idx: int = 0, user=Depends(get_admin_user)
+):
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    # Admin should be able to pull models from any source
+    payload = {**form_data.model_dump(exclude_none=True), "insecure": True}
+
+    return await post_streaming_url(f"{url}/api/pull", json.dumps(payload))
+
+
+class PushModelForm(BaseModel):
+    name: str
+    insecure: Optional[bool] = None
+    stream: Optional[bool] = None
+
+
+@app.delete("/api/push")
+@app.delete("/api/push/{url_idx}")
+async def push_model(
+    form_data: PushModelForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_admin_user),
+):
+    if url_idx is None:
+        if form_data.name in app.state.MODELS:
+            url_idx = app.state.MODELS[form_data.name]["urls"][0]
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.name),
+            )
+
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.debug(f"url: {url}")
+
+    return await post_streaming_url(
+        f"{url}/api/push", form_data.model_dump_json(exclude_none=True).encode()
+    )
+
+
+class CreateModelForm(BaseModel):
+    name: str
+    modelfile: Optional[str] = None
+    stream: Optional[bool] = None
+    path: Optional[str] = None
+
+
+@app.post("/api/create")
+@app.post("/api/create/{url_idx}")
+async def create_model(
+    form_data: CreateModelForm, url_idx: int = 0, user=Depends(get_admin_user)
+):
+    log.debug(f"form_data: {form_data}")
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    return await post_streaming_url(
+        f"{url}/api/create", form_data.model_dump_json(exclude_none=True).encode()
+    )
+
+
+class CopyModelForm(BaseModel):
+    source: str
+    destination: str
+
+
+@app.post("/api/copy")
+@app.post("/api/copy/{url_idx}")
+async def copy_model(
+    form_data: CopyModelForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_admin_user),
+):
+    if url_idx is None:
+        if form_data.source in app.state.MODELS:
+            url_idx = app.state.MODELS[form_data.source]["urls"][0]
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.source),
+            )
+
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+    r = requests.request(
+        method="POST",
+        url=f"{url}/api/copy",
+        headers={"Content-Type": "application/json"},
+        data=form_data.model_dump_json(exclude_none=True).encode(),
+    )
+
+    try:
+        r.raise_for_status()
+
+        log.debug(f"r.text: {r.text}")
+
+        return True
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = r.json()
+                if "error" in res:
+                    error_detail = f"Ollama: {res['error']}"
+            except Exception:
+                error_detail = f"Ollama: {e}"
+
+        raise HTTPException(
+            status_code=r.status_code if r else 500,
+            detail=error_detail,
+        )
+
+
+@app.delete("/api/delete")
+@app.delete("/api/delete/{url_idx}")
+async def delete_model(
+    form_data: ModelNameForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_admin_user),
+):
+    if url_idx is None:
+        if form_data.name in app.state.MODELS:
+            url_idx = app.state.MODELS[form_data.name]["urls"][0]
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.name),
+            )
+
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    r = requests.request(
+        method="DELETE",
+        url=f"{url}/api/delete",
+        headers={"Content-Type": "application/json"},
+        data=form_data.model_dump_json(exclude_none=True).encode(),
+    )
+    try:
+        r.raise_for_status()
+
+        log.debug(f"r.text: {r.text}")
+
+        return True
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = r.json()
+                if "error" in res:
+                    error_detail = f"Ollama: {res['error']}"
+            except Exception:
+                error_detail = f"Ollama: {e}"
+
+        raise HTTPException(
+            status_code=r.status_code if r else 500,
+            detail=error_detail,
+        )
+
+
+@app.post("/api/show")
+async def show_model_info(form_data: ModelNameForm, user=Depends(get_verified_user)):
+    if form_data.name not in app.state.MODELS:
+        raise HTTPException(
+            status_code=400,
+            detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.name),
+        )
+
+    url_idx = random.choice(app.state.MODELS[form_data.name]["urls"])
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    r = requests.request(
+        method="POST",
+        url=f"{url}/api/show",
+        headers={"Content-Type": "application/json"},
+        data=form_data.model_dump_json(exclude_none=True).encode(),
+    )
+    try:
+        r.raise_for_status()
+
+        return r.json()
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = r.json()
+                if "error" in res:
+                    error_detail = f"Ollama: {res['error']}"
+            except Exception:
+                error_detail = f"Ollama: {e}"
+
+        raise HTTPException(
+            status_code=r.status_code if r else 500,
+            detail=error_detail,
+        )
+
+
+class GenerateEmbeddingsForm(BaseModel):
+    model: str
+    prompt: str
+    options: Optional[dict] = None
+    keep_alive: Optional[Union[int, str]] = None
+
+
+class GenerateEmbedForm(BaseModel):
+    model: str
+    input: list[str] | str
+    truncate: Optional[bool] = None
+    options: Optional[dict] = None
+    keep_alive: Optional[Union[int, str]] = None
+
+
+@app.post("/api/embed")
+@app.post("/api/embed/{url_idx}")
+async def generate_embeddings(
+    form_data: GenerateEmbedForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+):
+    return generate_ollama_batch_embeddings(form_data, url_idx)
+
+
+@app.post("/api/embeddings")
+@app.post("/api/embeddings/{url_idx}")
+async def generate_embeddings(
+    form_data: GenerateEmbeddingsForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+):
+    return generate_ollama_embeddings(form_data=form_data, url_idx=url_idx)
+
+
+def generate_ollama_embeddings(
+    form_data: GenerateEmbeddingsForm,
+    url_idx: Optional[int] = None,
+):
+    log.info(f"generate_ollama_embeddings {form_data}")
+
+    if url_idx is None:
+        model = form_data.model
+
+        if ":" not in model:
+            model = f"{model}:latest"
+
+        if model in app.state.MODELS:
+            url_idx = random.choice(app.state.MODELS[model]["urls"])
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
+            )
+
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    r = requests.request(
+        method="POST",
+        url=f"{url}/api/embeddings",
+        headers={"Content-Type": "application/json"},
+        data=form_data.model_dump_json(exclude_none=True).encode(),
+    )
+    try:
+        r.raise_for_status()
+
+        data = r.json()
+
+        log.info(f"generate_ollama_embeddings {data}")
+
+        if "embedding" in data:
+            return data
+        else:
+            raise Exception("Something went wrong :/")
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = r.json()
+                if "error" in res:
+                    error_detail = f"Ollama: {res['error']}"
+            except Exception:
+                error_detail = f"Ollama: {e}"
+
+        raise HTTPException(
+            status_code=r.status_code if r else 500,
+            detail=error_detail,
+        )
+
+
+def generate_ollama_batch_embeddings(
+    form_data: GenerateEmbedForm,
+    url_idx: Optional[int] = None,
+):
+    log.info(f"generate_ollama_batch_embeddings {form_data}")
+
+    if url_idx is None:
+        model = form_data.model
+
+        if ":" not in model:
+            model = f"{model}:latest"
+
+        if model in app.state.MODELS:
+            url_idx = random.choice(app.state.MODELS[model]["urls"])
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
+            )
+
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    r = requests.request(
+        method="POST",
+        url=f"{url}/api/embed",
+        headers={"Content-Type": "application/json"},
+        data=form_data.model_dump_json(exclude_none=True).encode(),
+    )
+    try:
+        r.raise_for_status()
+
+        data = r.json()
+
+        log.info(f"generate_ollama_batch_embeddings {data}")
+
+        if "embeddings" in data:
+            return data
+        else:
+            raise Exception("Something went wrong :/")
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = r.json()
+                if "error" in res:
+                    error_detail = f"Ollama: {res['error']}"
+            except Exception:
+                error_detail = f"Ollama: {e}"
+
+        raise Exception(error_detail)
+
+
+class GenerateCompletionForm(BaseModel):
+    model: str
+    prompt: str
+    images: Optional[list[str]] = None
+    format: Optional[str] = None
+    options: Optional[dict] = None
+    system: Optional[str] = None
+    template: Optional[str] = None
+    context: Optional[str] = None
+    stream: Optional[bool] = True
+    raw: Optional[bool] = None
+    keep_alive: Optional[Union[int, str]] = None
+
+
+@app.post("/api/generate")
+@app.post("/api/generate/{url_idx}")
+async def generate_completion(
+    form_data: GenerateCompletionForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+):
+    if url_idx is None:
+        model = form_data.model
+
+        if ":" not in model:
+            model = f"{model}:latest"
+
+        if model in app.state.MODELS:
+            url_idx = random.choice(app.state.MODELS[model]["urls"])
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
+            )
+
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    log.info(f"url: {url}")
+
+    return await post_streaming_url(
+        f"{url}/api/generate", form_data.model_dump_json(exclude_none=True).encode()
+    )
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+    images: Optional[list[str]] = None
+
+
+class GenerateChatCompletionForm(BaseModel):
+    model: str
+    messages: list[ChatMessage]
+    format: Optional[str] = None
+    options: Optional[dict] = None
+    template: Optional[str] = None
+    stream: Optional[bool] = None
+    keep_alive: Optional[Union[int, str]] = None
+
+
+def get_ollama_url(url_idx: Optional[int], model: str):
+    if url_idx is None:
+        if model not in app.state.MODELS:
+            raise HTTPException(
+                status_code=400,
+                detail=ERROR_MESSAGES.MODEL_NOT_FOUND(model),
+            )
+        url_idx = random.choice(app.state.MODELS[model]["urls"])
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+    return url
+
+
+@app.post("/api/chat")
+@app.post("/api/chat/{url_idx}")
+async def generate_chat_completion(
+    form_data: GenerateChatCompletionForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+    bypass_filter: Optional[bool] = False,
+):
+    payload = {**form_data.model_dump(exclude_none=True)}
+    log.debug(f"generate_chat_completion() - 1.payload = {payload}")
+    if "metadata" in payload:
+        del payload["metadata"]
+
+    model_id = form_data.model
+
+    if not bypass_filter and app.state.config.ENABLE_MODEL_FILTER:
+        if user.role == "user" and model_id not in app.state.config.MODEL_FILTER_LIST:
+            raise HTTPException(
+                status_code=403,
+                detail="Model not found",
+            )
+
+    model_info = Models.get_model_by_id(model_id)
+
+    if model_info:
+        if model_info.base_model_id:
+            payload["model"] = model_info.base_model_id
+
+        params = model_info.params.model_dump()
+
+        if params:
+            if payload.get("options") is None:
+                payload["options"] = {}
+
+            payload["options"] = apply_model_params_to_body_ollama(
+                params, payload["options"]
+            )
+            payload = apply_model_system_prompt_to_body(params, payload, user)
+
+    if ":" not in payload["model"]:
+        payload["model"] = f"{payload['model']}:latest"
+
+    url = get_ollama_url(url_idx, payload["model"])
+    log.info(f"url: {url}")
+    log.debug(f"generate_chat_completion() - 2.payload = {payload}")
+
+    return await post_streaming_url(
+        f"{url}/api/chat",
+        json.dumps(payload),
+        stream=form_data.stream,
+        content_type="application/x-ndjson",
+    )
+
+
+# TODO: we should update this part once Ollama supports other types
+class OpenAIChatMessageContent(BaseModel):
+    type: str
+    model_config = ConfigDict(extra="allow")
+
+
+class OpenAIChatMessage(BaseModel):
+    role: str
+    content: Union[str, OpenAIChatMessageContent]
+
+    model_config = ConfigDict(extra="allow")
+
+
+class OpenAIChatCompletionForm(BaseModel):
+    model: str
+    messages: list[OpenAIChatMessage]
+
+    model_config = ConfigDict(extra="allow")
+
+
+@app.post("/v1/chat/completions")
+@app.post("/v1/chat/completions/{url_idx}")
+async def generate_openai_chat_completion(
+    form_data: dict,
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+):
+    completion_form = OpenAIChatCompletionForm(**form_data)
+    payload = {**completion_form.model_dump(exclude_none=True, exclude=["metadata"])}
+    if "metadata" in payload:
+        del payload["metadata"]
+
+    model_id = completion_form.model
+
+    if app.state.config.ENABLE_MODEL_FILTER:
+        if user.role == "user" and model_id not in app.state.config.MODEL_FILTER_LIST:
+            raise HTTPException(
+                status_code=403,
+                detail="Model not found",
+            )
+
+    model_info = Models.get_model_by_id(model_id)
+
+    if model_info:
+        if model_info.base_model_id:
+            payload["model"] = model_info.base_model_id
+
+        params = model_info.params.model_dump()
+
+        if params:
+            payload = apply_model_params_to_body_openai(params, payload)
+            payload = apply_model_system_prompt_to_body(params, payload, user)
+
+    if ":" not in payload["model"]:
+        payload["model"] = f"{payload['model']}:latest"
+
+    url = get_ollama_url(url_idx, payload["model"])
+    log.info(f"url: {url}")
+
+    return await post_streaming_url(
+        f"{url}/v1/chat/completions",
+        json.dumps(payload),
+        stream=payload.get("stream", False),
+    )
+
+
+@app.get("/v1/models")
+@app.get("/v1/models/{url_idx}")
+async def get_openai_models(
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+):
+    if url_idx is None:
+        models = await get_all_models()
+
+        if app.state.config.ENABLE_MODEL_FILTER:
+            if user.role == "user":
+                models["models"] = list(
+                    filter(
+                        lambda model: model["name"]
+                        in app.state.config.MODEL_FILTER_LIST,
+                        models["models"],
+                    )
+                )
+
+        return {
+            "data": [
+                {
+                    "id": model["model"],
+                    "object": "model",
+                    "created": int(time.time()),
+                    "owned_by": "openai",
+                }
+                for model in models["models"]
+            ],
+            "object": "list",
+        }
+
+    else:
+        url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+        try:
+            r = requests.request(method="GET", url=f"{url}/api/tags")
+            r.raise_for_status()
+
+            models = r.json()
+
+            return {
+                "data": [
+                    {
+                        "id": model["model"],
+                        "object": "model",
+                        "created": int(time.time()),
+                        "owned_by": "openai",
+                    }
+                    for model in models["models"]
+                ],
+                "object": "list",
+            }
+
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"Ollama: {res['error']}"
+                except Exception:
+                    error_detail = f"Ollama: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r else 500,
+                detail=error_detail,
+            )
+
+
+class UrlForm(BaseModel):
+    url: str
+
+
+class UploadBlobForm(BaseModel):
+    filename: str
+
+
+def parse_huggingface_url(hf_url):
+    try:
+        # Parse the URL
+        parsed_url = urlparse(hf_url)
+
+        # Get the path and split it into components
+        path_components = parsed_url.path.split("/")
+
+        # Extract the desired output
+        model_file = path_components[-1]
+
+        return model_file
+    except ValueError:
+        return None
+
+
+async def download_file_stream(
+    ollama_url, file_url, file_path, file_name, chunk_size=1024 * 1024
+):
+    done = False
+
+    if os.path.exists(file_path):
+        current_size = os.path.getsize(file_path)
+    else:
+        current_size = 0
+
+    headers = {"Range": f"bytes={current_size}-"} if current_size > 0 else {}
+
+    timeout = aiohttp.ClientTimeout(total=600)  # Set the timeout
+
+    async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
+        async with session.get(file_url, headers=headers) as response:
+            total_size = int(response.headers.get("content-length", 0)) + current_size
+
+            with open(file_path, "ab+") as file:
+                async for data in response.content.iter_chunked(chunk_size):
+                    current_size += len(data)
+                    file.write(data)
+
+                    done = current_size == total_size
+                    progress = round((current_size / total_size) * 100, 2)
+
+                    yield f'data: {{"progress": {progress}, "completed": {current_size}, "total": {total_size}}}\n\n'
+
+                if done:
+                    file.seek(0)
+                    hashed = calculate_sha256(file)
+                    file.seek(0)
+
+                    url = f"{ollama_url}/api/blobs/sha256:{hashed}"
+                    response = requests.post(url, data=file)
+
+                    if response.ok:
+                        res = {
+                            "done": done,
+                            "blob": f"sha256:{hashed}",
+                            "name": file_name,
+                        }
+                        os.remove(file_path)
+
+                        yield f"data: {json.dumps(res)}\n\n"
+                    else:
+                        raise "Ollama: Could not create blob, Please try again."
+
+
+# url = "https://huggingface.co/TheBloke/stablelm-zephyr-3b-GGUF/resolve/main/stablelm-zephyr-3b.Q2_K.gguf"
+@app.post("/models/download")
+@app.post("/models/download/{url_idx}")
+async def download_model(
+    form_data: UrlForm,
+    url_idx: Optional[int] = None,
+    user=Depends(get_admin_user),
+):
+    allowed_hosts = ["https://huggingface.co/", "https://github.com/"]
+
+    if not any(form_data.url.startswith(host) for host in allowed_hosts):
+        raise HTTPException(
+            status_code=400,
+            detail="Invalid file_url. Only URLs from allowed hosts are permitted.",
+        )
+
+    if url_idx is None:
+        url_idx = 0
+    url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+
+    file_name = parse_huggingface_url(form_data.url)
+
+    if file_name:
+        file_path = f"{UPLOAD_DIR}/{file_name}"
+
+        return StreamingResponse(
+            download_file_stream(url, form_data.url, file_path, file_name),
+        )
+    else:
+        return None
+
+
+@app.post("/models/upload")
+@app.post("/models/upload/{url_idx}")
+def upload_model(
+    file: UploadFile = File(...),
+    url_idx: Optional[int] = None,
+    user=Depends(get_admin_user),
+):
+    if url_idx is None:
+        url_idx = 0
+    ollama_url = app.state.config.OLLAMA_BASE_URLS[url_idx]
+
+    file_path = f"{UPLOAD_DIR}/{file.filename}"
+
+    # Save file in chunks
+    with open(file_path, "wb+") as f:
+        for chunk in file.file:
+            f.write(chunk)
+
+    def file_process_stream():
+        nonlocal ollama_url
+        total_size = os.path.getsize(file_path)
+        chunk_size = 1024 * 1024
+        try:
+            with open(file_path, "rb") as f:
+                total = 0
+                done = False
+
+                while not done:
+                    chunk = f.read(chunk_size)
+                    if not chunk:
+                        done = True
+                        continue
+
+                    total += len(chunk)
+                    progress = round((total / total_size) * 100, 2)
+
+                    res = {
+                        "progress": progress,
+                        "total": total_size,
+                        "completed": total,
+                    }
+                    yield f"data: {json.dumps(res)}\n\n"
+
+                if done:
+                    f.seek(0)
+                    hashed = calculate_sha256(f)
+                    f.seek(0)
+
+                    url = f"{ollama_url}/api/blobs/sha256:{hashed}"
+                    response = requests.post(url, data=f)
+
+                    if response.ok:
+                        res = {
+                            "done": done,
+                            "blob": f"sha256:{hashed}",
+                            "name": file.filename,
+                        }
+                        os.remove(file_path)
+                        yield f"data: {json.dumps(res)}\n\n"
+                    else:
+                        raise Exception(
+                            "Ollama: Could not create blob, Please try again."
+                        )
+
+        except Exception as e:
+            res = {"error": str(e)}
+            yield f"data: {json.dumps(res)}\n\n"
+
+    return StreamingResponse(file_process_stream(), media_type="text/event-stream")

backend/open_webui/apps/openai/main.py

@@ -1,554 +1,554 @@
-import asyncio
-import hashlib
-import json
-import logging
-from pathlib import Path
-from typing import Literal, Optional, overload
-
-import aiohttp
-import requests
-from open_webui.apps.webui.models.models import Models
-from open_webui.config import (
-    CACHE_DIR,
-    CORS_ALLOW_ORIGIN,
-    ENABLE_MODEL_FILTER,
-    ENABLE_OPENAI_API,
-    MODEL_FILTER_LIST,
-    OPENAI_API_BASE_URLS,
-    OPENAI_API_KEYS,
-    AppConfig,
-)
-from open_webui.env import (
-    AIOHTTP_CLIENT_TIMEOUT,
-    AIOHTTP_CLIENT_TIMEOUT_OPENAI_MODEL_LIST,
-)
-
-from open_webui.constants import ERROR_MESSAGES
-from open_webui.env import SRC_LOG_LEVELS
-from fastapi import Depends, FastAPI, HTTPException, Request
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import FileResponse, StreamingResponse
-from pydantic import BaseModel
-from starlette.background import BackgroundTask
-
-from open_webui.utils.payload import (
-    apply_model_params_to_body_openai,
-    apply_model_system_prompt_to_body,
-)
-
-from open_webui.utils.utils import get_admin_user, get_verified_user
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["OPENAI"])
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=CORS_ALLOW_ORIGIN,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.state.config = AppConfig()
-
-app.state.config.ENABLE_MODEL_FILTER = ENABLE_MODEL_FILTER
-app.state.config.MODEL_FILTER_LIST = MODEL_FILTER_LIST
-
-app.state.config.ENABLE_OPENAI_API = ENABLE_OPENAI_API
-app.state.config.OPENAI_API_BASE_URLS = OPENAI_API_BASE_URLS
-app.state.config.OPENAI_API_KEYS = OPENAI_API_KEYS
-
-app.state.MODELS = {}
-
-
-@app.middleware("http")
-async def check_url(request: Request, call_next):
-    if len(app.state.MODELS) == 0:
-        await get_all_models()
-
-    response = await call_next(request)
-    return response
-
-
-@app.get("/config")
-async def get_config(user=Depends(get_admin_user)):
-    return {"ENABLE_OPENAI_API": app.state.config.ENABLE_OPENAI_API}
-
-
-class OpenAIConfigForm(BaseModel):
-    enable_openai_api: Optional[bool] = None
-
-
-@app.post("/config/update")
-async def update_config(form_data: OpenAIConfigForm, user=Depends(get_admin_user)):
-    app.state.config.ENABLE_OPENAI_API = form_data.enable_openai_api
-    return {"ENABLE_OPENAI_API": app.state.config.ENABLE_OPENAI_API}
-
-
-class UrlsUpdateForm(BaseModel):
-    urls: list[str]
-
-
-class KeysUpdateForm(BaseModel):
-    keys: list[str]
-
-
-@app.get("/urls")
-async def get_openai_urls(user=Depends(get_admin_user)):
-    return {"OPENAI_API_BASE_URLS": app.state.config.OPENAI_API_BASE_URLS}
-
-
-@app.post("/urls/update")
-async def update_openai_urls(form_data: UrlsUpdateForm, user=Depends(get_admin_user)):
-    await get_all_models()
-    app.state.config.OPENAI_API_BASE_URLS = form_data.urls
-    return {"OPENAI_API_BASE_URLS": app.state.config.OPENAI_API_BASE_URLS}
-
-
-@app.get("/keys")
-async def get_openai_keys(user=Depends(get_admin_user)):
-    return {"OPENAI_API_KEYS": app.state.config.OPENAI_API_KEYS}
-
-
-@app.post("/keys/update")
-async def update_openai_key(form_data: KeysUpdateForm, user=Depends(get_admin_user)):
-    app.state.config.OPENAI_API_KEYS = form_data.keys
-    return {"OPENAI_API_KEYS": app.state.config.OPENAI_API_KEYS}
-
-
-@app.post("/audio/speech")
-async def speech(request: Request, user=Depends(get_verified_user)):
-    idx = None
-    try:
-        idx = app.state.config.OPENAI_API_BASE_URLS.index("https://api.openai.com/v1")
-        body = await request.body()
-        name = hashlib.sha256(body).hexdigest()
-
-        SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
-        SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)
-        file_path = SPEECH_CACHE_DIR.joinpath(f"{name}.mp3")
-        file_body_path = SPEECH_CACHE_DIR.joinpath(f"{name}.json")
-
-        # Check if the file already exists in the cache
-        if file_path.is_file():
-            return FileResponse(file_path)
-
-        headers = {}
-        headers["Authorization"] = f"Bearer {app.state.config.OPENAI_API_KEYS[idx]}"
-        headers["Content-Type"] = "application/json"
-        if "openrouter.ai" in app.state.config.OPENAI_API_BASE_URLS[idx]:
-            headers["HTTP-Referer"] = "https://openwebui.com/"
-            headers["X-Title"] = "Open WebUI"
-        r = None
-        try:
-            r = requests.post(
-                url=f"{app.state.config.OPENAI_API_BASE_URLS[idx]}/audio/speech",
-                data=body,
-                headers=headers,
-                stream=True,
-            )
-
-            r.raise_for_status()
-
-            # Save the streaming content to a file
-            with open(file_path, "wb") as f:
-                for chunk in r.iter_content(chunk_size=8192):
-                    f.write(chunk)
-
-            with open(file_body_path, "w") as f:
-                json.dump(json.loads(body.decode("utf-8")), f)
-
-            # Return the saved file
-            return FileResponse(file_path)
-
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"External: {res['error']}"
-                except Exception:
-                    error_detail = f"External: {e}"
-
-            raise HTTPException(
-                status_code=r.status_code if r else 500, detail=error_detail
-            )
-
-    except ValueError:
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.OPENAI_NOT_FOUND)
-
-
-async def fetch_url(url, key):
-    timeout = aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT_OPENAI_MODEL_LIST)
-    try:
-        headers = {"Authorization": f"Bearer {key}"}
-        async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
-            async with session.get(url, headers=headers) as response:
-                return await response.json()
-    except Exception as e:
-        # Handle connection error here
-        log.error(f"Connection error: {e}")
-        return None
-
-
-async def cleanup_response(
-    response: Optional[aiohttp.ClientResponse],
-    session: Optional[aiohttp.ClientSession],
-):
-    if response:
-        response.close()
-    if session:
-        await session.close()
-
-
-def merge_models_lists(model_lists):
-    log.debug(f"merge_models_lists {model_lists}")
-    merged_list = []
-
-    for idx, models in enumerate(model_lists):
-        if models is not None and "error" not in models:
-            merged_list.extend(
-                [
-                    {
-                        **model,
-                        "name": model.get("name", model["id"]),
-                        "owned_by": "openai",
-                        "openai": model,
-                        "urlIdx": idx,
-                    }
-                    for model in models
-                    if "api.openai.com"
-                    not in app.state.config.OPENAI_API_BASE_URLS[idx]
-                    or not any(
-                        name in model["id"]
-                        for name in [
-                            "babbage",
-                            "dall-e",
-                            "davinci",
-                            "embedding",
-                            "tts",
-                            "whisper",
-                        ]
-                    )
-                ]
-            )
-
-    return merged_list
-
-
-def is_openai_api_disabled():
-    return not app.state.config.ENABLE_OPENAI_API
-
-
-async def get_all_models_raw() -> list:
-    if is_openai_api_disabled():
-        return []
-
-    # Check if API KEYS length is same than API URLS length
-    num_urls = len(app.state.config.OPENAI_API_BASE_URLS)
-    num_keys = len(app.state.config.OPENAI_API_KEYS)
-
-    if num_keys != num_urls:
-        # if there are more keys than urls, remove the extra keys
-        if num_keys > num_urls:
-            new_keys = app.state.config.OPENAI_API_KEYS[:num_urls]
-            app.state.config.OPENAI_API_KEYS = new_keys
-        # if there are more urls than keys, add empty keys
-        else:
-            app.state.config.OPENAI_API_KEYS += [""] * (num_urls - num_keys)
-
-    tasks = [
-        fetch_url(f"{url}/models", app.state.config.OPENAI_API_KEYS[idx])
-        for idx, url in enumerate(app.state.config.OPENAI_API_BASE_URLS)
-    ]
-
-    responses = await asyncio.gather(*tasks)
-    log.debug(f"get_all_models:responses() {responses}")
-
-    return responses
-
-
-@overload
-async def get_all_models(raw: Literal[True]) -> list: ...
-
-
-@overload
-async def get_all_models(raw: Literal[False] = False) -> dict[str, list]: ...
-
-
-async def get_all_models(raw=False) -> dict[str, list] | list:
-    log.info("get_all_models()")
-    if is_openai_api_disabled():
-        return [] if raw else {"data": []}
-
-    responses = await get_all_models_raw()
-    if raw:
-        return responses
-
-    def extract_data(response):
-        if response and "data" in response:
-            return response["data"]
-        if isinstance(response, list):
-            return response
-        return None
-
-    models = {"data": merge_models_lists(map(extract_data, responses))}
-
-    log.debug(f"models: {models}")
-    app.state.MODELS = {model["id"]: model for model in models["data"]}
-
-    return models
-
-
-@app.get("/models")
-@app.get("/models/{url_idx}")
-async def get_models(url_idx: Optional[int] = None, user=Depends(get_verified_user)):
-    if url_idx is None:
-        models = await get_all_models()
-        if app.state.config.ENABLE_MODEL_FILTER:
-            if user.role == "user":
-                models["data"] = list(
-                    filter(
-                        lambda model: model["id"] in app.state.config.MODEL_FILTER_LIST,
-                        models["data"],
-                    )
-                )
-                return models
-        return models
-    else:
-        url = app.state.config.OPENAI_API_BASE_URLS[url_idx]
-        key = app.state.config.OPENAI_API_KEYS[url_idx]
-
-        headers = {}
-        headers["Authorization"] = f"Bearer {key}"
-        headers["Content-Type"] = "application/json"
-
-        r = None
-
-        try:
-            r = requests.request(method="GET", url=f"{url}/models", headers=headers)
-            r.raise_for_status()
-
-            response_data = r.json()
-
-            if "api.openai.com" in url:
-                # Filter the response data
-                response_data["data"] = [
-                    model
-                    for model in response_data["data"]
-                    if not any(
-                        name in model["id"]
-                        for name in [
-                            "babbage",
-                            "dall-e",
-                            "davinci",
-                            "embedding",
-                            "tts",
-                            "whisper",
-                        ]
-                    )
-                ]
-
-            return response_data
-        except Exception as e:
-            log.exception(e)
-            error_detail = "Open WebUI: Server Connection Error"
-            if r is not None:
-                try:
-                    res = r.json()
-                    if "error" in res:
-                        error_detail = f"External: {res['error']}"
-                except Exception:
-                    error_detail = f"External: {e}"
-
-            raise HTTPException(
-                status_code=r.status_code if r else 500,
-                detail=error_detail,
-            )
-
-
-@app.post("/chat/completions")
-@app.post("/chat/completions/{url_idx}")
-async def generate_chat_completion(
-    form_data: dict,
-    url_idx: Optional[int] = None,
-    user=Depends(get_verified_user),
-):
-    idx = 0
-    payload = {**form_data}
-
-    if "metadata" in payload:
-        del payload["metadata"]
-
-    model_id = form_data.get("model")
-    model_info = Models.get_model_by_id(model_id)
-
-    if model_info:
-        if model_info.base_model_id:
-            payload["model"] = model_info.base_model_id
-
-        params = model_info.params.model_dump()
-        payload = apply_model_params_to_body_openai(params, payload)
-        payload = apply_model_system_prompt_to_body(params, payload, user)
-
-    model = app.state.MODELS[payload.get("model")]
-    idx = model["urlIdx"]
-
-    if "pipeline" in model and model.get("pipeline"):
-        payload["user"] = {
-            "name": user.name,
-            "id": user.id,
-            "email": user.email,
-            "role": user.role,
-        }
-
-    url = app.state.config.OPENAI_API_BASE_URLS[idx]
-    key = app.state.config.OPENAI_API_KEYS[idx]
-    is_o1 = payload["model"].lower().startswith("o1-")
-
-    # Change max_completion_tokens to max_tokens (Backward compatible)
-    if "api.openai.com" not in url and not is_o1:
-        if "max_completion_tokens" in payload:
-            # Remove "max_completion_tokens" from the payload
-            payload["max_tokens"] = payload["max_completion_tokens"]
-            del payload["max_completion_tokens"]
-    else:
-        if is_o1 and "max_tokens" in payload:
-            payload["max_completion_tokens"] = payload["max_tokens"]
-            del payload["max_tokens"]
-        if "max_tokens" in payload and "max_completion_tokens" in payload:
-            del payload["max_tokens"]
-
-    # Fix: O1 does not support the "system" parameter, Modify "system" to "user"
-    if is_o1 and payload["messages"][0]["role"] == "system":
-        payload["messages"][0]["role"] = "user"
-
-    # Convert the modified body back to JSON
-    payload = json.dumps(payload)
-
-    log.debug(payload)
-
-    headers = {}
-    headers["Authorization"] = f"Bearer {key}"
-    headers["Content-Type"] = "application/json"
-    if "openrouter.ai" in app.state.config.OPENAI_API_BASE_URLS[idx]:
-        headers["HTTP-Referer"] = "https://openwebui.com/"
-        headers["X-Title"] = "Open WebUI"
-
-    r = None
-    session = None
-    streaming = False
-    response = None
-
-    try:
-        session = aiohttp.ClientSession(
-            trust_env=True, timeout=aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
-        )
-        r = await session.request(
-            method="POST",
-            url=f"{url}/chat/completions",
-            data=payload,
-            headers=headers,
-        )
-
-        # Check if response is SSE
-        if "text/event-stream" in r.headers.get("Content-Type", ""):
-            streaming = True
-            return StreamingResponse(
-                r.content,
-                status_code=r.status,
-                headers=dict(r.headers),
-                background=BackgroundTask(
-                    cleanup_response, response=r, session=session
-                ),
-            )
-        else:
-            try:
-                response = await r.json()
-            except Exception as e:
-                log.error(e)
-                response = await r.text()
-
-            r.raise_for_status()
-            return response
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if isinstance(response, dict):
-            if "error" in response:
-                error_detail = f"{response['error']['message'] if 'message' in response['error'] else response['error']}"
-        elif isinstance(response, str):
-            error_detail = response
-
-        raise HTTPException(status_code=r.status if r else 500, detail=error_detail)
-    finally:
-        if not streaming and session:
-            if r:
-                r.close()
-            await session.close()
-
-
-@app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
-async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
-    idx = 0
-
-    body = await request.body()
-
-    url = app.state.config.OPENAI_API_BASE_URLS[idx]
-    key = app.state.config.OPENAI_API_KEYS[idx]
-
-    target_url = f"{url}/{path}"
-
-    headers = {}
-    headers["Authorization"] = f"Bearer {key}"
-    headers["Content-Type"] = "application/json"
-
-    r = None
-    session = None
-    streaming = False
-
-    try:
-        session = aiohttp.ClientSession(trust_env=True)
-        r = await session.request(
-            method=request.method,
-            url=target_url,
-            data=body,
-            headers=headers,
-        )
-
-        r.raise_for_status()
-
-        # Check if response is SSE
-        if "text/event-stream" in r.headers.get("Content-Type", ""):
-            streaming = True
-            return StreamingResponse(
-                r.content,
-                status_code=r.status,
-                headers=dict(r.headers),
-                background=BackgroundTask(
-                    cleanup_response, response=r, session=session
-                ),
-            )
-        else:
-            response_data = await r.json()
-            return response_data
-    except Exception as e:
-        log.exception(e)
-        error_detail = "Open WebUI: Server Connection Error"
-        if r is not None:
-            try:
-                res = await r.json()
-                print(res)
-                if "error" in res:
-                    error_detail = f"External: {res['error']['message'] if 'message' in res['error'] else res['error']}"
-            except Exception:
-                error_detail = f"External: {e}"
-        raise HTTPException(status_code=r.status if r else 500, detail=error_detail)
-    finally:
-        if not streaming and session:
-            if r:
-                r.close()
-            await session.close()
+import asyncio
+import hashlib
+import json
+import logging
+from pathlib import Path
+from typing import Literal, Optional, overload
+
+import aiohttp
+import requests
+from open_webui.apps.webui.models.models import Models
+from open_webui.config import (
+    CACHE_DIR,
+    CORS_ALLOW_ORIGIN,
+    ENABLE_MODEL_FILTER,
+    ENABLE_OPENAI_API,
+    MODEL_FILTER_LIST,
+    OPENAI_API_BASE_URLS,
+    OPENAI_API_KEYS,
+    AppConfig,
+)
+from open_webui.env import (
+    AIOHTTP_CLIENT_TIMEOUT,
+    AIOHTTP_CLIENT_TIMEOUT_OPENAI_MODEL_LIST,
+)
+
+from open_webui.constants import ERROR_MESSAGES
+from open_webui.env import SRC_LOG_LEVELS
+from fastapi import Depends, FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, StreamingResponse
+from pydantic import BaseModel
+from starlette.background import BackgroundTask
+
+from open_webui.utils.payload import (
+    apply_model_params_to_body_openai,
+    apply_model_system_prompt_to_body,
+)
+
+from open_webui.utils.utils import get_admin_user, get_verified_user
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["OPENAI"])
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ALLOW_ORIGIN,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.state.config = AppConfig()
+
+app.state.config.ENABLE_MODEL_FILTER = ENABLE_MODEL_FILTER
+app.state.config.MODEL_FILTER_LIST = MODEL_FILTER_LIST
+
+app.state.config.ENABLE_OPENAI_API = ENABLE_OPENAI_API
+app.state.config.OPENAI_API_BASE_URLS = OPENAI_API_BASE_URLS
+app.state.config.OPENAI_API_KEYS = OPENAI_API_KEYS
+
+app.state.MODELS = {}
+
+
+@app.middleware("http")
+async def check_url(request: Request, call_next):
+    if len(app.state.MODELS) == 0:
+        await get_all_models()
+
+    response = await call_next(request)
+    return response
+
+
+@app.get("/config")
+async def get_config(user=Depends(get_admin_user)):
+    return {"ENABLE_OPENAI_API": app.state.config.ENABLE_OPENAI_API}
+
+
+class OpenAIConfigForm(BaseModel):
+    enable_openai_api: Optional[bool] = None
+
+
+@app.post("/config/update")
+async def update_config(form_data: OpenAIConfigForm, user=Depends(get_admin_user)):
+    app.state.config.ENABLE_OPENAI_API = form_data.enable_openai_api
+    return {"ENABLE_OPENAI_API": app.state.config.ENABLE_OPENAI_API}
+
+
+class UrlsUpdateForm(BaseModel):
+    urls: list[str]
+
+
+class KeysUpdateForm(BaseModel):
+    keys: list[str]
+
+
+@app.get("/urls")
+async def get_openai_urls(user=Depends(get_admin_user)):
+    return {"OPENAI_API_BASE_URLS": app.state.config.OPENAI_API_BASE_URLS}
+
+
+@app.post("/urls/update")
+async def update_openai_urls(form_data: UrlsUpdateForm, user=Depends(get_admin_user)):
+    await get_all_models()
+    app.state.config.OPENAI_API_BASE_URLS = form_data.urls
+    return {"OPENAI_API_BASE_URLS": app.state.config.OPENAI_API_BASE_URLS}
+
+
+@app.get("/keys")
+async def get_openai_keys(user=Depends(get_admin_user)):
+    return {"OPENAI_API_KEYS": app.state.config.OPENAI_API_KEYS}
+
+
+@app.post("/keys/update")
+async def update_openai_key(form_data: KeysUpdateForm, user=Depends(get_admin_user)):
+    app.state.config.OPENAI_API_KEYS = form_data.keys
+    return {"OPENAI_API_KEYS": app.state.config.OPENAI_API_KEYS}
+
+
+@app.post("/audio/speech")
+async def speech(request: Request, user=Depends(get_verified_user)):
+    idx = None
+    try:
+        idx = app.state.config.OPENAI_API_BASE_URLS.index("https://api.openai.com/v1")
+        body = await request.body()
+        name = hashlib.sha256(body).hexdigest()
+
+        SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
+        SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)
+        file_path = SPEECH_CACHE_DIR.joinpath(f"{name}.mp3")
+        file_body_path = SPEECH_CACHE_DIR.joinpath(f"{name}.json")
+
+        # Check if the file already exists in the cache
+        if file_path.is_file():
+            return FileResponse(file_path)
+
+        headers = {}
+        headers["Authorization"] = f"Bearer {app.state.config.OPENAI_API_KEYS[idx]}"
+        headers["Content-Type"] = "application/json"
+        if "openrouter.ai" in app.state.config.OPENAI_API_BASE_URLS[idx]:
+            headers["HTTP-Referer"] = "https://openwebui.com/"
+            headers["X-Title"] = "Open WebUI"
+        r = None
+        try:
+            r = requests.post(
+                url=f"{app.state.config.OPENAI_API_BASE_URLS[idx]}/audio/speech",
+                data=body,
+                headers=headers,
+                stream=True,
+            )
+
+            r.raise_for_status()
+
+            # Save the streaming content to a file
+            with open(file_path, "wb") as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    f.write(chunk)
+
+            with open(file_body_path, "w") as f:
+                json.dump(json.loads(body.decode("utf-8")), f)
+
+            # Return the saved file
+            return FileResponse(file_path)
+
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"External: {res['error']}"
+                except Exception:
+                    error_detail = f"External: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r else 500, detail=error_detail
+            )
+
+    except ValueError:
+        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.OPENAI_NOT_FOUND)
+
+
+async def fetch_url(url, key):
+    timeout = aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT_OPENAI_MODEL_LIST)
+    try:
+        headers = {"Authorization": f"Bearer {key}"}
+        async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
+            async with session.get(url, headers=headers) as response:
+                return await response.json()
+    except Exception as e:
+        # Handle connection error here
+        log.error(f"Connection error: {e}")
+        return None
+
+
+async def cleanup_response(
+    response: Optional[aiohttp.ClientResponse],
+    session: Optional[aiohttp.ClientSession],
+):
+    if response:
+        response.close()
+    if session:
+        await session.close()
+
+
+def merge_models_lists(model_lists):
+    log.debug(f"merge_models_lists {model_lists}")
+    merged_list = []
+
+    for idx, models in enumerate(model_lists):
+        if models is not None and "error" not in models:
+            merged_list.extend(
+                [
+                    {
+                        **model,
+                        "name": model.get("name", model["id"]),
+                        "owned_by": "openai",
+                        "openai": model,
+                        "urlIdx": idx,
+                    }
+                    for model in models
+                    if "api.openai.com"
+                    not in app.state.config.OPENAI_API_BASE_URLS[idx]
+                    or not any(
+                        name in model["id"]
+                        for name in [
+                            "babbage",
+                            "dall-e",
+                            "davinci",
+                            "embedding",
+                            "tts",
+                            "whisper",
+                        ]
+                    )
+                ]
+            )
+
+    return merged_list
+
+
+def is_openai_api_disabled():
+    return not app.state.config.ENABLE_OPENAI_API
+
+
+async def get_all_models_raw() -> list:
+    if is_openai_api_disabled():
+        return []
+
+    # Check if API KEYS length is same than API URLS length
+    num_urls = len(app.state.config.OPENAI_API_BASE_URLS)
+    num_keys = len(app.state.config.OPENAI_API_KEYS)
+
+    if num_keys != num_urls:
+        # if there are more keys than urls, remove the extra keys
+        if num_keys > num_urls:
+            new_keys = app.state.config.OPENAI_API_KEYS[:num_urls]
+            app.state.config.OPENAI_API_KEYS = new_keys
+        # if there are more urls than keys, add empty keys
+        else:
+            app.state.config.OPENAI_API_KEYS += [""] * (num_urls - num_keys)
+
+    tasks = [
+        fetch_url(f"{url}/models", app.state.config.OPENAI_API_KEYS[idx])
+        for idx, url in enumerate(app.state.config.OPENAI_API_BASE_URLS)
+    ]
+
+    responses = await asyncio.gather(*tasks)
+    log.debug(f"get_all_models:responses() {responses}")
+
+    return responses
+
+
+@overload
+async def get_all_models(raw: Literal[True]) -> list: ...
+
+
+@overload
+async def get_all_models(raw: Literal[False] = False) -> dict[str, list]: ...
+
+
+async def get_all_models(raw=False) -> dict[str, list] | list:
+    log.info("get_all_models()")
+    if is_openai_api_disabled():
+        return [] if raw else {"data": []}
+
+    responses = await get_all_models_raw()
+    if raw:
+        return responses
+
+    def extract_data(response):
+        if response and "data" in response:
+            return response["data"]
+        if isinstance(response, list):
+            return response
+        return None
+
+    models = {"data": merge_models_lists(map(extract_data, responses))}
+
+    log.debug(f"models: {models}")
+    app.state.MODELS = {model["id"]: model for model in models["data"]}
+
+    return models
+
+
+@app.get("/models")
+@app.get("/models/{url_idx}")
+async def get_models(url_idx: Optional[int] = None, user=Depends(get_verified_user)):
+    if url_idx is None:
+        models = await get_all_models()
+        if app.state.config.ENABLE_MODEL_FILTER:
+            if user.role == "user":
+                models["data"] = list(
+                    filter(
+                        lambda model: model["id"] in app.state.config.MODEL_FILTER_LIST,
+                        models["data"],
+                    )
+                )
+                return models
+        return models
+    else:
+        url = app.state.config.OPENAI_API_BASE_URLS[url_idx]
+        key = app.state.config.OPENAI_API_KEYS[url_idx]
+
+        headers = {}
+        headers["Authorization"] = f"Bearer {key}"
+        headers["Content-Type"] = "application/json"
+
+        r = None
+
+        try:
+            r = requests.request(method="GET", url=f"{url}/models", headers=headers)
+            r.raise_for_status()
+
+            response_data = r.json()
+
+            if "api.openai.com" in url:
+                # Filter the response data
+                response_data["data"] = [
+                    model
+                    for model in response_data["data"]
+                    if not any(
+                        name in model["id"]
+                        for name in [
+                            "babbage",
+                            "dall-e",
+                            "davinci",
+                            "embedding",
+                            "tts",
+                            "whisper",
+                        ]
+                    )
+                ]
+
+            return response_data
+        except Exception as e:
+            log.exception(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"External: {res['error']}"
+                except Exception:
+                    error_detail = f"External: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r else 500,
+                detail=error_detail,
+            )
+
+
+@app.post("/chat/completions")
+@app.post("/chat/completions/{url_idx}")
+async def generate_chat_completion(
+    form_data: dict,
+    url_idx: Optional[int] = None,
+    user=Depends(get_verified_user),
+):
+    idx = 0
+    payload = {**form_data}
+
+    if "metadata" in payload:
+        del payload["metadata"]
+
+    model_id = form_data.get("model")
+    model_info = Models.get_model_by_id(model_id)
+
+    if model_info:
+        if model_info.base_model_id:
+            payload["model"] = model_info.base_model_id
+
+        params = model_info.params.model_dump()
+        payload = apply_model_params_to_body_openai(params, payload)
+        payload = apply_model_system_prompt_to_body(params, payload, user)
+
+    model = app.state.MODELS[payload.get("model")]
+    idx = model["urlIdx"]
+
+    if "pipeline" in model and model.get("pipeline"):
+        payload["user"] = {
+            "name": user.name,
+            "id": user.id,
+            "email": user.email,
+            "role": user.role,
+        }
+
+    url = app.state.config.OPENAI_API_BASE_URLS[idx]
+    key = app.state.config.OPENAI_API_KEYS[idx]
+    is_o1 = payload["model"].lower().startswith("o1-")
+
+    # Change max_completion_tokens to max_tokens (Backward compatible)
+    if "api.openai.com" not in url and not is_o1:
+        if "max_completion_tokens" in payload:
+            # Remove "max_completion_tokens" from the payload
+            payload["max_tokens"] = payload["max_completion_tokens"]
+            del payload["max_completion_tokens"]
+    else:
+        if is_o1 and "max_tokens" in payload:
+            payload["max_completion_tokens"] = payload["max_tokens"]
+            del payload["max_tokens"]
+        if "max_tokens" in payload and "max_completion_tokens" in payload:
+            del payload["max_tokens"]
+
+    # Fix: O1 does not support the "system" parameter, Modify "system" to "user"
+    if is_o1 and payload["messages"][0]["role"] == "system":
+        payload["messages"][0]["role"] = "user"
+
+    # Convert the modified body back to JSON
+    payload = json.dumps(payload)
+
+    log.debug(payload)
+
+    headers = {}
+    headers["Authorization"] = f"Bearer {key}"
+    headers["Content-Type"] = "application/json"
+    if "openrouter.ai" in app.state.config.OPENAI_API_BASE_URLS[idx]:
+        headers["HTTP-Referer"] = "https://openwebui.com/"
+        headers["X-Title"] = "Open WebUI"
+
+    r = None
+    session = None
+    streaming = False
+    response = None
+
+    try:
+        session = aiohttp.ClientSession(
+            trust_env=True, timeout=aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
+        )
+        r = await session.request(
+            method="POST",
+            url=f"{url}/chat/completions",
+            data=payload,
+            headers=headers,
+        )
+
+        # Check if response is SSE
+        if "text/event-stream" in r.headers.get("Content-Type", ""):
+            streaming = True
+            return StreamingResponse(
+                r.content,
+                status_code=r.status,
+                headers=dict(r.headers),
+                background=BackgroundTask(
+                    cleanup_response, response=r, session=session
+                ),
+            )
+        else:
+            try:
+                response = await r.json()
+            except Exception as e:
+                log.error(e)
+                response = await r.text()
+
+            r.raise_for_status()
+            return response
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if isinstance(response, dict):
+            if "error" in response:
+                error_detail = f"{response['error']['message'] if 'message' in response['error'] else response['error']}"
+        elif isinstance(response, str):
+            error_detail = response
+
+        raise HTTPException(status_code=r.status if r else 500, detail=error_detail)
+    finally:
+        if not streaming and session:
+            if r:
+                r.close()
+            await session.close()
+
+
+@app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
+async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
+    idx = 0
+
+    body = await request.body()
+
+    url = app.state.config.OPENAI_API_BASE_URLS[idx]
+    key = app.state.config.OPENAI_API_KEYS[idx]
+
+    target_url = f"{url}/{path}"
+
+    headers = {}
+    headers["Authorization"] = f"Bearer {key}"
+    headers["Content-Type"] = "application/json"
+
+    r = None
+    session = None
+    streaming = False
+
+    try:
+        session = aiohttp.ClientSession(trust_env=True)
+        r = await session.request(
+            method=request.method,
+            url=target_url,
+            data=body,
+            headers=headers,
+        )
+
+        r.raise_for_status()
+
+        # Check if response is SSE
+        if "text/event-stream" in r.headers.get("Content-Type", ""):
+            streaming = True
+            return StreamingResponse(
+                r.content,
+                status_code=r.status,
+                headers=dict(r.headers),
+                background=BackgroundTask(
+                    cleanup_response, response=r, session=session
+                ),
+            )
+        else:
+            response_data = await r.json()
+            return response_data
+    except Exception as e:
+        log.exception(e)
+        error_detail = "Open WebUI: Server Connection Error"
+        if r is not None:
+            try:
+                res = await r.json()
+                print(res)
+                if "error" in res:
+                    error_detail = f"External: {res['error']['message'] if 'message' in res['error'] else res['error']}"
+            except Exception:
+                error_detail = f"External: {e}"
+        raise HTTPException(status_code=r.status if r else 500, detail=error_detail)
+    finally:
+        if not streaming and session:
+            if r:
+                r.close()
+            await session.close()

backend/open_webui/apps/retrieval/loaders/main.py

@@ -1,190 +1,190 @@
-import requests
-import logging
-import ftfy
-
-from langchain_community.document_loaders import (
-    BSHTMLLoader,
-    CSVLoader,
-    Docx2txtLoader,
-    OutlookMessageLoader,
-    PyPDFLoader,
-    TextLoader,
-    UnstructuredEPubLoader,
-    UnstructuredExcelLoader,
-    UnstructuredMarkdownLoader,
-    UnstructuredPowerPointLoader,
-    UnstructuredRSTLoader,
-    UnstructuredXMLLoader,
-    YoutubeLoader,
-)
-from langchain_core.documents import Document
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-known_source_ext = [
-    "go",
-    "py",
-    "java",
-    "sh",
-    "bat",
-    "ps1",
-    "cmd",
-    "js",
-    "ts",
-    "css",
-    "cpp",
-    "hpp",
-    "h",
-    "c",
-    "cs",
-    "sql",
-    "log",
-    "ini",
-    "pl",
-    "pm",
-    "r",
-    "dart",
-    "dockerfile",
-    "env",
-    "php",
-    "hs",
-    "hsc",
-    "lua",
-    "nginxconf",
-    "conf",
-    "m",
-    "mm",
-    "plsql",
-    "perl",
-    "rb",
-    "rs",
-    "db2",
-    "scala",
-    "bash",
-    "swift",
-    "vue",
-    "svelte",
-    "msg",
-    "ex",
-    "exs",
-    "erl",
-    "tsx",
-    "jsx",
-    "hs",
-    "lhs",
-]
-
-
-class TikaLoader:
-    def __init__(self, url, file_path, mime_type=None):
-        self.url = url
-        self.file_path = file_path
-        self.mime_type = mime_type
-
-    def load(self) -> list[Document]:
-        with open(self.file_path, "rb") as f:
-            data = f.read()
-
-        if self.mime_type is not None:
-            headers = {"Content-Type": self.mime_type}
-        else:
-            headers = {}
-
-        endpoint = self.url
-        if not endpoint.endswith("/"):
-            endpoint += "/"
-        endpoint += "tika/text"
-
-        r = requests.put(endpoint, data=data, headers=headers)
-
-        if r.ok:
-            raw_metadata = r.json()
-            text = raw_metadata.get("X-TIKA:content", "<No text content found>")
-
-            if "Content-Type" in raw_metadata:
-                headers["Content-Type"] = raw_metadata["Content-Type"]
-
-            log.info("Tika extracted text: %s", text)
-
-            return [Document(page_content=text, metadata=headers)]
-        else:
-            raise Exception(f"Error calling Tika: {r.reason}")
-
-
-class Loader:
-    def __init__(self, engine: str = "", **kwargs):
-        self.engine = engine
-        self.kwargs = kwargs
-
-    def load(
-        self, filename: str, file_content_type: str, file_path: str
-    ) -> list[Document]:
-        loader = self._get_loader(filename, file_content_type, file_path)
-        docs = loader.load()
-
-        return [
-            Document(
-                page_content=ftfy.fix_text(doc.page_content), metadata=doc.metadata
-            )
-            for doc in docs
-        ]
-
-    def _get_loader(self, filename: str, file_content_type: str, file_path: str):
-        file_ext = filename.split(".")[-1].lower()
-
-        if self.engine == "tika" and self.kwargs.get("TIKA_SERVER_URL"):
-            if file_ext in known_source_ext or (
-                file_content_type and file_content_type.find("text/") >= 0
-            ):
-                loader = TextLoader(file_path, autodetect_encoding=True)
-            else:
-                loader = TikaLoader(
-                    url=self.kwargs.get("TIKA_SERVER_URL"),
-                    file_path=file_path,
-                    mime_type=file_content_type,
-                )
-        else:
-            if file_ext == "pdf":
-                loader = PyPDFLoader(
-                    file_path, extract_images=self.kwargs.get("PDF_EXTRACT_IMAGES")
-                )
-            elif file_ext == "csv":
-                loader = CSVLoader(file_path)
-            elif file_ext == "rst":
-                loader = UnstructuredRSTLoader(file_path, mode="elements")
-            elif file_ext == "xml":
-                loader = UnstructuredXMLLoader(file_path)
-            elif file_ext in ["htm", "html"]:
-                loader = BSHTMLLoader(file_path, open_encoding="unicode_escape")
-            elif file_ext == "md":
-                loader = UnstructuredMarkdownLoader(file_path)
-            elif file_content_type == "application/epub+zip":
-                loader = UnstructuredEPubLoader(file_path)
-            elif (
-                file_content_type
-                == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-                or file_ext == "docx"
-            ):
-                loader = Docx2txtLoader(file_path)
-            elif file_content_type in [
-                "application/vnd.ms-excel",
-                "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-            ] or file_ext in ["xls", "xlsx"]:
-                loader = UnstructuredExcelLoader(file_path)
-            elif file_content_type in [
-                "application/vnd.ms-powerpoint",
-                "application/vnd.openxmlformats-officedocument.presentationml.presentation",
-            ] or file_ext in ["ppt", "pptx"]:
-                loader = UnstructuredPowerPointLoader(file_path)
-            elif file_ext == "msg":
-                loader = OutlookMessageLoader(file_path)
-            elif file_ext in known_source_ext or (
-                file_content_type and file_content_type.find("text/") >= 0
-            ):
-                loader = TextLoader(file_path, autodetect_encoding=True)
-            else:
-                loader = TextLoader(file_path, autodetect_encoding=True)
-
-        return loader
+import requests
+import logging
+import ftfy
+
+from langchain_community.document_loaders import (
+    BSHTMLLoader,
+    CSVLoader,
+    Docx2txtLoader,
+    OutlookMessageLoader,
+    PyPDFLoader,
+    TextLoader,
+    UnstructuredEPubLoader,
+    UnstructuredExcelLoader,
+    UnstructuredMarkdownLoader,
+    UnstructuredPowerPointLoader,
+    UnstructuredRSTLoader,
+    UnstructuredXMLLoader,
+    YoutubeLoader,
+)
+from langchain_core.documents import Document
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+known_source_ext = [
+    "go",
+    "py",
+    "java",
+    "sh",
+    "bat",
+    "ps1",
+    "cmd",
+    "js",
+    "ts",
+    "css",
+    "cpp",
+    "hpp",
+    "h",
+    "c",
+    "cs",
+    "sql",
+    "log",
+    "ini",
+    "pl",
+    "pm",
+    "r",
+    "dart",
+    "dockerfile",
+    "env",
+    "php",
+    "hs",
+    "hsc",
+    "lua",
+    "nginxconf",
+    "conf",
+    "m",
+    "mm",
+    "plsql",
+    "perl",
+    "rb",
+    "rs",
+    "db2",
+    "scala",
+    "bash",
+    "swift",
+    "vue",
+    "svelte",
+    "msg",
+    "ex",
+    "exs",
+    "erl",
+    "tsx",
+    "jsx",
+    "hs",
+    "lhs",
+]
+
+
+class TikaLoader:
+    def __init__(self, url, file_path, mime_type=None):
+        self.url = url
+        self.file_path = file_path
+        self.mime_type = mime_type
+
+    def load(self) -> list[Document]:
+        with open(self.file_path, "rb") as f:
+            data = f.read()
+
+        if self.mime_type is not None:
+            headers = {"Content-Type": self.mime_type}
+        else:
+            headers = {}
+
+        endpoint = self.url
+        if not endpoint.endswith("/"):
+            endpoint += "/"
+        endpoint += "tika/text"
+
+        r = requests.put(endpoint, data=data, headers=headers)
+
+        if r.ok:
+            raw_metadata = r.json()
+            text = raw_metadata.get("X-TIKA:content", "<No text content found>")
+
+            if "Content-Type" in raw_metadata:
+                headers["Content-Type"] = raw_metadata["Content-Type"]
+
+            log.info("Tika extracted text: %s", text)
+
+            return [Document(page_content=text, metadata=headers)]
+        else:
+            raise Exception(f"Error calling Tika: {r.reason}")
+
+
+class Loader:
+    def __init__(self, engine: str = "", **kwargs):
+        self.engine = engine
+        self.kwargs = kwargs
+
+    def load(
+        self, filename: str, file_content_type: str, file_path: str
+    ) -> list[Document]:
+        loader = self._get_loader(filename, file_content_type, file_path)
+        docs = loader.load()
+
+        return [
+            Document(
+                page_content=ftfy.fix_text(doc.page_content), metadata=doc.metadata
+            )
+            for doc in docs
+        ]
+
+    def _get_loader(self, filename: str, file_content_type: str, file_path: str):
+        file_ext = filename.split(".")[-1].lower()
+
+        if self.engine == "tika" and self.kwargs.get("TIKA_SERVER_URL"):
+            if file_ext in known_source_ext or (
+                file_content_type and file_content_type.find("text/") >= 0
+            ):
+                loader = TextLoader(file_path, autodetect_encoding=True)
+            else:
+                loader = TikaLoader(
+                    url=self.kwargs.get("TIKA_SERVER_URL"),
+                    file_path=file_path,
+                    mime_type=file_content_type,
+                )
+        else:
+            if file_ext == "pdf":
+                loader = PyPDFLoader(
+                    file_path, extract_images=self.kwargs.get("PDF_EXTRACT_IMAGES")
+                )
+            elif file_ext == "csv":
+                loader = CSVLoader(file_path)
+            elif file_ext == "rst":
+                loader = UnstructuredRSTLoader(file_path, mode="elements")
+            elif file_ext == "xml":
+                loader = UnstructuredXMLLoader(file_path)
+            elif file_ext in ["htm", "html"]:
+                loader = BSHTMLLoader(file_path, open_encoding="unicode_escape")
+            elif file_ext == "md":
+                loader = UnstructuredMarkdownLoader(file_path)
+            elif file_content_type == "application/epub+zip":
+                loader = UnstructuredEPubLoader(file_path)
+            elif (
+                file_content_type
+                == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+                or file_ext == "docx"
+            ):
+                loader = Docx2txtLoader(file_path)
+            elif file_content_type in [
+                "application/vnd.ms-excel",
+                "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+            ] or file_ext in ["xls", "xlsx"]:
+                loader = UnstructuredExcelLoader(file_path)
+            elif file_content_type in [
+                "application/vnd.ms-powerpoint",
+                "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+            ] or file_ext in ["ppt", "pptx"]:
+                loader = UnstructuredPowerPointLoader(file_path)
+            elif file_ext == "msg":
+                loader = OutlookMessageLoader(file_path)
+            elif file_ext in known_source_ext or (
+                file_content_type and file_content_type.find("text/") >= 0
+            ):
+                loader = TextLoader(file_path, autodetect_encoding=True)
+            else:
+                loader = TextLoader(file_path, autodetect_encoding=True)
+
+        return loader

backend/open_webui/apps/retrieval/main.py

@@ -1,1326 +1,1326 @@
-# TODO: Merge this with the webui_app and make it a single app
-
-import json
-import logging
-import mimetypes
-import os
-import shutil
-
-import uuid
-from datetime import datetime
-from pathlib import Path
-from typing import Iterator, Optional, Sequence, Union
-
-from fastapi import Depends, FastAPI, File, Form, HTTPException, UploadFile, status
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-
-
-from open_webui.storage.provider import Storage
-from open_webui.apps.webui.models.knowledge import Knowledges
-from open_webui.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
-
-# Document loaders
-from open_webui.apps.retrieval.loaders.main import Loader
-
-# Web search engines
-from open_webui.apps.retrieval.web.main import SearchResult
-from open_webui.apps.retrieval.web.utils import get_web_loader
-from open_webui.apps.retrieval.web.brave import search_brave
-from open_webui.apps.retrieval.web.duckduckgo import search_duckduckgo
-from open_webui.apps.retrieval.web.google_pse import search_google_pse
-from open_webui.apps.retrieval.web.jina_search import search_jina
-from open_webui.apps.retrieval.web.searchapi import search_searchapi
-from open_webui.apps.retrieval.web.searxng import search_searxng
-from open_webui.apps.retrieval.web.serper import search_serper
-from open_webui.apps.retrieval.web.serply import search_serply
-from open_webui.apps.retrieval.web.serpstack import search_serpstack
-from open_webui.apps.retrieval.web.tavily import search_tavily
-
-
-from open_webui.apps.retrieval.utils import (
-    get_embedding_function,
-    get_model_path,
-    query_collection,
-    query_collection_with_hybrid_search,
-    query_doc,
-    query_doc_with_hybrid_search,
-)
-
-from open_webui.apps.webui.models.files import Files
-from open_webui.config import (
-    BRAVE_SEARCH_API_KEY,
-    TIKTOKEN_ENCODING_NAME,
-    RAG_TEXT_SPLITTER,
-    CHUNK_OVERLAP,
-    CHUNK_SIZE,
-    CONTENT_EXTRACTION_ENGINE,
-    CORS_ALLOW_ORIGIN,
-    ENABLE_RAG_HYBRID_SEARCH,
-    ENABLE_RAG_LOCAL_WEB_FETCH,
-    ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
-    ENABLE_RAG_WEB_SEARCH,
-    ENV,
-    GOOGLE_PSE_API_KEY,
-    GOOGLE_PSE_ENGINE_ID,
-    PDF_EXTRACT_IMAGES,
-    RAG_EMBEDDING_ENGINE,
-    RAG_EMBEDDING_MODEL,
-    RAG_EMBEDDING_MODEL_AUTO_UPDATE,
-    RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
-    RAG_EMBEDDING_BATCH_SIZE,
-    RAG_FILE_MAX_COUNT,
-    RAG_FILE_MAX_SIZE,
-    RAG_OPENAI_API_BASE_URL,
-    RAG_OPENAI_API_KEY,
-    RAG_RELEVANCE_THRESHOLD,
-    RAG_RERANKING_MODEL,
-    RAG_RERANKING_MODEL_AUTO_UPDATE,
-    RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
-    DEFAULT_RAG_TEMPLATE,
-    RAG_TEMPLATE,
-    RAG_TOP_K,
-    RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
-    RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-    RAG_WEB_SEARCH_ENGINE,
-    RAG_WEB_SEARCH_RESULT_COUNT,
-    SEARCHAPI_API_KEY,
-    SEARCHAPI_ENGINE,
-    SEARXNG_QUERY_URL,
-    SERPER_API_KEY,
-    SERPLY_API_KEY,
-    SERPSTACK_API_KEY,
-    SERPSTACK_HTTPS,
-    TAVILY_API_KEY,
-    TIKA_SERVER_URL,
-    UPLOAD_DIR,
-    YOUTUBE_LOADER_LANGUAGE,
-    AppConfig,
-)
-from open_webui.constants import ERROR_MESSAGES
-from open_webui.env import SRC_LOG_LEVELS, DEVICE_TYPE, DOCKER
-from open_webui.utils.misc import (
-    calculate_sha256,
-    calculate_sha256_string,
-    extract_folders_after_data_docs,
-    sanitize_filename,
-)
-from open_webui.utils.utils import get_admin_user, get_verified_user
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
-from langchain_community.document_loaders import (
-    YoutubeLoader,
-)
-from langchain_core.documents import Document
-
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-app = FastAPI()
-
-app.state.config = AppConfig()
-
-app.state.config.TOP_K = RAG_TOP_K
-app.state.config.RELEVANCE_THRESHOLD = RAG_RELEVANCE_THRESHOLD
-app.state.config.FILE_MAX_SIZE = RAG_FILE_MAX_SIZE
-app.state.config.FILE_MAX_COUNT = RAG_FILE_MAX_COUNT
-
-app.state.config.ENABLE_RAG_HYBRID_SEARCH = ENABLE_RAG_HYBRID_SEARCH
-app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
-    ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION
-)
-
-app.state.config.CONTENT_EXTRACTION_ENGINE = CONTENT_EXTRACTION_ENGINE
-app.state.config.TIKA_SERVER_URL = TIKA_SERVER_URL
-
-app.state.config.TEXT_SPLITTER = RAG_TEXT_SPLITTER
-app.state.config.TIKTOKEN_ENCODING_NAME = TIKTOKEN_ENCODING_NAME
-
-app.state.config.CHUNK_SIZE = CHUNK_SIZE
-app.state.config.CHUNK_OVERLAP = CHUNK_OVERLAP
-
-app.state.config.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
-app.state.config.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
-app.state.config.RAG_EMBEDDING_BATCH_SIZE = RAG_EMBEDDING_BATCH_SIZE
-app.state.config.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL
-app.state.config.RAG_TEMPLATE = RAG_TEMPLATE
-
-app.state.config.OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
-app.state.config.OPENAI_API_KEY = RAG_OPENAI_API_KEY
-
-app.state.config.PDF_EXTRACT_IMAGES = PDF_EXTRACT_IMAGES
-
-app.state.config.YOUTUBE_LOADER_LANGUAGE = YOUTUBE_LOADER_LANGUAGE
-app.state.YOUTUBE_LOADER_TRANSLATION = None
-
-
-app.state.config.ENABLE_RAG_WEB_SEARCH = ENABLE_RAG_WEB_SEARCH
-app.state.config.RAG_WEB_SEARCH_ENGINE = RAG_WEB_SEARCH_ENGINE
-app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST = RAG_WEB_SEARCH_DOMAIN_FILTER_LIST
-
-app.state.config.SEARXNG_QUERY_URL = SEARXNG_QUERY_URL
-app.state.config.GOOGLE_PSE_API_KEY = GOOGLE_PSE_API_KEY
-app.state.config.GOOGLE_PSE_ENGINE_ID = GOOGLE_PSE_ENGINE_ID
-app.state.config.BRAVE_SEARCH_API_KEY = BRAVE_SEARCH_API_KEY
-app.state.config.SERPSTACK_API_KEY = SERPSTACK_API_KEY
-app.state.config.SERPSTACK_HTTPS = SERPSTACK_HTTPS
-app.state.config.SERPER_API_KEY = SERPER_API_KEY
-app.state.config.SERPLY_API_KEY = SERPLY_API_KEY
-app.state.config.TAVILY_API_KEY = TAVILY_API_KEY
-app.state.config.SEARCHAPI_API_KEY = SEARCHAPI_API_KEY
-app.state.config.SEARCHAPI_ENGINE = SEARCHAPI_ENGINE
-app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = RAG_WEB_SEARCH_RESULT_COUNT
-app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = RAG_WEB_SEARCH_CONCURRENT_REQUESTS
-
-
-def update_embedding_model(
-    embedding_model: str,
-    auto_update: bool = False,
-):
-    if embedding_model and app.state.config.RAG_EMBEDDING_ENGINE == "":
-        from sentence_transformers import SentenceTransformer
-
-        app.state.sentence_transformer_ef = SentenceTransformer(
-            get_model_path(embedding_model, auto_update),
-            device=DEVICE_TYPE,
-            trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
-        )
-    else:
-        app.state.sentence_transformer_ef = None
-
-
-def update_reranking_model(
-    reranking_model: str,
-    auto_update: bool = False,
-):
-    if reranking_model:
-        if any(model in reranking_model for model in ["jinaai/jina-colbert-v2"]):
-            try:
-                from open_webui.apps.retrieval.models.colbert import ColBERT
-
-                app.state.sentence_transformer_rf = ColBERT(
-                    get_model_path(reranking_model, auto_update),
-                    env="docker" if DOCKER else None,
-                )
-            except Exception as e:
-                log.error(f"ColBERT: {e}")
-                app.state.sentence_transformer_rf = None
-                app.state.config.ENABLE_RAG_HYBRID_SEARCH = False
-        else:
-            import sentence_transformers
-
-            try:
-                app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
-                    get_model_path(reranking_model, auto_update),
-                    device=DEVICE_TYPE,
-                    trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
-                )
-            except:
-                log.error("CrossEncoder error")
-                app.state.sentence_transformer_rf = None
-                app.state.config.ENABLE_RAG_HYBRID_SEARCH = False
-    else:
-        app.state.sentence_transformer_rf = None
-
-
-update_embedding_model(
-    app.state.config.RAG_EMBEDDING_MODEL,
-    RAG_EMBEDDING_MODEL_AUTO_UPDATE,
-)
-
-update_reranking_model(
-    app.state.config.RAG_RERANKING_MODEL,
-    RAG_RERANKING_MODEL_AUTO_UPDATE,
-)
-
-
-app.state.EMBEDDING_FUNCTION = get_embedding_function(
-    app.state.config.RAG_EMBEDDING_ENGINE,
-    app.state.config.RAG_EMBEDDING_MODEL,
-    app.state.sentence_transformer_ef,
-    app.state.config.OPENAI_API_KEY,
-    app.state.config.OPENAI_API_BASE_URL,
-    app.state.config.RAG_EMBEDDING_BATCH_SIZE,
-)
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=CORS_ALLOW_ORIGIN,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-class CollectionNameForm(BaseModel):
-    collection_name: Optional[str] = None
-
-
-class ProcessUrlForm(CollectionNameForm):
-    url: str
-
-
-class SearchForm(CollectionNameForm):
-    query: str
-
-
-@app.get("/")
-async def get_status():
-    return {
-        "status": True,
-        "chunk_size": app.state.config.CHUNK_SIZE,
-        "chunk_overlap": app.state.config.CHUNK_OVERLAP,
-        "template": app.state.config.RAG_TEMPLATE,
-        "embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
-        "embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
-        "reranking_model": app.state.config.RAG_RERANKING_MODEL,
-        "embedding_batch_size": app.state.config.RAG_EMBEDDING_BATCH_SIZE,
-    }
-
-
-@app.get("/embedding")
-async def get_embedding_config(user=Depends(get_admin_user)):
-    return {
-        "status": True,
-        "embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
-        "embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
-        "embedding_batch_size": app.state.config.RAG_EMBEDDING_BATCH_SIZE,
-        "openai_config": {
-            "url": app.state.config.OPENAI_API_BASE_URL,
-            "key": app.state.config.OPENAI_API_KEY,
-        },
-    }
-
-
-@app.get("/reranking")
-async def get_reraanking_config(user=Depends(get_admin_user)):
-    return {
-        "status": True,
-        "reranking_model": app.state.config.RAG_RERANKING_MODEL,
-    }
-
-
-class OpenAIConfigForm(BaseModel):
-    url: str
-    key: str
-
-
-class EmbeddingModelUpdateForm(BaseModel):
-    openai_config: Optional[OpenAIConfigForm] = None
-    embedding_engine: str
-    embedding_model: str
-    embedding_batch_size: Optional[int] = 1
-
-
-@app.post("/embedding/update")
-async def update_embedding_config(
-    form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
-):
-    log.info(
-        f"Updating embedding model: {app.state.config.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
-    )
-    try:
-        app.state.config.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
-        app.state.config.RAG_EMBEDDING_MODEL = form_data.embedding_model
-
-        if app.state.config.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]:
-            if form_data.openai_config is not None:
-                app.state.config.OPENAI_API_BASE_URL = form_data.openai_config.url
-                app.state.config.OPENAI_API_KEY = form_data.openai_config.key
-            app.state.config.RAG_EMBEDDING_BATCH_SIZE = form_data.embedding_batch_size
-
-        update_embedding_model(app.state.config.RAG_EMBEDDING_MODEL)
-
-        app.state.EMBEDDING_FUNCTION = get_embedding_function(
-            app.state.config.RAG_EMBEDDING_ENGINE,
-            app.state.config.RAG_EMBEDDING_MODEL,
-            app.state.sentence_transformer_ef,
-            app.state.config.OPENAI_API_KEY,
-            app.state.config.OPENAI_API_BASE_URL,
-            app.state.config.RAG_EMBEDDING_BATCH_SIZE,
-        )
-
-        return {
-            "status": True,
-            "embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
-            "embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
-            "embedding_batch_size": app.state.config.RAG_EMBEDDING_BATCH_SIZE,
-            "openai_config": {
-                "url": app.state.config.OPENAI_API_BASE_URL,
-                "key": app.state.config.OPENAI_API_KEY,
-            },
-        }
-    except Exception as e:
-        log.exception(f"Problem updating embedding model: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-class RerankingModelUpdateForm(BaseModel):
-    reranking_model: str
-
-
-@app.post("/reranking/update")
-async def update_reranking_config(
-    form_data: RerankingModelUpdateForm, user=Depends(get_admin_user)
-):
-    log.info(
-        f"Updating reranking model: {app.state.config.RAG_RERANKING_MODEL} to {form_data.reranking_model}"
-    )
-    try:
-        app.state.config.RAG_RERANKING_MODEL = form_data.reranking_model
-
-        update_reranking_model(app.state.config.RAG_RERANKING_MODEL, True)
-
-        return {
-            "status": True,
-            "reranking_model": app.state.config.RAG_RERANKING_MODEL,
-        }
-    except Exception as e:
-        log.exception(f"Problem updating reranking model: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-@app.get("/config")
-async def get_rag_config(user=Depends(get_admin_user)):
-    return {
-        "status": True,
-        "pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
-        "content_extraction": {
-            "engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
-            "tika_server_url": app.state.config.TIKA_SERVER_URL,
-        },
-        "chunk": {
-            "text_splitter": app.state.config.TEXT_SPLITTER,
-            "chunk_size": app.state.config.CHUNK_SIZE,
-            "chunk_overlap": app.state.config.CHUNK_OVERLAP,
-        },
-        "file": {
-            "max_size": app.state.config.FILE_MAX_SIZE,
-            "max_count": app.state.config.FILE_MAX_COUNT,
-        },
-        "youtube": {
-            "language": app.state.config.YOUTUBE_LOADER_LANGUAGE,
-            "translation": app.state.YOUTUBE_LOADER_TRANSLATION,
-        },
-        "web": {
-            "ssl_verification": app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
-            "search": {
-                "enabled": app.state.config.ENABLE_RAG_WEB_SEARCH,
-                "engine": app.state.config.RAG_WEB_SEARCH_ENGINE,
-                "searxng_query_url": app.state.config.SEARXNG_QUERY_URL,
-                "google_pse_api_key": app.state.config.GOOGLE_PSE_API_KEY,
-                "google_pse_engine_id": app.state.config.GOOGLE_PSE_ENGINE_ID,
-                "brave_search_api_key": app.state.config.BRAVE_SEARCH_API_KEY,
-                "serpstack_api_key": app.state.config.SERPSTACK_API_KEY,
-                "serpstack_https": app.state.config.SERPSTACK_HTTPS,
-                "serper_api_key": app.state.config.SERPER_API_KEY,
-                "serply_api_key": app.state.config.SERPLY_API_KEY,
-                "tavily_api_key": app.state.config.TAVILY_API_KEY,
-                "searchapi_api_key": app.state.config.SEARCHAPI_API_KEY,
-                "seaarchapi_engine": app.state.config.SEARCHAPI_ENGINE,
-                "result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                "concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
-            },
-        },
-    }
-
-
-class FileConfig(BaseModel):
-    max_size: Optional[int] = None
-    max_count: Optional[int] = None
-
-
-class ContentExtractionConfig(BaseModel):
-    engine: str = ""
-    tika_server_url: Optional[str] = None
-
-
-class ChunkParamUpdateForm(BaseModel):
-    text_splitter: Optional[str] = None
-    chunk_size: int
-    chunk_overlap: int
-
-
-class YoutubeLoaderConfig(BaseModel):
-    language: list[str]
-    translation: Optional[str] = None
-
-
-class WebSearchConfig(BaseModel):
-    enabled: bool
-    engine: Optional[str] = None
-    searxng_query_url: Optional[str] = None
-    google_pse_api_key: Optional[str] = None
-    google_pse_engine_id: Optional[str] = None
-    brave_search_api_key: Optional[str] = None
-    serpstack_api_key: Optional[str] = None
-    serpstack_https: Optional[bool] = None
-    serper_api_key: Optional[str] = None
-    serply_api_key: Optional[str] = None
-    tavily_api_key: Optional[str] = None
-    searchapi_api_key: Optional[str] = None
-    searchapi_engine: Optional[str] = None
-    result_count: Optional[int] = None
-    concurrent_requests: Optional[int] = None
-
-
-class WebConfig(BaseModel):
-    search: WebSearchConfig
-    web_loader_ssl_verification: Optional[bool] = None
-
-
-class ConfigUpdateForm(BaseModel):
-    pdf_extract_images: Optional[bool] = None
-    file: Optional[FileConfig] = None
-    content_extraction: Optional[ContentExtractionConfig] = None
-    chunk: Optional[ChunkParamUpdateForm] = None
-    youtube: Optional[YoutubeLoaderConfig] = None
-    web: Optional[WebConfig] = None
-
-
-@app.post("/config/update")
-async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_user)):
-    app.state.config.PDF_EXTRACT_IMAGES = (
-        form_data.pdf_extract_images
-        if form_data.pdf_extract_images is not None
-        else app.state.config.PDF_EXTRACT_IMAGES
-    )
-
-    if form_data.file is not None:
-        app.state.config.FILE_MAX_SIZE = form_data.file.max_size
-        app.state.config.FILE_MAX_COUNT = form_data.file.max_count
-
-    if form_data.content_extraction is not None:
-        log.info(f"Updating text settings: {form_data.content_extraction}")
-        app.state.config.CONTENT_EXTRACTION_ENGINE = form_data.content_extraction.engine
-        app.state.config.TIKA_SERVER_URL = form_data.content_extraction.tika_server_url
-
-    if form_data.chunk is not None:
-        app.state.config.TEXT_SPLITTER = form_data.chunk.text_splitter
-        app.state.config.CHUNK_SIZE = form_data.chunk.chunk_size
-        app.state.config.CHUNK_OVERLAP = form_data.chunk.chunk_overlap
-
-    if form_data.youtube is not None:
-        app.state.config.YOUTUBE_LOADER_LANGUAGE = form_data.youtube.language
-        app.state.YOUTUBE_LOADER_TRANSLATION = form_data.youtube.translation
-
-    if form_data.web is not None:
-        app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
-            form_data.web.web_loader_ssl_verification
-        )
-
-        app.state.config.ENABLE_RAG_WEB_SEARCH = form_data.web.search.enabled
-        app.state.config.RAG_WEB_SEARCH_ENGINE = form_data.web.search.engine
-        app.state.config.SEARXNG_QUERY_URL = form_data.web.search.searxng_query_url
-        app.state.config.GOOGLE_PSE_API_KEY = form_data.web.search.google_pse_api_key
-        app.state.config.GOOGLE_PSE_ENGINE_ID = (
-            form_data.web.search.google_pse_engine_id
-        )
-        app.state.config.BRAVE_SEARCH_API_KEY = (
-            form_data.web.search.brave_search_api_key
-        )
-        app.state.config.SERPSTACK_API_KEY = form_data.web.search.serpstack_api_key
-        app.state.config.SERPSTACK_HTTPS = form_data.web.search.serpstack_https
-        app.state.config.SERPER_API_KEY = form_data.web.search.serper_api_key
-        app.state.config.SERPLY_API_KEY = form_data.web.search.serply_api_key
-        app.state.config.TAVILY_API_KEY = form_data.web.search.tavily_api_key
-        app.state.config.SEARCHAPI_API_KEY = form_data.web.search.searchapi_api_key
-        app.state.config.SEARCHAPI_ENGINE = form_data.web.search.searchapi_engine
-        app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = form_data.web.search.result_count
-        app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = (
-            form_data.web.search.concurrent_requests
-        )
-
-    return {
-        "status": True,
-        "pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
-        "file": {
-            "max_size": app.state.config.FILE_MAX_SIZE,
-            "max_count": app.state.config.FILE_MAX_COUNT,
-        },
-        "content_extraction": {
-            "engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
-            "tika_server_url": app.state.config.TIKA_SERVER_URL,
-        },
-        "chunk": {
-            "text_splitter": app.state.config.TEXT_SPLITTER,
-            "chunk_size": app.state.config.CHUNK_SIZE,
-            "chunk_overlap": app.state.config.CHUNK_OVERLAP,
-        },
-        "youtube": {
-            "language": app.state.config.YOUTUBE_LOADER_LANGUAGE,
-            "translation": app.state.YOUTUBE_LOADER_TRANSLATION,
-        },
-        "web": {
-            "ssl_verification": app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
-            "search": {
-                "enabled": app.state.config.ENABLE_RAG_WEB_SEARCH,
-                "engine": app.state.config.RAG_WEB_SEARCH_ENGINE,
-                "searxng_query_url": app.state.config.SEARXNG_QUERY_URL,
-                "google_pse_api_key": app.state.config.GOOGLE_PSE_API_KEY,
-                "google_pse_engine_id": app.state.config.GOOGLE_PSE_ENGINE_ID,
-                "brave_search_api_key": app.state.config.BRAVE_SEARCH_API_KEY,
-                "serpstack_api_key": app.state.config.SERPSTACK_API_KEY,
-                "serpstack_https": app.state.config.SERPSTACK_HTTPS,
-                "serper_api_key": app.state.config.SERPER_API_KEY,
-                "serply_api_key": app.state.config.SERPLY_API_KEY,
-                "serachapi_api_key": app.state.config.SEARCHAPI_API_KEY,
-                "searchapi_engine": app.state.config.SEARCHAPI_ENGINE,
-                "tavily_api_key": app.state.config.TAVILY_API_KEY,
-                "result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                "concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
-            },
-        },
-    }
-
-
-@app.get("/template")
-async def get_rag_template(user=Depends(get_verified_user)):
-    return {
-        "status": True,
-        "template": app.state.config.RAG_TEMPLATE,
-    }
-
-
-@app.get("/query/settings")
-async def get_query_settings(user=Depends(get_admin_user)):
-    return {
-        "status": True,
-        "template": app.state.config.RAG_TEMPLATE,
-        "k": app.state.config.TOP_K,
-        "r": app.state.config.RELEVANCE_THRESHOLD,
-        "hybrid": app.state.config.ENABLE_RAG_HYBRID_SEARCH,
-    }
-
-
-class QuerySettingsForm(BaseModel):
-    k: Optional[int] = None
-    r: Optional[float] = None
-    template: Optional[str] = None
-    hybrid: Optional[bool] = None
-
-
-@app.post("/query/settings/update")
-async def update_query_settings(
-    form_data: QuerySettingsForm, user=Depends(get_admin_user)
-):
-    app.state.config.RAG_TEMPLATE = form_data.template
-    app.state.config.TOP_K = form_data.k if form_data.k else 4
-    app.state.config.RELEVANCE_THRESHOLD = form_data.r if form_data.r else 0.0
-
-    app.state.config.ENABLE_RAG_HYBRID_SEARCH = (
-        form_data.hybrid if form_data.hybrid else False
-    )
-
-    return {
-        "status": True,
-        "template": app.state.config.RAG_TEMPLATE,
-        "k": app.state.config.TOP_K,
-        "r": app.state.config.RELEVANCE_THRESHOLD,
-        "hybrid": app.state.config.ENABLE_RAG_HYBRID_SEARCH,
-    }
-
-
-####################################
-#
-# Document process and retrieval
-#
-####################################
-
-
-def save_docs_to_vector_db(
-    docs,
-    collection_name,
-    metadata: Optional[dict] = None,
-    overwrite: bool = False,
-    split: bool = True,
-    add: bool = False,
-) -> bool:
-    log.info(f"save_docs_to_vector_db {docs} {collection_name}")
-
-    # Check if entries with the same hash (metadata.hash) already exist
-    if metadata and "hash" in metadata:
-        result = VECTOR_DB_CLIENT.query(
-            collection_name=collection_name,
-            filter={"hash": metadata["hash"]},
-        )
-
-        if result is not None:
-            existing_doc_ids = result.ids[0]
-            if existing_doc_ids:
-                log.info(f"Document with hash {metadata['hash']} already exists")
-                raise ValueError(ERROR_MESSAGES.DUPLICATE_CONTENT)
-
-    if split:
-        if app.state.config.TEXT_SPLITTER in ["", "character"]:
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size=app.state.config.CHUNK_SIZE,
-                chunk_overlap=app.state.config.CHUNK_OVERLAP,
-                add_start_index=True,
-            )
-        elif app.state.config.TEXT_SPLITTER == "token":
-            text_splitter = TokenTextSplitter(
-                encoding_name=app.state.config.TIKTOKEN_ENCODING_NAME,
-                chunk_size=app.state.config.CHUNK_SIZE,
-                chunk_overlap=app.state.config.CHUNK_OVERLAP,
-                add_start_index=True,
-            )
-        else:
-            raise ValueError(ERROR_MESSAGES.DEFAULT("Invalid text splitter"))
-
-        docs = text_splitter.split_documents(docs)
-
-    if len(docs) == 0:
-        raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)
-
-    texts = [doc.page_content for doc in docs]
-    metadatas = [
-        {
-            **doc.metadata,
-            **(metadata if metadata else {}),
-            "embedding_config": json.dumps(
-                {
-                    "engine": app.state.config.RAG_EMBEDDING_ENGINE,
-                    "model": app.state.config.RAG_EMBEDDING_MODEL,
-                }
-            ),
-        }
-        for doc in docs
-    ]
-
-    # ChromaDB does not like datetime formats
-    # for meta-data so convert them to string.
-    for metadata in metadatas:
-        for key, value in metadata.items():
-            if isinstance(value, datetime):
-                metadata[key] = str(value)
-
-    try:
-        if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
-            log.info(f"collection {collection_name} already exists")
-
-            if overwrite:
-                VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
-                log.info(f"deleting existing collection {collection_name}")
-            elif add is False:
-                log.info(
-                    f"collection {collection_name} already exists, overwrite is False and add is False"
-                )
-                return True
-
-        log.info(f"adding to collection {collection_name}")
-        embedding_function = get_embedding_function(
-            app.state.config.RAG_EMBEDDING_ENGINE,
-            app.state.config.RAG_EMBEDDING_MODEL,
-            app.state.sentence_transformer_ef,
-            app.state.config.OPENAI_API_KEY,
-            app.state.config.OPENAI_API_BASE_URL,
-            app.state.config.RAG_EMBEDDING_BATCH_SIZE,
-        )
-
-        embeddings = embedding_function(
-            list(map(lambda x: x.replace("\n", " "), texts))
-        )
-
-        items = [
-            {
-                "id": str(uuid.uuid4()),
-                "text": text,
-                "vector": embeddings[idx],
-                "metadata": metadatas[idx],
-            }
-            for idx, text in enumerate(texts)
-        ]
-
-        VECTOR_DB_CLIENT.insert(
-            collection_name=collection_name,
-            items=items,
-        )
-
-        return True
-    except Exception as e:
-        log.exception(e)
-        return False
-
-
-class ProcessFileForm(BaseModel):
-    file_id: str
-    content: Optional[str] = None
-    collection_name: Optional[str] = None
-
-
-@app.post("/process/file")
-def process_file(
-    form_data: ProcessFileForm,
-    user=Depends(get_verified_user),
-):
-    try:
-        file = Files.get_file_by_id(form_data.file_id)
-
-        collection_name = form_data.collection_name
-
-        if collection_name is None:
-            collection_name = f"file-{file.id}"
-
-        if form_data.content:
-            # Update the content in the file
-            # Usage: /files/{file_id}/data/content/update
-
-            VECTOR_DB_CLIENT.delete(
-                collection_name=f"file-{file.id}",
-                filter={"file_id": file.id},
-            )
-
-            docs = [
-                Document(
-                    page_content=form_data.content,
-                    metadata={
-                        "name": file.meta.get("name", file.filename),
-                        "created_by": file.user_id,
-                        "file_id": file.id,
-                        **file.meta,
-                    },
-                )
-            ]
-
-            text_content = form_data.content
-        elif form_data.collection_name:
-            # Check if the file has already been processed and save the content
-            # Usage: /knowledge/{id}/file/add, /knowledge/{id}/file/update
-
-            result = VECTOR_DB_CLIENT.query(
-                collection_name=f"file-{file.id}", filter={"file_id": file.id}
-            )
-
-            if result is not None and len(result.ids[0]) > 0:
-                docs = [
-                    Document(
-                        page_content=result.documents[0][idx],
-                        metadata=result.metadatas[0][idx],
-                    )
-                    for idx, id in enumerate(result.ids[0])
-                ]
-            else:
-                docs = [
-                    Document(
-                        page_content=file.data.get("content", ""),
-                        metadata={
-                            "name": file.meta.get("name", file.filename),
-                            "created_by": file.user_id,
-                            "file_id": file.id,
-                            **file.meta,
-                        },
-                    )
-                ]
-
-            text_content = file.data.get("content", "")
-        else:
-            # Process the file and save the content
-            # Usage: /files/
-            file_path = file.path
-            if file_path:
-                file_path = Storage.get_file(file_path)
-                loader = Loader(
-                    engine=app.state.config.CONTENT_EXTRACTION_ENGINE,
-                    TIKA_SERVER_URL=app.state.config.TIKA_SERVER_URL,
-                    PDF_EXTRACT_IMAGES=app.state.config.PDF_EXTRACT_IMAGES,
-                )
-                docs = loader.load(
-                    file.filename, file.meta.get("content_type"), file_path
-                )
-            else:
-                docs = [
-                    Document(
-                        page_content=file.data.get("content", ""),
-                        metadata={
-                            "name": file.filename,
-                            "created_by": file.user_id,
-                            "file_id": file.id,
-                            **file.meta,
-                        },
-                    )
-                ]
-            text_content = " ".join([doc.page_content for doc in docs])
-
-        log.debug(f"text_content: {text_content}")
-        Files.update_file_data_by_id(
-            file.id,
-            {"content": text_content},
-        )
-
-        hash = calculate_sha256_string(text_content)
-        Files.update_file_hash_by_id(file.id, hash)
-
-        try:
-            result = save_docs_to_vector_db(
-                docs=docs,
-                collection_name=collection_name,
-                metadata={
-                    "file_id": file.id,
-                    "name": file.meta.get("name", file.filename),
-                    "hash": hash,
-                },
-                add=(True if form_data.collection_name else False),
-            )
-
-            if result:
-                Files.update_file_metadata_by_id(
-                    file.id,
-                    {
-                        "collection_name": collection_name,
-                    },
-                )
-
-                return {
-                    "status": True,
-                    "collection_name": collection_name,
-                    "filename": file.meta.get("name", file.filename),
-                    "content": text_content,
-                }
-        except Exception as e:
-            raise e
-    except Exception as e:
-        log.exception(e)
-        if "No pandoc was found" in str(e):
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
-            )
-        else:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=str(e),
-            )
-
-
-class ProcessTextForm(BaseModel):
-    name: str
-    content: str
-    collection_name: Optional[str] = None
-
-
-@app.post("/process/text")
-def process_text(
-    form_data: ProcessTextForm,
-    user=Depends(get_verified_user),
-):
-    collection_name = form_data.collection_name
-    if collection_name is None:
-        collection_name = calculate_sha256_string(form_data.content)
-
-    docs = [
-        Document(
-            page_content=form_data.content,
-            metadata={"name": form_data.name, "created_by": user.id},
-        )
-    ]
-    text_content = form_data.content
-    log.debug(f"text_content: {text_content}")
-
-    result = save_docs_to_vector_db(docs, collection_name)
-
-    if result:
-        return {
-            "status": True,
-            "collection_name": collection_name,
-            "content": text_content,
-        }
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=ERROR_MESSAGES.DEFAULT(),
-        )
-
-
-@app.post("/process/youtube")
-def process_youtube_video(form_data: ProcessUrlForm, user=Depends(get_verified_user)):
-    try:
-        collection_name = form_data.collection_name
-        if not collection_name:
-            collection_name = calculate_sha256_string(form_data.url)[:63]
-
-        loader = YoutubeLoader.from_youtube_url(
-            form_data.url,
-            add_video_info=True,
-            language=app.state.config.YOUTUBE_LOADER_LANGUAGE,
-            translation=app.state.YOUTUBE_LOADER_TRANSLATION,
-        )
-        docs = loader.load()
-        content = " ".join([doc.page_content for doc in docs])
-        log.debug(f"text_content: {content}")
-        save_docs_to_vector_db(docs, collection_name, overwrite=True)
-
-        return {
-            "status": True,
-            "collection_name": collection_name,
-            "filename": form_data.url,
-            "file": {
-                "data": {
-                    "content": content,
-                },
-                "meta": {
-                    "name": form_data.url,
-                },
-            },
-        }
-    except Exception as e:
-        log.exception(e)
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-@app.post("/process/web")
-def process_web(form_data: ProcessUrlForm, user=Depends(get_verified_user)):
-    try:
-        collection_name = form_data.collection_name
-        if not collection_name:
-            collection_name = calculate_sha256_string(form_data.url)[:63]
-
-        loader = get_web_loader(
-            form_data.url,
-            verify_ssl=app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
-            requests_per_second=app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
-        )
-        docs = loader.load()
-        content = " ".join([doc.page_content for doc in docs])
-        log.debug(f"text_content: {content}")
-        save_docs_to_vector_db(docs, collection_name, overwrite=True)
-
-        return {
-            "status": True,
-            "collection_name": collection_name,
-            "filename": form_data.url,
-            "file": {
-                "data": {
-                    "content": content,
-                },
-                "meta": {
-                    "name": form_data.url,
-                },
-            },
-        }
-    except Exception as e:
-        log.exception(e)
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-def search_web(engine: str, query: str) -> list[SearchResult]:
-    """Search the web using a search engine and return the results as a list of SearchResult objects.
-    Will look for a search engine API key in environment variables in the following order:
-    - SEARXNG_QUERY_URL
-    - GOOGLE_PSE_API_KEY + GOOGLE_PSE_ENGINE_ID
-    - BRAVE_SEARCH_API_KEY
-    - SERPSTACK_API_KEY
-    - SERPER_API_KEY
-    - SERPLY_API_KEY
-    - TAVILY_API_KEY
-    - SEARCHAPI_API_KEY + SEARCHAPI_ENGINE (by default `google`)
-    Args:
-        query (str): The query to search for
-    """
-
-    # TODO: add playwright to search the web
-    if engine == "searxng":
-        if app.state.config.SEARXNG_QUERY_URL:
-            return search_searxng(
-                app.state.config.SEARXNG_QUERY_URL,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-            )
-        else:
-            raise Exception("No SEARXNG_QUERY_URL found in environment variables")
-    elif engine == "google_pse":
-        if (
-            app.state.config.GOOGLE_PSE_API_KEY
-            and app.state.config.GOOGLE_PSE_ENGINE_ID
-        ):
-            return search_google_pse(
-                app.state.config.GOOGLE_PSE_API_KEY,
-                app.state.config.GOOGLE_PSE_ENGINE_ID,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-            )
-        else:
-            raise Exception(
-                "No GOOGLE_PSE_API_KEY or GOOGLE_PSE_ENGINE_ID found in environment variables"
-            )
-    elif engine == "brave":
-        if app.state.config.BRAVE_SEARCH_API_KEY:
-            return search_brave(
-                app.state.config.BRAVE_SEARCH_API_KEY,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-            )
-        else:
-            raise Exception("No BRAVE_SEARCH_API_KEY found in environment variables")
-    elif engine == "serpstack":
-        if app.state.config.SERPSTACK_API_KEY:
-            return search_serpstack(
-                app.state.config.SERPSTACK_API_KEY,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-                https_enabled=app.state.config.SERPSTACK_HTTPS,
-            )
-        else:
-            raise Exception("No SERPSTACK_API_KEY found in environment variables")
-    elif engine == "serper":
-        if app.state.config.SERPER_API_KEY:
-            return search_serper(
-                app.state.config.SERPER_API_KEY,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-            )
-        else:
-            raise Exception("No SERPER_API_KEY found in environment variables")
-    elif engine == "serply":
-        if app.state.config.SERPLY_API_KEY:
-            return search_serply(
-                app.state.config.SERPLY_API_KEY,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-            )
-        else:
-            raise Exception("No SERPLY_API_KEY found in environment variables")
-    elif engine == "duckduckgo":
-        return search_duckduckgo(
-            query,
-            app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-            app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-        )
-    elif engine == "tavily":
-        if app.state.config.TAVILY_API_KEY:
-            return search_tavily(
-                app.state.config.TAVILY_API_KEY,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-            )
-        else:
-            raise Exception("No TAVILY_API_KEY found in environment variables")
-    elif engine == "searchapi":
-        if app.state.config.SEARCHAPI_API_KEY:
-            return search_searchapi(
-                app.state.config.SEARCHAPI_API_KEY,
-                app.state.config.SEARCHAPI_ENGINE,
-                query,
-                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
-                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
-            )
-        else:
-            raise Exception("No SEARCHAPI_API_KEY found in environment variables")
-    elif engine == "jina":
-        return search_jina(query, app.state.config.RAG_WEB_SEARCH_RESULT_COUNT)
-    else:
-        raise Exception("No search engine API key found in environment variables")
-
-
-@app.post("/process/web/search")
-def process_web_search(form_data: SearchForm, user=Depends(get_verified_user)):
-    try:
-        logging.info(
-            f"trying to web search with {app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query}"
-        )
-        web_results = search_web(
-            app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query
-        )
-    except Exception as e:
-        log.exception(e)
-
-        print(e)
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.WEB_SEARCH_ERROR(e),
-        )
-
-    try:
-        collection_name = form_data.collection_name
-        if collection_name == "":
-            collection_name = calculate_sha256_string(form_data.query)[:63]
-
-        urls = [result.link for result in web_results]
-
-        loader = get_web_loader(urls)
-        docs = loader.load()
-
-        save_docs_to_vector_db(docs, collection_name, overwrite=True)
-
-        return {
-            "status": True,
-            "collection_name": collection_name,
-            "filenames": urls,
-        }
-    except Exception as e:
-        log.exception(e)
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-class QueryDocForm(BaseModel):
-    collection_name: str
-    query: str
-    k: Optional[int] = None
-    r: Optional[float] = None
-    hybrid: Optional[bool] = None
-
-
-@app.post("/query/doc")
-def query_doc_handler(
-    form_data: QueryDocForm,
-    user=Depends(get_verified_user),
-):
-    try:
-        if app.state.config.ENABLE_RAG_HYBRID_SEARCH:
-            return query_doc_with_hybrid_search(
-                collection_name=form_data.collection_name,
-                query=form_data.query,
-                embedding_function=app.state.EMBEDDING_FUNCTION,
-                k=form_data.k if form_data.k else app.state.config.TOP_K,
-                reranking_function=app.state.sentence_transformer_rf,
-                r=(
-                    form_data.r if form_data.r else app.state.config.RELEVANCE_THRESHOLD
-                ),
-            )
-        else:
-            return query_doc(
-                collection_name=form_data.collection_name,
-                query=form_data.query,
-                embedding_function=app.state.EMBEDDING_FUNCTION,
-                k=form_data.k if form_data.k else app.state.config.TOP_K,
-            )
-    except Exception as e:
-        log.exception(e)
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-class QueryCollectionsForm(BaseModel):
-    collection_names: list[str]
-    query: str
-    k: Optional[int] = None
-    r: Optional[float] = None
-    hybrid: Optional[bool] = None
-
-
-@app.post("/query/collection")
-def query_collection_handler(
-    form_data: QueryCollectionsForm,
-    user=Depends(get_verified_user),
-):
-    try:
-        if app.state.config.ENABLE_RAG_HYBRID_SEARCH:
-            return query_collection_with_hybrid_search(
-                collection_names=form_data.collection_names,
-                query=form_data.query,
-                embedding_function=app.state.EMBEDDING_FUNCTION,
-                k=form_data.k if form_data.k else app.state.config.TOP_K,
-                reranking_function=app.state.sentence_transformer_rf,
-                r=(
-                    form_data.r if form_data.r else app.state.config.RELEVANCE_THRESHOLD
-                ),
-            )
-        else:
-            return query_collection(
-                collection_names=form_data.collection_names,
-                query=form_data.query,
-                embedding_function=app.state.EMBEDDING_FUNCTION,
-                k=form_data.k if form_data.k else app.state.config.TOP_K,
-            )
-
-    except Exception as e:
-        log.exception(e)
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.DEFAULT(e),
-        )
-
-
-####################################
-#
-# Vector DB operations
-#
-####################################
-
-
-class DeleteForm(BaseModel):
-    collection_name: str
-    file_id: str
-
-
-@app.post("/delete")
-def delete_entries_from_collection(form_data: DeleteForm, user=Depends(get_admin_user)):
-    try:
-        if VECTOR_DB_CLIENT.has_collection(collection_name=form_data.collection_name):
-            file = Files.get_file_by_id(form_data.file_id)
-            hash = file.hash
-
-            VECTOR_DB_CLIENT.delete(
-                collection_name=form_data.collection_name,
-                metadata={"hash": hash},
-            )
-            return {"status": True}
-        else:
-            return {"status": False}
-    except Exception as e:
-        log.exception(e)
-        return {"status": False}
-
-
-@app.post("/reset/db")
-def reset_vector_db(user=Depends(get_admin_user)):
-    VECTOR_DB_CLIENT.reset()
-    Knowledges.delete_all_knowledge()
-
-
-@app.post("/reset/uploads")
-def reset_upload_dir(user=Depends(get_admin_user)) -> bool:
-    folder = f"{UPLOAD_DIR}"
-    try:
-        # Check if the directory exists
-        if os.path.exists(folder):
-            # Iterate over all the files and directories in the specified directory
-            for filename in os.listdir(folder):
-                file_path = os.path.join(folder, filename)
-                try:
-                    if os.path.isfile(file_path) or os.path.islink(file_path):
-                        os.unlink(file_path)  # Remove the file or link
-                    elif os.path.isdir(file_path):
-                        shutil.rmtree(file_path)  # Remove the directory
-                except Exception as e:
-                    print(f"Failed to delete {file_path}. Reason: {e}")
-        else:
-            print(f"The directory {folder} does not exist")
-    except Exception as e:
-        print(f"Failed to process the directory {folder}. Reason: {e}")
-    return True
-
-
-if ENV == "dev":
-
-    @app.get("/ef")
-    async def get_embeddings():
-        return {"result": app.state.EMBEDDING_FUNCTION("hello world")}
-
-    @app.get("/ef/{text}")
-    async def get_embeddings_text(text: str):
-        return {"result": app.state.EMBEDDING_FUNCTION(text)}
+# TODO: Merge this with the webui_app and make it a single app
+
+import json
+import logging
+import mimetypes
+import os
+import shutil
+
+import uuid
+from datetime import datetime
+from pathlib import Path
+from typing import Iterator, Optional, Sequence, Union
+
+from fastapi import Depends, FastAPI, File, Form, HTTPException, UploadFile, status
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+
+from open_webui.storage.provider import Storage
+from open_webui.apps.webui.models.knowledge import Knowledges
+from open_webui.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
+
+# Document loaders
+from open_webui.apps.retrieval.loaders.main import Loader
+
+# Web search engines
+from open_webui.apps.retrieval.web.main import SearchResult
+from open_webui.apps.retrieval.web.utils import get_web_loader
+from open_webui.apps.retrieval.web.brave import search_brave
+from open_webui.apps.retrieval.web.duckduckgo import search_duckduckgo
+from open_webui.apps.retrieval.web.google_pse import search_google_pse
+from open_webui.apps.retrieval.web.jina_search import search_jina
+from open_webui.apps.retrieval.web.searchapi import search_searchapi
+from open_webui.apps.retrieval.web.searxng import search_searxng
+from open_webui.apps.retrieval.web.serper import search_serper
+from open_webui.apps.retrieval.web.serply import search_serply
+from open_webui.apps.retrieval.web.serpstack import search_serpstack
+from open_webui.apps.retrieval.web.tavily import search_tavily
+
+
+from open_webui.apps.retrieval.utils import (
+    get_embedding_function,
+    get_model_path,
+    query_collection,
+    query_collection_with_hybrid_search,
+    query_doc,
+    query_doc_with_hybrid_search,
+)
+
+from open_webui.apps.webui.models.files import Files
+from open_webui.config import (
+    BRAVE_SEARCH_API_KEY,
+    TIKTOKEN_ENCODING_NAME,
+    RAG_TEXT_SPLITTER,
+    CHUNK_OVERLAP,
+    CHUNK_SIZE,
+    CONTENT_EXTRACTION_ENGINE,
+    CORS_ALLOW_ORIGIN,
+    ENABLE_RAG_HYBRID_SEARCH,
+    ENABLE_RAG_LOCAL_WEB_FETCH,
+    ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
+    ENABLE_RAG_WEB_SEARCH,
+    ENV,
+    GOOGLE_PSE_API_KEY,
+    GOOGLE_PSE_ENGINE_ID,
+    PDF_EXTRACT_IMAGES,
+    RAG_EMBEDDING_ENGINE,
+    RAG_EMBEDDING_MODEL,
+    RAG_EMBEDDING_MODEL_AUTO_UPDATE,
+    RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
+    RAG_EMBEDDING_BATCH_SIZE,
+    RAG_FILE_MAX_COUNT,
+    RAG_FILE_MAX_SIZE,
+    RAG_OPENAI_API_BASE_URL,
+    RAG_OPENAI_API_KEY,
+    RAG_RELEVANCE_THRESHOLD,
+    RAG_RERANKING_MODEL,
+    RAG_RERANKING_MODEL_AUTO_UPDATE,
+    RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
+    DEFAULT_RAG_TEMPLATE,
+    RAG_TEMPLATE,
+    RAG_TOP_K,
+    RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
+    RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+    RAG_WEB_SEARCH_ENGINE,
+    RAG_WEB_SEARCH_RESULT_COUNT,
+    SEARCHAPI_API_KEY,
+    SEARCHAPI_ENGINE,
+    SEARXNG_QUERY_URL,
+    SERPER_API_KEY,
+    SERPLY_API_KEY,
+    SERPSTACK_API_KEY,
+    SERPSTACK_HTTPS,
+    TAVILY_API_KEY,
+    TIKA_SERVER_URL,
+    UPLOAD_DIR,
+    YOUTUBE_LOADER_LANGUAGE,
+    AppConfig,
+)
+from open_webui.constants import ERROR_MESSAGES
+from open_webui.env import SRC_LOG_LEVELS, DEVICE_TYPE, DOCKER
+from open_webui.utils.misc import (
+    calculate_sha256,
+    calculate_sha256_string,
+    extract_folders_after_data_docs,
+    sanitize_filename,
+)
+from open_webui.utils.utils import get_admin_user, get_verified_user
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
+from langchain_community.document_loaders import (
+    YoutubeLoader,
+)
+from langchain_core.documents import Document
+
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+app = FastAPI()
+
+app.state.config = AppConfig()
+
+app.state.config.TOP_K = RAG_TOP_K
+app.state.config.RELEVANCE_THRESHOLD = RAG_RELEVANCE_THRESHOLD
+app.state.config.FILE_MAX_SIZE = RAG_FILE_MAX_SIZE
+app.state.config.FILE_MAX_COUNT = RAG_FILE_MAX_COUNT
+
+app.state.config.ENABLE_RAG_HYBRID_SEARCH = ENABLE_RAG_HYBRID_SEARCH
+app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
+    ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION
+)
+
+app.state.config.CONTENT_EXTRACTION_ENGINE = CONTENT_EXTRACTION_ENGINE
+app.state.config.TIKA_SERVER_URL = TIKA_SERVER_URL
+
+app.state.config.TEXT_SPLITTER = RAG_TEXT_SPLITTER
+app.state.config.TIKTOKEN_ENCODING_NAME = TIKTOKEN_ENCODING_NAME
+
+app.state.config.CHUNK_SIZE = CHUNK_SIZE
+app.state.config.CHUNK_OVERLAP = CHUNK_OVERLAP
+
+app.state.config.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
+app.state.config.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
+app.state.config.RAG_EMBEDDING_BATCH_SIZE = RAG_EMBEDDING_BATCH_SIZE
+app.state.config.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL
+app.state.config.RAG_TEMPLATE = RAG_TEMPLATE
+
+app.state.config.OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
+app.state.config.OPENAI_API_KEY = RAG_OPENAI_API_KEY
+
+app.state.config.PDF_EXTRACT_IMAGES = PDF_EXTRACT_IMAGES
+
+app.state.config.YOUTUBE_LOADER_LANGUAGE = YOUTUBE_LOADER_LANGUAGE
+app.state.YOUTUBE_LOADER_TRANSLATION = None
+
+
+app.state.config.ENABLE_RAG_WEB_SEARCH = ENABLE_RAG_WEB_SEARCH
+app.state.config.RAG_WEB_SEARCH_ENGINE = RAG_WEB_SEARCH_ENGINE
+app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST = RAG_WEB_SEARCH_DOMAIN_FILTER_LIST
+
+app.state.config.SEARXNG_QUERY_URL = SEARXNG_QUERY_URL
+app.state.config.GOOGLE_PSE_API_KEY = GOOGLE_PSE_API_KEY
+app.state.config.GOOGLE_PSE_ENGINE_ID = GOOGLE_PSE_ENGINE_ID
+app.state.config.BRAVE_SEARCH_API_KEY = BRAVE_SEARCH_API_KEY
+app.state.config.SERPSTACK_API_KEY = SERPSTACK_API_KEY
+app.state.config.SERPSTACK_HTTPS = SERPSTACK_HTTPS
+app.state.config.SERPER_API_KEY = SERPER_API_KEY
+app.state.config.SERPLY_API_KEY = SERPLY_API_KEY
+app.state.config.TAVILY_API_KEY = TAVILY_API_KEY
+app.state.config.SEARCHAPI_API_KEY = SEARCHAPI_API_KEY
+app.state.config.SEARCHAPI_ENGINE = SEARCHAPI_ENGINE
+app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = RAG_WEB_SEARCH_RESULT_COUNT
+app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = RAG_WEB_SEARCH_CONCURRENT_REQUESTS
+
+
+def update_embedding_model(
+    embedding_model: str,
+    auto_update: bool = False,
+):
+    if embedding_model and app.state.config.RAG_EMBEDDING_ENGINE == "":
+        from sentence_transformers import SentenceTransformer
+
+        app.state.sentence_transformer_ef = SentenceTransformer(
+            get_model_path(embedding_model, auto_update),
+            device=DEVICE_TYPE,
+            trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
+        )
+    else:
+        app.state.sentence_transformer_ef = None
+
+
+def update_reranking_model(
+    reranking_model: str,
+    auto_update: bool = False,
+):
+    if reranking_model:
+        if any(model in reranking_model for model in ["jinaai/jina-colbert-v2"]):
+            try:
+                from open_webui.apps.retrieval.models.colbert import ColBERT
+
+                app.state.sentence_transformer_rf = ColBERT(
+                    get_model_path(reranking_model, auto_update),
+                    env="docker" if DOCKER else None,
+                )
+            except Exception as e:
+                log.error(f"ColBERT: {e}")
+                app.state.sentence_transformer_rf = None
+                app.state.config.ENABLE_RAG_HYBRID_SEARCH = False
+        else:
+            import sentence_transformers
+
+            try:
+                app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
+                    get_model_path(reranking_model, auto_update),
+                    device=DEVICE_TYPE,
+                    trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
+                )
+            except:
+                log.error("CrossEncoder error")
+                app.state.sentence_transformer_rf = None
+                app.state.config.ENABLE_RAG_HYBRID_SEARCH = False
+    else:
+        app.state.sentence_transformer_rf = None
+
+
+update_embedding_model(
+    app.state.config.RAG_EMBEDDING_MODEL,
+    RAG_EMBEDDING_MODEL_AUTO_UPDATE,
+)
+
+update_reranking_model(
+    app.state.config.RAG_RERANKING_MODEL,
+    RAG_RERANKING_MODEL_AUTO_UPDATE,
+)
+
+
+app.state.EMBEDDING_FUNCTION = get_embedding_function(
+    app.state.config.RAG_EMBEDDING_ENGINE,
+    app.state.config.RAG_EMBEDDING_MODEL,
+    app.state.sentence_transformer_ef,
+    app.state.config.OPENAI_API_KEY,
+    app.state.config.OPENAI_API_BASE_URL,
+    app.state.config.RAG_EMBEDDING_BATCH_SIZE,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ALLOW_ORIGIN,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class CollectionNameForm(BaseModel):
+    collection_name: Optional[str] = None
+
+
+class ProcessUrlForm(CollectionNameForm):
+    url: str
+
+
+class SearchForm(CollectionNameForm):
+    query: str
+
+
+@app.get("/")
+async def get_status():
+    return {
+        "status": True,
+        "chunk_size": app.state.config.CHUNK_SIZE,
+        "chunk_overlap": app.state.config.CHUNK_OVERLAP,
+        "template": app.state.config.RAG_TEMPLATE,
+        "embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
+        "embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
+        "reranking_model": app.state.config.RAG_RERANKING_MODEL,
+        "embedding_batch_size": app.state.config.RAG_EMBEDDING_BATCH_SIZE,
+    }
+
+
+@app.get("/embedding")
+async def get_embedding_config(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
+        "embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
+        "embedding_batch_size": app.state.config.RAG_EMBEDDING_BATCH_SIZE,
+        "openai_config": {
+            "url": app.state.config.OPENAI_API_BASE_URL,
+            "key": app.state.config.OPENAI_API_KEY,
+        },
+    }
+
+
+@app.get("/reranking")
+async def get_reraanking_config(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "reranking_model": app.state.config.RAG_RERANKING_MODEL,
+    }
+
+
+class OpenAIConfigForm(BaseModel):
+    url: str
+    key: str
+
+
+class EmbeddingModelUpdateForm(BaseModel):
+    openai_config: Optional[OpenAIConfigForm] = None
+    embedding_engine: str
+    embedding_model: str
+    embedding_batch_size: Optional[int] = 1
+
+
+@app.post("/embedding/update")
+async def update_embedding_config(
+    form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
+):
+    log.info(
+        f"Updating embedding model: {app.state.config.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
+    )
+    try:
+        app.state.config.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
+        app.state.config.RAG_EMBEDDING_MODEL = form_data.embedding_model
+
+        if app.state.config.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]:
+            if form_data.openai_config is not None:
+                app.state.config.OPENAI_API_BASE_URL = form_data.openai_config.url
+                app.state.config.OPENAI_API_KEY = form_data.openai_config.key
+            app.state.config.RAG_EMBEDDING_BATCH_SIZE = form_data.embedding_batch_size
+
+        update_embedding_model(app.state.config.RAG_EMBEDDING_MODEL)
+
+        app.state.EMBEDDING_FUNCTION = get_embedding_function(
+            app.state.config.RAG_EMBEDDING_ENGINE,
+            app.state.config.RAG_EMBEDDING_MODEL,
+            app.state.sentence_transformer_ef,
+            app.state.config.OPENAI_API_KEY,
+            app.state.config.OPENAI_API_BASE_URL,
+            app.state.config.RAG_EMBEDDING_BATCH_SIZE,
+        )
+
+        return {
+            "status": True,
+            "embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
+            "embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
+            "embedding_batch_size": app.state.config.RAG_EMBEDDING_BATCH_SIZE,
+            "openai_config": {
+                "url": app.state.config.OPENAI_API_BASE_URL,
+                "key": app.state.config.OPENAI_API_KEY,
+            },
+        }
+    except Exception as e:
+        log.exception(f"Problem updating embedding model: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+class RerankingModelUpdateForm(BaseModel):
+    reranking_model: str
+
+
+@app.post("/reranking/update")
+async def update_reranking_config(
+    form_data: RerankingModelUpdateForm, user=Depends(get_admin_user)
+):
+    log.info(
+        f"Updating reranking model: {app.state.config.RAG_RERANKING_MODEL} to {form_data.reranking_model}"
+    )
+    try:
+        app.state.config.RAG_RERANKING_MODEL = form_data.reranking_model
+
+        update_reranking_model(app.state.config.RAG_RERANKING_MODEL, True)
+
+        return {
+            "status": True,
+            "reranking_model": app.state.config.RAG_RERANKING_MODEL,
+        }
+    except Exception as e:
+        log.exception(f"Problem updating reranking model: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+@app.get("/config")
+async def get_rag_config(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
+        "content_extraction": {
+            "engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
+            "tika_server_url": app.state.config.TIKA_SERVER_URL,
+        },
+        "chunk": {
+            "text_splitter": app.state.config.TEXT_SPLITTER,
+            "chunk_size": app.state.config.CHUNK_SIZE,
+            "chunk_overlap": app.state.config.CHUNK_OVERLAP,
+        },
+        "file": {
+            "max_size": app.state.config.FILE_MAX_SIZE,
+            "max_count": app.state.config.FILE_MAX_COUNT,
+        },
+        "youtube": {
+            "language": app.state.config.YOUTUBE_LOADER_LANGUAGE,
+            "translation": app.state.YOUTUBE_LOADER_TRANSLATION,
+        },
+        "web": {
+            "ssl_verification": app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
+            "search": {
+                "enabled": app.state.config.ENABLE_RAG_WEB_SEARCH,
+                "engine": app.state.config.RAG_WEB_SEARCH_ENGINE,
+                "searxng_query_url": app.state.config.SEARXNG_QUERY_URL,
+                "google_pse_api_key": app.state.config.GOOGLE_PSE_API_KEY,
+                "google_pse_engine_id": app.state.config.GOOGLE_PSE_ENGINE_ID,
+                "brave_search_api_key": app.state.config.BRAVE_SEARCH_API_KEY,
+                "serpstack_api_key": app.state.config.SERPSTACK_API_KEY,
+                "serpstack_https": app.state.config.SERPSTACK_HTTPS,
+                "serper_api_key": app.state.config.SERPER_API_KEY,
+                "serply_api_key": app.state.config.SERPLY_API_KEY,
+                "tavily_api_key": app.state.config.TAVILY_API_KEY,
+                "searchapi_api_key": app.state.config.SEARCHAPI_API_KEY,
+                "seaarchapi_engine": app.state.config.SEARCHAPI_ENGINE,
+                "result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                "concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
+            },
+        },
+    }
+
+
+class FileConfig(BaseModel):
+    max_size: Optional[int] = None
+    max_count: Optional[int] = None
+
+
+class ContentExtractionConfig(BaseModel):
+    engine: str = ""
+    tika_server_url: Optional[str] = None
+
+
+class ChunkParamUpdateForm(BaseModel):
+    text_splitter: Optional[str] = None
+    chunk_size: int
+    chunk_overlap: int
+
+
+class YoutubeLoaderConfig(BaseModel):
+    language: list[str]
+    translation: Optional[str] = None
+
+
+class WebSearchConfig(BaseModel):
+    enabled: bool
+    engine: Optional[str] = None
+    searxng_query_url: Optional[str] = None
+    google_pse_api_key: Optional[str] = None
+    google_pse_engine_id: Optional[str] = None
+    brave_search_api_key: Optional[str] = None
+    serpstack_api_key: Optional[str] = None
+    serpstack_https: Optional[bool] = None
+    serper_api_key: Optional[str] = None
+    serply_api_key: Optional[str] = None
+    tavily_api_key: Optional[str] = None
+    searchapi_api_key: Optional[str] = None
+    searchapi_engine: Optional[str] = None
+    result_count: Optional[int] = None
+    concurrent_requests: Optional[int] = None
+
+
+class WebConfig(BaseModel):
+    search: WebSearchConfig
+    web_loader_ssl_verification: Optional[bool] = None
+
+
+class ConfigUpdateForm(BaseModel):
+    pdf_extract_images: Optional[bool] = None
+    file: Optional[FileConfig] = None
+    content_extraction: Optional[ContentExtractionConfig] = None
+    chunk: Optional[ChunkParamUpdateForm] = None
+    youtube: Optional[YoutubeLoaderConfig] = None
+    web: Optional[WebConfig] = None
+
+
+@app.post("/config/update")
+async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_user)):
+    app.state.config.PDF_EXTRACT_IMAGES = (
+        form_data.pdf_extract_images
+        if form_data.pdf_extract_images is not None
+        else app.state.config.PDF_EXTRACT_IMAGES
+    )
+
+    if form_data.file is not None:
+        app.state.config.FILE_MAX_SIZE = form_data.file.max_size
+        app.state.config.FILE_MAX_COUNT = form_data.file.max_count
+
+    if form_data.content_extraction is not None:
+        log.info(f"Updating text settings: {form_data.content_extraction}")
+        app.state.config.CONTENT_EXTRACTION_ENGINE = form_data.content_extraction.engine
+        app.state.config.TIKA_SERVER_URL = form_data.content_extraction.tika_server_url
+
+    if form_data.chunk is not None:
+        app.state.config.TEXT_SPLITTER = form_data.chunk.text_splitter
+        app.state.config.CHUNK_SIZE = form_data.chunk.chunk_size
+        app.state.config.CHUNK_OVERLAP = form_data.chunk.chunk_overlap
+
+    if form_data.youtube is not None:
+        app.state.config.YOUTUBE_LOADER_LANGUAGE = form_data.youtube.language
+        app.state.YOUTUBE_LOADER_TRANSLATION = form_data.youtube.translation
+
+    if form_data.web is not None:
+        app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
+            form_data.web.web_loader_ssl_verification
+        )
+
+        app.state.config.ENABLE_RAG_WEB_SEARCH = form_data.web.search.enabled
+        app.state.config.RAG_WEB_SEARCH_ENGINE = form_data.web.search.engine
+        app.state.config.SEARXNG_QUERY_URL = form_data.web.search.searxng_query_url
+        app.state.config.GOOGLE_PSE_API_KEY = form_data.web.search.google_pse_api_key
+        app.state.config.GOOGLE_PSE_ENGINE_ID = (
+            form_data.web.search.google_pse_engine_id
+        )
+        app.state.config.BRAVE_SEARCH_API_KEY = (
+            form_data.web.search.brave_search_api_key
+        )
+        app.state.config.SERPSTACK_API_KEY = form_data.web.search.serpstack_api_key
+        app.state.config.SERPSTACK_HTTPS = form_data.web.search.serpstack_https
+        app.state.config.SERPER_API_KEY = form_data.web.search.serper_api_key
+        app.state.config.SERPLY_API_KEY = form_data.web.search.serply_api_key
+        app.state.config.TAVILY_API_KEY = form_data.web.search.tavily_api_key
+        app.state.config.SEARCHAPI_API_KEY = form_data.web.search.searchapi_api_key
+        app.state.config.SEARCHAPI_ENGINE = form_data.web.search.searchapi_engine
+        app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = form_data.web.search.result_count
+        app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = (
+            form_data.web.search.concurrent_requests
+        )
+
+    return {
+        "status": True,
+        "pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
+        "file": {
+            "max_size": app.state.config.FILE_MAX_SIZE,
+            "max_count": app.state.config.FILE_MAX_COUNT,
+        },
+        "content_extraction": {
+            "engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
+            "tika_server_url": app.state.config.TIKA_SERVER_URL,
+        },
+        "chunk": {
+            "text_splitter": app.state.config.TEXT_SPLITTER,
+            "chunk_size": app.state.config.CHUNK_SIZE,
+            "chunk_overlap": app.state.config.CHUNK_OVERLAP,
+        },
+        "youtube": {
+            "language": app.state.config.YOUTUBE_LOADER_LANGUAGE,
+            "translation": app.state.YOUTUBE_LOADER_TRANSLATION,
+        },
+        "web": {
+            "ssl_verification": app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
+            "search": {
+                "enabled": app.state.config.ENABLE_RAG_WEB_SEARCH,
+                "engine": app.state.config.RAG_WEB_SEARCH_ENGINE,
+                "searxng_query_url": app.state.config.SEARXNG_QUERY_URL,
+                "google_pse_api_key": app.state.config.GOOGLE_PSE_API_KEY,
+                "google_pse_engine_id": app.state.config.GOOGLE_PSE_ENGINE_ID,
+                "brave_search_api_key": app.state.config.BRAVE_SEARCH_API_KEY,
+                "serpstack_api_key": app.state.config.SERPSTACK_API_KEY,
+                "serpstack_https": app.state.config.SERPSTACK_HTTPS,
+                "serper_api_key": app.state.config.SERPER_API_KEY,
+                "serply_api_key": app.state.config.SERPLY_API_KEY,
+                "serachapi_api_key": app.state.config.SEARCHAPI_API_KEY,
+                "searchapi_engine": app.state.config.SEARCHAPI_ENGINE,
+                "tavily_api_key": app.state.config.TAVILY_API_KEY,
+                "result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                "concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
+            },
+        },
+    }
+
+
+@app.get("/template")
+async def get_rag_template(user=Depends(get_verified_user)):
+    return {
+        "status": True,
+        "template": app.state.config.RAG_TEMPLATE,
+    }
+
+
+@app.get("/query/settings")
+async def get_query_settings(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "template": app.state.config.RAG_TEMPLATE,
+        "k": app.state.config.TOP_K,
+        "r": app.state.config.RELEVANCE_THRESHOLD,
+        "hybrid": app.state.config.ENABLE_RAG_HYBRID_SEARCH,
+    }
+
+
+class QuerySettingsForm(BaseModel):
+    k: Optional[int] = None
+    r: Optional[float] = None
+    template: Optional[str] = None
+    hybrid: Optional[bool] = None
+
+
+@app.post("/query/settings/update")
+async def update_query_settings(
+    form_data: QuerySettingsForm, user=Depends(get_admin_user)
+):
+    app.state.config.RAG_TEMPLATE = form_data.template
+    app.state.config.TOP_K = form_data.k if form_data.k else 4
+    app.state.config.RELEVANCE_THRESHOLD = form_data.r if form_data.r else 0.0
+
+    app.state.config.ENABLE_RAG_HYBRID_SEARCH = (
+        form_data.hybrid if form_data.hybrid else False
+    )
+
+    return {
+        "status": True,
+        "template": app.state.config.RAG_TEMPLATE,
+        "k": app.state.config.TOP_K,
+        "r": app.state.config.RELEVANCE_THRESHOLD,
+        "hybrid": app.state.config.ENABLE_RAG_HYBRID_SEARCH,
+    }
+
+
+####################################
+#
+# Document process and retrieval
+#
+####################################
+
+
+def save_docs_to_vector_db(
+    docs,
+    collection_name,
+    metadata: Optional[dict] = None,
+    overwrite: bool = False,
+    split: bool = True,
+    add: bool = False,
+) -> bool:
+    log.info(f"save_docs_to_vector_db {docs} {collection_name}")
+
+    # Check if entries with the same hash (metadata.hash) already exist
+    if metadata and "hash" in metadata:
+        result = VECTOR_DB_CLIENT.query(
+            collection_name=collection_name,
+            filter={"hash": metadata["hash"]},
+        )
+
+        if result is not None:
+            existing_doc_ids = result.ids[0]
+            if existing_doc_ids:
+                log.info(f"Document with hash {metadata['hash']} already exists")
+                raise ValueError(ERROR_MESSAGES.DUPLICATE_CONTENT)
+
+    if split:
+        if app.state.config.TEXT_SPLITTER in ["", "character"]:
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=app.state.config.CHUNK_SIZE,
+                chunk_overlap=app.state.config.CHUNK_OVERLAP,
+                add_start_index=True,
+            )
+        elif app.state.config.TEXT_SPLITTER == "token":
+            text_splitter = TokenTextSplitter(
+                encoding_name=app.state.config.TIKTOKEN_ENCODING_NAME,
+                chunk_size=app.state.config.CHUNK_SIZE,
+                chunk_overlap=app.state.config.CHUNK_OVERLAP,
+                add_start_index=True,
+            )
+        else:
+            raise ValueError(ERROR_MESSAGES.DEFAULT("Invalid text splitter"))
+
+        docs = text_splitter.split_documents(docs)
+
+    if len(docs) == 0:
+        raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)
+
+    texts = [doc.page_content for doc in docs]
+    metadatas = [
+        {
+            **doc.metadata,
+            **(metadata if metadata else {}),
+            "embedding_config": json.dumps(
+                {
+                    "engine": app.state.config.RAG_EMBEDDING_ENGINE,
+                    "model": app.state.config.RAG_EMBEDDING_MODEL,
+                }
+            ),
+        }
+        for doc in docs
+    ]
+
+    # ChromaDB does not like datetime formats
+    # for meta-data so convert them to string.
+    for metadata in metadatas:
+        for key, value in metadata.items():
+            if isinstance(value, datetime):
+                metadata[key] = str(value)
+
+    try:
+        if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
+            log.info(f"collection {collection_name} already exists")
+
+            if overwrite:
+                VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
+                log.info(f"deleting existing collection {collection_name}")
+            elif add is False:
+                log.info(
+                    f"collection {collection_name} already exists, overwrite is False and add is False"
+                )
+                return True
+
+        log.info(f"adding to collection {collection_name}")
+        embedding_function = get_embedding_function(
+            app.state.config.RAG_EMBEDDING_ENGINE,
+            app.state.config.RAG_EMBEDDING_MODEL,
+            app.state.sentence_transformer_ef,
+            app.state.config.OPENAI_API_KEY,
+            app.state.config.OPENAI_API_BASE_URL,
+            app.state.config.RAG_EMBEDDING_BATCH_SIZE,
+        )
+
+        embeddings = embedding_function(
+            list(map(lambda x: x.replace("\n", " "), texts))
+        )
+
+        items = [
+            {
+                "id": str(uuid.uuid4()),
+                "text": text,
+                "vector": embeddings[idx],
+                "metadata": metadatas[idx],
+            }
+            for idx, text in enumerate(texts)
+        ]
+
+        VECTOR_DB_CLIENT.insert(
+            collection_name=collection_name,
+            items=items,
+        )
+
+        return True
+    except Exception as e:
+        log.exception(e)
+        return False
+
+
+class ProcessFileForm(BaseModel):
+    file_id: str
+    content: Optional[str] = None
+    collection_name: Optional[str] = None
+
+
+@app.post("/process/file")
+def process_file(
+    form_data: ProcessFileForm,
+    user=Depends(get_verified_user),
+):
+    try:
+        file = Files.get_file_by_id(form_data.file_id)
+
+        collection_name = form_data.collection_name
+
+        if collection_name is None:
+            collection_name = f"file-{file.id}"
+
+        if form_data.content:
+            # Update the content in the file
+            # Usage: /files/{file_id}/data/content/update
+
+            VECTOR_DB_CLIENT.delete(
+                collection_name=f"file-{file.id}",
+                filter={"file_id": file.id},
+            )
+
+            docs = [
+                Document(
+                    page_content=form_data.content,
+                    metadata={
+                        "name": file.meta.get("name", file.filename),
+                        "created_by": file.user_id,
+                        "file_id": file.id,
+                        **file.meta,
+                    },
+                )
+            ]
+
+            text_content = form_data.content
+        elif form_data.collection_name:
+            # Check if the file has already been processed and save the content
+            # Usage: /knowledge/{id}/file/add, /knowledge/{id}/file/update
+
+            result = VECTOR_DB_CLIENT.query(
+                collection_name=f"file-{file.id}", filter={"file_id": file.id}
+            )
+
+            if result is not None and len(result.ids[0]) > 0:
+                docs = [
+                    Document(
+                        page_content=result.documents[0][idx],
+                        metadata=result.metadatas[0][idx],
+                    )
+                    for idx, id in enumerate(result.ids[0])
+                ]
+            else:
+                docs = [
+                    Document(
+                        page_content=file.data.get("content", ""),
+                        metadata={
+                            "name": file.meta.get("name", file.filename),
+                            "created_by": file.user_id,
+                            "file_id": file.id,
+                            **file.meta,
+                        },
+                    )
+                ]
+
+            text_content = file.data.get("content", "")
+        else:
+            # Process the file and save the content
+            # Usage: /files/
+            file_path = file.path
+            if file_path:
+                file_path = Storage.get_file(file_path)
+                loader = Loader(
+                    engine=app.state.config.CONTENT_EXTRACTION_ENGINE,
+                    TIKA_SERVER_URL=app.state.config.TIKA_SERVER_URL,
+                    PDF_EXTRACT_IMAGES=app.state.config.PDF_EXTRACT_IMAGES,
+                )
+                docs = loader.load(
+                    file.filename, file.meta.get("content_type"), file_path
+                )
+            else:
+                docs = [
+                    Document(
+                        page_content=file.data.get("content", ""),
+                        metadata={
+                            "name": file.filename,
+                            "created_by": file.user_id,
+                            "file_id": file.id,
+                            **file.meta,
+                        },
+                    )
+                ]
+            text_content = " ".join([doc.page_content for doc in docs])
+
+        log.debug(f"text_content: {text_content}")
+        Files.update_file_data_by_id(
+            file.id,
+            {"content": text_content},
+        )
+
+        hash = calculate_sha256_string(text_content)
+        Files.update_file_hash_by_id(file.id, hash)
+
+        try:
+            result = save_docs_to_vector_db(
+                docs=docs,
+                collection_name=collection_name,
+                metadata={
+                    "file_id": file.id,
+                    "name": file.meta.get("name", file.filename),
+                    "hash": hash,
+                },
+                add=(True if form_data.collection_name else False),
+            )
+
+            if result:
+                Files.update_file_metadata_by_id(
+                    file.id,
+                    {
+                        "collection_name": collection_name,
+                    },
+                )
+
+                return {
+                    "status": True,
+                    "collection_name": collection_name,
+                    "filename": file.meta.get("name", file.filename),
+                    "content": text_content,
+                }
+        except Exception as e:
+            raise e
+    except Exception as e:
+        log.exception(e)
+        if "No pandoc was found" in str(e):
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
+            )
+        else:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=str(e),
+            )
+
+
+class ProcessTextForm(BaseModel):
+    name: str
+    content: str
+    collection_name: Optional[str] = None
+
+
+@app.post("/process/text")
+def process_text(
+    form_data: ProcessTextForm,
+    user=Depends(get_verified_user),
+):
+    collection_name = form_data.collection_name
+    if collection_name is None:
+        collection_name = calculate_sha256_string(form_data.content)
+
+    docs = [
+        Document(
+            page_content=form_data.content,
+            metadata={"name": form_data.name, "created_by": user.id},
+        )
+    ]
+    text_content = form_data.content
+    log.debug(f"text_content: {text_content}")
+
+    result = save_docs_to_vector_db(docs, collection_name)
+
+    if result:
+        return {
+            "status": True,
+            "collection_name": collection_name,
+            "content": text_content,
+        }
+    else:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=ERROR_MESSAGES.DEFAULT(),
+        )
+
+
+@app.post("/process/youtube")
+def process_youtube_video(form_data: ProcessUrlForm, user=Depends(get_verified_user)):
+    try:
+        collection_name = form_data.collection_name
+        if not collection_name:
+            collection_name = calculate_sha256_string(form_data.url)[:63]
+
+        loader = YoutubeLoader.from_youtube_url(
+            form_data.url,
+            add_video_info=True,
+            language=app.state.config.YOUTUBE_LOADER_LANGUAGE,
+            translation=app.state.YOUTUBE_LOADER_TRANSLATION,
+        )
+        docs = loader.load()
+        content = " ".join([doc.page_content for doc in docs])
+        log.debug(f"text_content: {content}")
+        save_docs_to_vector_db(docs, collection_name, overwrite=True)
+
+        return {
+            "status": True,
+            "collection_name": collection_name,
+            "filename": form_data.url,
+            "file": {
+                "data": {
+                    "content": content,
+                },
+                "meta": {
+                    "name": form_data.url,
+                },
+            },
+        }
+    except Exception as e:
+        log.exception(e)
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+@app.post("/process/web")
+def process_web(form_data: ProcessUrlForm, user=Depends(get_verified_user)):
+    try:
+        collection_name = form_data.collection_name
+        if not collection_name:
+            collection_name = calculate_sha256_string(form_data.url)[:63]
+
+        loader = get_web_loader(
+            form_data.url,
+            verify_ssl=app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
+            requests_per_second=app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
+        )
+        docs = loader.load()
+        content = " ".join([doc.page_content for doc in docs])
+        log.debug(f"text_content: {content}")
+        save_docs_to_vector_db(docs, collection_name, overwrite=True)
+
+        return {
+            "status": True,
+            "collection_name": collection_name,
+            "filename": form_data.url,
+            "file": {
+                "data": {
+                    "content": content,
+                },
+                "meta": {
+                    "name": form_data.url,
+                },
+            },
+        }
+    except Exception as e:
+        log.exception(e)
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+def search_web(engine: str, query: str) -> list[SearchResult]:
+    """Search the web using a search engine and return the results as a list of SearchResult objects.
+    Will look for a search engine API key in environment variables in the following order:
+    - SEARXNG_QUERY_URL
+    - GOOGLE_PSE_API_KEY + GOOGLE_PSE_ENGINE_ID
+    - BRAVE_SEARCH_API_KEY
+    - SERPSTACK_API_KEY
+    - SERPER_API_KEY
+    - SERPLY_API_KEY
+    - TAVILY_API_KEY
+    - SEARCHAPI_API_KEY + SEARCHAPI_ENGINE (by default `google`)
+    Args:
+        query (str): The query to search for
+    """
+
+    # TODO: add playwright to search the web
+    if engine == "searxng":
+        if app.state.config.SEARXNG_QUERY_URL:
+            return search_searxng(
+                app.state.config.SEARXNG_QUERY_URL,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+            )
+        else:
+            raise Exception("No SEARXNG_QUERY_URL found in environment variables")
+    elif engine == "google_pse":
+        if (
+            app.state.config.GOOGLE_PSE_API_KEY
+            and app.state.config.GOOGLE_PSE_ENGINE_ID
+        ):
+            return search_google_pse(
+                app.state.config.GOOGLE_PSE_API_KEY,
+                app.state.config.GOOGLE_PSE_ENGINE_ID,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+            )
+        else:
+            raise Exception(
+                "No GOOGLE_PSE_API_KEY or GOOGLE_PSE_ENGINE_ID found in environment variables"
+            )
+    elif engine == "brave":
+        if app.state.config.BRAVE_SEARCH_API_KEY:
+            return search_brave(
+                app.state.config.BRAVE_SEARCH_API_KEY,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+            )
+        else:
+            raise Exception("No BRAVE_SEARCH_API_KEY found in environment variables")
+    elif engine == "serpstack":
+        if app.state.config.SERPSTACK_API_KEY:
+            return search_serpstack(
+                app.state.config.SERPSTACK_API_KEY,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+                https_enabled=app.state.config.SERPSTACK_HTTPS,
+            )
+        else:
+            raise Exception("No SERPSTACK_API_KEY found in environment variables")
+    elif engine == "serper":
+        if app.state.config.SERPER_API_KEY:
+            return search_serper(
+                app.state.config.SERPER_API_KEY,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+            )
+        else:
+            raise Exception("No SERPER_API_KEY found in environment variables")
+    elif engine == "serply":
+        if app.state.config.SERPLY_API_KEY:
+            return search_serply(
+                app.state.config.SERPLY_API_KEY,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+            )
+        else:
+            raise Exception("No SERPLY_API_KEY found in environment variables")
+    elif engine == "duckduckgo":
+        return search_duckduckgo(
+            query,
+            app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+            app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+        )
+    elif engine == "tavily":
+        if app.state.config.TAVILY_API_KEY:
+            return search_tavily(
+                app.state.config.TAVILY_API_KEY,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+            )
+        else:
+            raise Exception("No TAVILY_API_KEY found in environment variables")
+    elif engine == "searchapi":
+        if app.state.config.SEARCHAPI_API_KEY:
+            return search_searchapi(
+                app.state.config.SEARCHAPI_API_KEY,
+                app.state.config.SEARCHAPI_ENGINE,
+                query,
+                app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
+                app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
+            )
+        else:
+            raise Exception("No SEARCHAPI_API_KEY found in environment variables")
+    elif engine == "jina":
+        return search_jina(query, app.state.config.RAG_WEB_SEARCH_RESULT_COUNT)
+    else:
+        raise Exception("No search engine API key found in environment variables")
+
+
+@app.post("/process/web/search")
+def process_web_search(form_data: SearchForm, user=Depends(get_verified_user)):
+    try:
+        logging.info(
+            f"trying to web search with {app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query}"
+        )
+        web_results = search_web(
+            app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query
+        )
+    except Exception as e:
+        log.exception(e)
+
+        print(e)
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.WEB_SEARCH_ERROR(e),
+        )
+
+    try:
+        collection_name = form_data.collection_name
+        if collection_name == "":
+            collection_name = calculate_sha256_string(form_data.query)[:63]
+
+        urls = [result.link for result in web_results]
+
+        loader = get_web_loader(urls)
+        docs = loader.load()
+
+        save_docs_to_vector_db(docs, collection_name, overwrite=True)
+
+        return {
+            "status": True,
+            "collection_name": collection_name,
+            "filenames": urls,
+        }
+    except Exception as e:
+        log.exception(e)
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+class QueryDocForm(BaseModel):
+    collection_name: str
+    query: str
+    k: Optional[int] = None
+    r: Optional[float] = None
+    hybrid: Optional[bool] = None
+
+
+@app.post("/query/doc")
+def query_doc_handler(
+    form_data: QueryDocForm,
+    user=Depends(get_verified_user),
+):
+    try:
+        if app.state.config.ENABLE_RAG_HYBRID_SEARCH:
+            return query_doc_with_hybrid_search(
+                collection_name=form_data.collection_name,
+                query=form_data.query,
+                embedding_function=app.state.EMBEDDING_FUNCTION,
+                k=form_data.k if form_data.k else app.state.config.TOP_K,
+                reranking_function=app.state.sentence_transformer_rf,
+                r=(
+                    form_data.r if form_data.r else app.state.config.RELEVANCE_THRESHOLD
+                ),
+            )
+        else:
+            return query_doc(
+                collection_name=form_data.collection_name,
+                query=form_data.query,
+                embedding_function=app.state.EMBEDDING_FUNCTION,
+                k=form_data.k if form_data.k else app.state.config.TOP_K,
+            )
+    except Exception as e:
+        log.exception(e)
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+class QueryCollectionsForm(BaseModel):
+    collection_names: list[str]
+    query: str
+    k: Optional[int] = None
+    r: Optional[float] = None
+    hybrid: Optional[bool] = None
+
+
+@app.post("/query/collection")
+def query_collection_handler(
+    form_data: QueryCollectionsForm,
+    user=Depends(get_verified_user),
+):
+    try:
+        if app.state.config.ENABLE_RAG_HYBRID_SEARCH:
+            return query_collection_with_hybrid_search(
+                collection_names=form_data.collection_names,
+                query=form_data.query,
+                embedding_function=app.state.EMBEDDING_FUNCTION,
+                k=form_data.k if form_data.k else app.state.config.TOP_K,
+                reranking_function=app.state.sentence_transformer_rf,
+                r=(
+                    form_data.r if form_data.r else app.state.config.RELEVANCE_THRESHOLD
+                ),
+            )
+        else:
+            return query_collection(
+                collection_names=form_data.collection_names,
+                query=form_data.query,
+                embedding_function=app.state.EMBEDDING_FUNCTION,
+                k=form_data.k if form_data.k else app.state.config.TOP_K,
+            )
+
+    except Exception as e:
+        log.exception(e)
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )
+
+
+####################################
+#
+# Vector DB operations
+#
+####################################
+
+
+class DeleteForm(BaseModel):
+    collection_name: str
+    file_id: str
+
+
+@app.post("/delete")
+def delete_entries_from_collection(form_data: DeleteForm, user=Depends(get_admin_user)):
+    try:
+        if VECTOR_DB_CLIENT.has_collection(collection_name=form_data.collection_name):
+            file = Files.get_file_by_id(form_data.file_id)
+            hash = file.hash
+
+            VECTOR_DB_CLIENT.delete(
+                collection_name=form_data.collection_name,
+                metadata={"hash": hash},
+            )
+            return {"status": True}
+        else:
+            return {"status": False}
+    except Exception as e:
+        log.exception(e)
+        return {"status": False}
+
+
+@app.post("/reset/db")
+def reset_vector_db(user=Depends(get_admin_user)):
+    VECTOR_DB_CLIENT.reset()
+    Knowledges.delete_all_knowledge()
+
+
+@app.post("/reset/uploads")
+def reset_upload_dir(user=Depends(get_admin_user)) -> bool:
+    folder = f"{UPLOAD_DIR}"
+    try:
+        # Check if the directory exists
+        if os.path.exists(folder):
+            # Iterate over all the files and directories in the specified directory
+            for filename in os.listdir(folder):
+                file_path = os.path.join(folder, filename)
+                try:
+                    if os.path.isfile(file_path) or os.path.islink(file_path):
+                        os.unlink(file_path)  # Remove the file or link
+                    elif os.path.isdir(file_path):
+                        shutil.rmtree(file_path)  # Remove the directory
+                except Exception as e:
+                    print(f"Failed to delete {file_path}. Reason: {e}")
+        else:
+            print(f"The directory {folder} does not exist")
+    except Exception as e:
+        print(f"Failed to process the directory {folder}. Reason: {e}")
+    return True
+
+
+if ENV == "dev":
+
+    @app.get("/ef")
+    async def get_embeddings():
+        return {"result": app.state.EMBEDDING_FUNCTION("hello world")}
+
+    @app.get("/ef/{text}")
+    async def get_embeddings_text(text: str):
+        return {"result": app.state.EMBEDDING_FUNCTION(text)}

backend/open_webui/apps/retrieval/models/colbert.py

@@ -1,81 +1,81 @@
-import os
-import torch
-import numpy as np
-from colbert.infra import ColBERTConfig
-from colbert.modeling.checkpoint import Checkpoint
-
-
-class ColBERT:
-    def __init__(self, name, **kwargs) -> None:
-        print("ColBERT: Loading model", name)
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        DOCKER = kwargs.get("env") == "docker"
-        if DOCKER:
-            # This is a workaround for the issue with the docker container
-            # where the torch extension is not loaded properly
-            # and the following error is thrown:
-            # /root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/segmented_maxsim_cpp.so: cannot open shared object file: No such file or directory
-
-            lock_file = (
-                "/root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/lock"
-            )
-            if os.path.exists(lock_file):
-                os.remove(lock_file)
-
-        self.ckpt = Checkpoint(
-            name,
-            colbert_config=ColBERTConfig(model_name=name),
-        ).to(self.device)
-        pass
-
-    def calculate_similarity_scores(self, query_embeddings, document_embeddings):
-
-        query_embeddings = query_embeddings.to(self.device)
-        document_embeddings = document_embeddings.to(self.device)
-
-        # Validate dimensions to ensure compatibility
-        if query_embeddings.dim() != 3:
-            raise ValueError(
-                f"Expected query embeddings to have 3 dimensions, but got {query_embeddings.dim()}."
-            )
-        if document_embeddings.dim() != 3:
-            raise ValueError(
-                f"Expected document embeddings to have 3 dimensions, but got {document_embeddings.dim()}."
-            )
-        if query_embeddings.size(0) not in [1, document_embeddings.size(0)]:
-            raise ValueError(
-                "There should be either one query or queries equal to the number of documents."
-            )
-
-        # Transpose the query embeddings to align for matrix multiplication
-        transposed_query_embeddings = query_embeddings.permute(0, 2, 1)
-        # Compute similarity scores using batch matrix multiplication
-        computed_scores = torch.matmul(document_embeddings, transposed_query_embeddings)
-        # Apply max pooling to extract the highest semantic similarity across each document's sequence
-        maximum_scores = torch.max(computed_scores, dim=1).values
-
-        # Sum up the maximum scores across features to get the overall document relevance scores
-        final_scores = maximum_scores.sum(dim=1)
-
-        normalized_scores = torch.softmax(final_scores, dim=0)
-
-        return normalized_scores.detach().cpu().numpy().astype(np.float32)
-
-    def predict(self, sentences):
-
-        query = sentences[0][0]
-        docs = [i[1] for i in sentences]
-
-        # Embedding the documents
-        embedded_docs = self.ckpt.docFromText(docs, bsize=32)[0]
-        # Embedding the queries
-        embedded_queries = self.ckpt.queryFromText([query], bsize=32)
-        embedded_query = embedded_queries[0]
-
-        # Calculate retrieval scores for the query against all documents
-        scores = self.calculate_similarity_scores(
-            embedded_query.unsqueeze(0), embedded_docs
-        )
-
-        return scores
+import os
+import torch
+import numpy as np
+from colbert.infra import ColBERTConfig
+from colbert.modeling.checkpoint import Checkpoint
+
+
+class ColBERT:
+    def __init__(self, name, **kwargs) -> None:
+        print("ColBERT: Loading model", name)
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        DOCKER = kwargs.get("env") == "docker"
+        if DOCKER:
+            # This is a workaround for the issue with the docker container
+            # where the torch extension is not loaded properly
+            # and the following error is thrown:
+            # /root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/segmented_maxsim_cpp.so: cannot open shared object file: No such file or directory
+
+            lock_file = (
+                "/root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/lock"
+            )
+            if os.path.exists(lock_file):
+                os.remove(lock_file)
+
+        self.ckpt = Checkpoint(
+            name,
+            colbert_config=ColBERTConfig(model_name=name),
+        ).to(self.device)
+        pass
+
+    def calculate_similarity_scores(self, query_embeddings, document_embeddings):
+
+        query_embeddings = query_embeddings.to(self.device)
+        document_embeddings = document_embeddings.to(self.device)
+
+        # Validate dimensions to ensure compatibility
+        if query_embeddings.dim() != 3:
+            raise ValueError(
+                f"Expected query embeddings to have 3 dimensions, but got {query_embeddings.dim()}."
+            )
+        if document_embeddings.dim() != 3:
+            raise ValueError(
+                f"Expected document embeddings to have 3 dimensions, but got {document_embeddings.dim()}."
+            )
+        if query_embeddings.size(0) not in [1, document_embeddings.size(0)]:
+            raise ValueError(
+                "There should be either one query or queries equal to the number of documents."
+            )
+
+        # Transpose the query embeddings to align for matrix multiplication
+        transposed_query_embeddings = query_embeddings.permute(0, 2, 1)
+        # Compute similarity scores using batch matrix multiplication
+        computed_scores = torch.matmul(document_embeddings, transposed_query_embeddings)
+        # Apply max pooling to extract the highest semantic similarity across each document's sequence
+        maximum_scores = torch.max(computed_scores, dim=1).values
+
+        # Sum up the maximum scores across features to get the overall document relevance scores
+        final_scores = maximum_scores.sum(dim=1)
+
+        normalized_scores = torch.softmax(final_scores, dim=0)
+
+        return normalized_scores.detach().cpu().numpy().astype(np.float32)
+
+    def predict(self, sentences):
+
+        query = sentences[0][0]
+        docs = [i[1] for i in sentences]
+
+        # Embedding the documents
+        embedded_docs = self.ckpt.docFromText(docs, bsize=32)[0]
+        # Embedding the queries
+        embedded_queries = self.ckpt.queryFromText([query], bsize=32)
+        embedded_query = embedded_queries[0]
+
+        # Calculate retrieval scores for the query against all documents
+        scores = self.calculate_similarity_scores(
+            embedded_query.unsqueeze(0), embedded_docs
+        )
+
+        return scores

backend/open_webui/apps/retrieval/utils.py

@@ -1,573 +1,573 @@
-import logging
-import os
-import uuid
-from typing import Optional, Union
-
-import requests
-
-from huggingface_hub import snapshot_download
-from langchain.retrievers import ContextualCompressionRetriever, EnsembleRetriever
-from langchain_community.retrievers import BM25Retriever
-from langchain_core.documents import Document
-
-
-from open_webui.apps.ollama.main import (
-    GenerateEmbedForm,
-    generate_ollama_batch_embeddings,
-)
-from open_webui.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
-from open_webui.utils.misc import get_last_user_message
-
-from open_webui.env import SRC_LOG_LEVELS
-from open_webui.config import DEFAULT_RAG_TEMPLATE
-
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-from typing import Any
-
-from langchain_core.callbacks import CallbackManagerForRetrieverRun
-from langchain_core.retrievers import BaseRetriever
-
-
-class VectorSearchRetriever(BaseRetriever):
-    collection_name: Any
-    embedding_function: Any
-    top_k: int
-
-    def _get_relevant_documents(
-        self,
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-    ) -> list[Document]:
-        result = VECTOR_DB_CLIENT.search(
-            collection_name=self.collection_name,
-            vectors=[self.embedding_function(query)],
-            limit=self.top_k,
-        )
-
-        ids = result.ids[0]
-        metadatas = result.metadatas[0]
-        documents = result.documents[0]
-
-        results = []
-        for idx in range(len(ids)):
-            results.append(
-                Document(
-                    metadata=metadatas[idx],
-                    page_content=documents[idx],
-                )
-            )
-        return results
-
-
-def query_doc(
-    collection_name: str,
-    query_embedding: list[float],
-    k: int,
-):
-    try:
-        result = VECTOR_DB_CLIENT.search(
-            collection_name=collection_name,
-            vectors=[query_embedding],
-            limit=k,
-        )
-
-        log.info(f"query_doc:result {result}")
-        return result
-    except Exception as e:
-        print(e)
-        raise e
-
-
-def query_doc_with_hybrid_search(
-    collection_name: str,
-    query: str,
-    embedding_function,
-    k: int,
-    reranking_function,
-    r: float,
-) -> dict:
-    try:
-        result = VECTOR_DB_CLIENT.get(collection_name=collection_name)
-
-        bm25_retriever = BM25Retriever.from_texts(
-            texts=result.documents[0],
-            metadatas=result.metadatas[0],
-        )
-        bm25_retriever.k = k
-
-        vector_search_retriever = VectorSearchRetriever(
-            collection_name=collection_name,
-            embedding_function=embedding_function,
-            top_k=k,
-        )
-
-        ensemble_retriever = EnsembleRetriever(
-            retrievers=[bm25_retriever, vector_search_retriever], weights=[0.5, 0.5]
-        )
-        compressor = RerankCompressor(
-            embedding_function=embedding_function,
-            top_n=k,
-            reranking_function=reranking_function,
-            r_score=r,
-        )
-
-        compression_retriever = ContextualCompressionRetriever(
-            base_compressor=compressor, base_retriever=ensemble_retriever
-        )
-
-        result = compression_retriever.invoke(query)
-        result = {
-            "distances": [[d.metadata.get("score") for d in result]],
-            "documents": [[d.page_content for d in result]],
-            "metadatas": [[d.metadata for d in result]],
-        }
-
-        log.info(f"query_doc_with_hybrid_search:result {result}")
-        return result
-    except Exception as e:
-        raise e
-
-
-def merge_and_sort_query_results(
-    query_results: list[dict], k: int, reverse: bool = False
-) -> list[dict]:
-    # Initialize lists to store combined data
-    combined_distances = []
-    combined_documents = []
-    combined_metadatas = []
-
-    for data in query_results:
-        combined_distances.extend(data["distances"][0])
-        combined_documents.extend(data["documents"][0])
-        combined_metadatas.extend(data["metadatas"][0])
-
-    # Create a list of tuples (distance, document, metadata)
-    combined = list(zip(combined_distances, combined_documents, combined_metadatas))
-
-    # Sort the list based on distances
-    combined.sort(key=lambda x: x[0], reverse=reverse)
-
-    # We don't have anything :-(
-    if not combined:
-        sorted_distances = []
-        sorted_documents = []
-        sorted_metadatas = []
-    else:
-        # Unzip the sorted list
-        sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
-
-        # Slicing the lists to include only k elements
-        sorted_distances = list(sorted_distances)[:k]
-        sorted_documents = list(sorted_documents)[:k]
-        sorted_metadatas = list(sorted_metadatas)[:k]
-
-    # Create the output dictionary
-    result = {
-        "distances": [sorted_distances],
-        "documents": [sorted_documents],
-        "metadatas": [sorted_metadatas],
-    }
-
-    return result
-
-
-def query_collection(
-    collection_names: list[str],
-    query: str,
-    embedding_function,
-    k: int,
-) -> dict:
-
-    results = []
-    query_embedding = embedding_function(query)
-
-    for collection_name in collection_names:
-        if collection_name:
-            try:
-                result = query_doc(
-                    collection_name=collection_name,
-                    k=k,
-                    query_embedding=query_embedding,
-                )
-                if result is not None:
-                    results.append(result.model_dump())
-            except Exception as e:
-                log.exception(f"Error when querying the collection: {e}")
-        else:
-            pass
-
-    return merge_and_sort_query_results(results, k=k)
-
-
-def query_collection_with_hybrid_search(
-    collection_names: list[str],
-    query: str,
-    embedding_function,
-    k: int,
-    reranking_function,
-    r: float,
-) -> dict:
-    results = []
-    error = False
-    for collection_name in collection_names:
-        try:
-            result = query_doc_with_hybrid_search(
-                collection_name=collection_name,
-                query=query,
-                embedding_function=embedding_function,
-                k=k,
-                reranking_function=reranking_function,
-                r=r,
-            )
-            results.append(result)
-        except Exception as e:
-            log.exception(
-                "Error when querying the collection with " f"hybrid_search: {e}"
-            )
-            error = True
-
-    if error:
-        raise Exception(
-            "Hybrid search failed for all collections. Using Non hybrid search as fallback."
-        )
-
-    return merge_and_sort_query_results(results, k=k, reverse=True)
-
-
-def rag_template(template: str, context: str, query: str):
-    if template == "":
-        template = DEFAULT_RAG_TEMPLATE
-
-    if "[context]" not in template and "{{CONTEXT}}" not in template:
-        log.debug(
-            "WARNING: The RAG template does not contain the '[context]' or '{{CONTEXT}}' placeholder."
-        )
-
-    if "<context>" in context and "</context>" in context:
-        log.debug(
-            "WARNING: Potential prompt injection attack: the RAG "
-            "context contains '<context>' and '</context>'. This might be "
-            "nothing, or the user might be trying to hack something."
-        )
-
-    query_placeholders = []
-    if "[query]" in context:
-        query_placeholder = "{{QUERY" + str(uuid.uuid4()) + "}}"
-        template = template.replace("[query]", query_placeholder)
-        query_placeholders.append(query_placeholder)
-
-    if "{{QUERY}}" in context:
-        query_placeholder = "{{QUERY" + str(uuid.uuid4()) + "}}"
-        template = template.replace("{{QUERY}}", query_placeholder)
-        query_placeholders.append(query_placeholder)
-
-    template = template.replace("[context]", context)
-    template = template.replace("{{CONTEXT}}", context)
-    template = template.replace("[query]", query)
-    template = template.replace("{{QUERY}}", query)
-
-    for query_placeholder in query_placeholders:
-        template = template.replace(query_placeholder, query)
-
-    return template
-
-
-def get_embedding_function(
-    embedding_engine,
-    embedding_model,
-    embedding_function,
-    openai_key,
-    openai_url,
-    embedding_batch_size,
-):
-    if embedding_engine == "":
-        return lambda query: embedding_function.encode(query).tolist()
-    elif embedding_engine in ["ollama", "openai"]:
-        func = lambda query: generate_embeddings(
-            engine=embedding_engine,
-            model=embedding_model,
-            text=query,
-            key=openai_key if embedding_engine == "openai" else "",
-            url=openai_url if embedding_engine == "openai" else "",
-        )
-
-        def generate_multiple(query, func):
-            if isinstance(query, list):
-                embeddings = []
-                for i in range(0, len(query), embedding_batch_size):
-                    embeddings.extend(func(query[i : i + embedding_batch_size]))
-                return embeddings
-            else:
-                return func(query)
-
-        return lambda query: generate_multiple(query, func)
-
-
-def get_rag_context(
-    files,
-    messages,
-    embedding_function,
-    k,
-    reranking_function,
-    r,
-    hybrid_search,
-):
-    log.debug(f"files: {files} {messages} {embedding_function} {reranking_function}")
-    query = get_last_user_message(messages)
-
-    extracted_collections = []
-    relevant_contexts = []
-
-    for file in files:
-        if file.get("context") == "full":
-            context = {
-                "documents": [[file.get("file").get("data", {}).get("content")]],
-                "metadatas": [[{"file_id": file.get("id"), "name": file.get("name")}]],
-            }
-        else:
-            context = None
-
-            collection_names = []
-            if file.get("type") == "collection":
-                if file.get("legacy"):
-                    collection_names = file.get("collection_names", [])
-                else:
-                    collection_names.append(file["id"])
-            elif file.get("collection_name"):
-                collection_names.append(file["collection_name"])
-            elif file.get("id"):
-                if file.get("legacy"):
-                    collection_names.append(f"{file['id']}")
-                else:
-                    collection_names.append(f"file-{file['id']}")
-
-            collection_names = set(collection_names).difference(extracted_collections)
-            if not collection_names:
-                log.debug(f"skipping {file} as it has already been extracted")
-                continue
-
-            try:
-                context = None
-                if file.get("type") == "text":
-                    context = file["content"]
-                else:
-                    if hybrid_search:
-                        try:
-                            context = query_collection_with_hybrid_search(
-                                collection_names=collection_names,
-                                query=query,
-                                embedding_function=embedding_function,
-                                k=k,
-                                reranking_function=reranking_function,
-                                r=r,
-                            )
-                        except Exception as e:
-                            log.debug(
-                                "Error when using hybrid search, using"
-                                " non hybrid search as fallback."
-                            )
-
-                    if (not hybrid_search) or (context is None):
-                        context = query_collection(
-                            collection_names=collection_names,
-                            query=query,
-                            embedding_function=embedding_function,
-                            k=k,
-                        )
-            except Exception as e:
-                log.exception(e)
-
-            extracted_collections.extend(collection_names)
-
-        if context:
-            if "data" in file:
-                del file["data"]
-            relevant_contexts.append({**context, "file": file})
-
-    contexts = []
-    citations = []
-    for context in relevant_contexts:
-        try:
-            if "documents" in context:
-                file_names = list(
-                    set(
-                        [
-                            metadata["name"]
-                            for metadata in context["metadatas"][0]
-                            if metadata is not None and "name" in metadata
-                        ]
-                    )
-                )
-                contexts.append(
-                    ((", ".join(file_names) + ":\n\n") if file_names else "")
-                    + "\n\n".join(
-                        [text for text in context["documents"][0] if text is not None]
-                    )
-                )
-
-                if "metadatas" in context:
-                    citation = {
-                        "source": context["file"],
-                        "document": context["documents"][0],
-                        "metadata": context["metadatas"][0],
-                    }
-                    if "distances" in context and context["distances"]:
-                        citation["distances"] = context["distances"][0]
-                    citations.append(citation)
-        except Exception as e:
-            log.exception(e)
-
-    print("contexts", contexts)
-    print("citations", citations)
-
-    return contexts, citations
-
-
-def get_model_path(model: str, update_model: bool = False):
-    # Construct huggingface_hub kwargs with local_files_only to return the snapshot path
-    cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
-
-    local_files_only = not update_model
-
-    snapshot_kwargs = {
-        "cache_dir": cache_dir,
-        "local_files_only": local_files_only,
-    }
-
-    log.debug(f"model: {model}")
-    log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
-
-    # Inspiration from upstream sentence_transformers
-    if (
-        os.path.exists(model)
-        or ("\\" in model or model.count("/") > 1)
-        and local_files_only
-    ):
-        # If fully qualified path exists, return input, else set repo_id
-        return model
-    elif "/" not in model:
-        # Set valid repo_id for model short-name
-        model = "sentence-transformers" + "/" + model
-
-    snapshot_kwargs["repo_id"] = model
-
-    # Attempt to query the huggingface_hub library to determine the local path and/or to update
-    try:
-        model_repo_path = snapshot_download(**snapshot_kwargs)
-        log.debug(f"model_repo_path: {model_repo_path}")
-        return model_repo_path
-    except Exception as e:
-        log.exception(f"Cannot determine model snapshot path: {e}")
-        return model
-
-
-def generate_openai_batch_embeddings(
-    model: str, texts: list[str], key: str, url: str = "https://api.openai.com/v1"
-) -> Optional[list[list[float]]]:
-    try:
-        r = requests.post(
-            f"{url}/embeddings",
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {key}",
-            },
-            json={"input": texts, "model": model},
-        )
-        r.raise_for_status()
-        data = r.json()
-        if "data" in data:
-            return [elem["embedding"] for elem in data["data"]]
-        else:
-            raise "Something went wrong :/"
-    except Exception as e:
-        print(e)
-        return None
-
-
-def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **kwargs):
-    if engine == "ollama":
-        if isinstance(text, list):
-            embeddings = generate_ollama_batch_embeddings(
-                GenerateEmbedForm(**{"model": model, "input": text})
-            )
-        else:
-            embeddings = generate_ollama_batch_embeddings(
-                GenerateEmbedForm(**{"model": model, "input": [text]})
-            )
-        return (
-            embeddings["embeddings"][0]
-            if isinstance(text, str)
-            else embeddings["embeddings"]
-        )
-    elif engine == "openai":
-        key = kwargs.get("key", "")
-        url = kwargs.get("url", "https://api.openai.com/v1")
-
-        if isinstance(text, list):
-            embeddings = generate_openai_batch_embeddings(model, text, key, url)
-        else:
-            embeddings = generate_openai_batch_embeddings(model, [text], key, url)
-
-        return embeddings[0] if isinstance(text, str) else embeddings
-
-
-import operator
-from typing import Optional, Sequence
-
-from langchain_core.callbacks import Callbacks
-from langchain_core.documents import BaseDocumentCompressor, Document
-
-
-class RerankCompressor(BaseDocumentCompressor):
-    embedding_function: Any
-    top_n: int
-    reranking_function: Any
-    r_score: float
-
-    class Config:
-        extra = "forbid"
-        arbitrary_types_allowed = True
-
-    def compress_documents(
-        self,
-        documents: Sequence[Document],
-        query: str,
-        callbacks: Optional[Callbacks] = None,
-    ) -> Sequence[Document]:
-        reranking = self.reranking_function is not None
-
-        if reranking:
-            scores = self.reranking_function.predict(
-                [(query, doc.page_content) for doc in documents]
-            )
-        else:
-            from sentence_transformers import util
-
-            query_embedding = self.embedding_function(query)
-            document_embedding = self.embedding_function(
-                [doc.page_content for doc in documents]
-            )
-            scores = util.cos_sim(query_embedding, document_embedding)[0]
-
-        docs_with_scores = list(zip(documents, scores.tolist()))
-        if self.r_score:
-            docs_with_scores = [
-                (d, s) for d, s in docs_with_scores if s >= self.r_score
-            ]
-
-        result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
-        final_results = []
-        for doc, doc_score in result[: self.top_n]:
-            metadata = doc.metadata
-            metadata["score"] = doc_score
-            doc = Document(
-                page_content=doc.page_content,
-                metadata=metadata,
-            )
-            final_results.append(doc)
-        return final_results
+import logging
+import os
+import uuid
+from typing import Optional, Union
+
+import requests
+
+from huggingface_hub import snapshot_download
+from langchain.retrievers import ContextualCompressionRetriever, EnsembleRetriever
+from langchain_community.retrievers import BM25Retriever
+from langchain_core.documents import Document
+
+
+from open_webui.apps.ollama.main import (
+    GenerateEmbedForm,
+    generate_ollama_batch_embeddings,
+)
+from open_webui.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
+from open_webui.utils.misc import get_last_user_message
+
+from open_webui.env import SRC_LOG_LEVELS
+from open_webui.config import DEFAULT_RAG_TEMPLATE
+
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+from typing import Any
+
+from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain_core.retrievers import BaseRetriever
+
+
+class VectorSearchRetriever(BaseRetriever):
+    collection_name: Any
+    embedding_function: Any
+    top_k: int
+
+    def _get_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+    ) -> list[Document]:
+        result = VECTOR_DB_CLIENT.search(
+            collection_name=self.collection_name,
+            vectors=[self.embedding_function(query)],
+            limit=self.top_k,
+        )
+
+        ids = result.ids[0]
+        metadatas = result.metadatas[0]
+        documents = result.documents[0]
+
+        results = []
+        for idx in range(len(ids)):
+            results.append(
+                Document(
+                    metadata=metadatas[idx],
+                    page_content=documents[idx],
+                )
+            )
+        return results
+
+
+def query_doc(
+    collection_name: str,
+    query_embedding: list[float],
+    k: int,
+):
+    try:
+        result = VECTOR_DB_CLIENT.search(
+            collection_name=collection_name,
+            vectors=[query_embedding],
+            limit=k,
+        )
+
+        log.info(f"query_doc:result {result}")
+        return result
+    except Exception as e:
+        print(e)
+        raise e
+
+
+def query_doc_with_hybrid_search(
+    collection_name: str,
+    query: str,
+    embedding_function,
+    k: int,
+    reranking_function,
+    r: float,
+) -> dict:
+    try:
+        result = VECTOR_DB_CLIENT.get(collection_name=collection_name)
+
+        bm25_retriever = BM25Retriever.from_texts(
+            texts=result.documents[0],
+            metadatas=result.metadatas[0],
+        )
+        bm25_retriever.k = k
+
+        vector_search_retriever = VectorSearchRetriever(
+            collection_name=collection_name,
+            embedding_function=embedding_function,
+            top_k=k,
+        )
+
+        ensemble_retriever = EnsembleRetriever(
+            retrievers=[bm25_retriever, vector_search_retriever], weights=[0.5, 0.5]
+        )
+        compressor = RerankCompressor(
+            embedding_function=embedding_function,
+            top_n=k,
+            reranking_function=reranking_function,
+            r_score=r,
+        )
+
+        compression_retriever = ContextualCompressionRetriever(
+            base_compressor=compressor, base_retriever=ensemble_retriever
+        )
+
+        result = compression_retriever.invoke(query)
+        result = {
+            "distances": [[d.metadata.get("score") for d in result]],
+            "documents": [[d.page_content for d in result]],
+            "metadatas": [[d.metadata for d in result]],
+        }
+
+        log.info(f"query_doc_with_hybrid_search:result {result}")
+        return result
+    except Exception as e:
+        raise e
+
+
+def merge_and_sort_query_results(
+    query_results: list[dict], k: int, reverse: bool = False
+) -> list[dict]:
+    # Initialize lists to store combined data
+    combined_distances = []
+    combined_documents = []
+    combined_metadatas = []
+
+    for data in query_results:
+        combined_distances.extend(data["distances"][0])
+        combined_documents.extend(data["documents"][0])
+        combined_metadatas.extend(data["metadatas"][0])
+
+    # Create a list of tuples (distance, document, metadata)
+    combined = list(zip(combined_distances, combined_documents, combined_metadatas))
+
+    # Sort the list based on distances
+    combined.sort(key=lambda x: x[0], reverse=reverse)
+
+    # We don't have anything :-(
+    if not combined:
+        sorted_distances = []
+        sorted_documents = []
+        sorted_metadatas = []
+    else:
+        # Unzip the sorted list
+        sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
+
+        # Slicing the lists to include only k elements
+        sorted_distances = list(sorted_distances)[:k]
+        sorted_documents = list(sorted_documents)[:k]
+        sorted_metadatas = list(sorted_metadatas)[:k]
+
+    # Create the output dictionary
+    result = {
+        "distances": [sorted_distances],
+        "documents": [sorted_documents],
+        "metadatas": [sorted_metadatas],
+    }
+
+    return result
+
+
+def query_collection(
+    collection_names: list[str],
+    query: str,
+    embedding_function,
+    k: int,
+) -> dict:
+
+    results = []
+    query_embedding = embedding_function(query)
+
+    for collection_name in collection_names:
+        if collection_name:
+            try:
+                result = query_doc(
+                    collection_name=collection_name,
+                    k=k,
+                    query_embedding=query_embedding,
+                )
+                if result is not None:
+                    results.append(result.model_dump())
+            except Exception as e:
+                log.exception(f"Error when querying the collection: {e}")
+        else:
+            pass
+
+    return merge_and_sort_query_results(results, k=k)
+
+
+def query_collection_with_hybrid_search(
+    collection_names: list[str],
+    query: str,
+    embedding_function,
+    k: int,
+    reranking_function,
+    r: float,
+) -> dict:
+    results = []
+    error = False
+    for collection_name in collection_names:
+        try:
+            result = query_doc_with_hybrid_search(
+                collection_name=collection_name,
+                query=query,
+                embedding_function=embedding_function,
+                k=k,
+                reranking_function=reranking_function,
+                r=r,
+            )
+            results.append(result)
+        except Exception as e:
+            log.exception(
+                "Error when querying the collection with " f"hybrid_search: {e}"
+            )
+            error = True
+
+    if error:
+        raise Exception(
+            "Hybrid search failed for all collections. Using Non hybrid search as fallback."
+        )
+
+    return merge_and_sort_query_results(results, k=k, reverse=True)
+
+
+def rag_template(template: str, context: str, query: str):
+    if template == "":
+        template = DEFAULT_RAG_TEMPLATE
+
+    if "[context]" not in template and "{{CONTEXT}}" not in template:
+        log.debug(
+            "WARNING: The RAG template does not contain the '[context]' or '{{CONTEXT}}' placeholder."
+        )
+
+    if "<context>" in context and "</context>" in context:
+        log.debug(
+            "WARNING: Potential prompt injection attack: the RAG "
+            "context contains '<context>' and '</context>'. This might be "
+            "nothing, or the user might be trying to hack something."
+        )
+
+    query_placeholders = []
+    if "[query]" in context:
+        query_placeholder = "{{QUERY" + str(uuid.uuid4()) + "}}"
+        template = template.replace("[query]", query_placeholder)
+        query_placeholders.append(query_placeholder)
+
+    if "{{QUERY}}" in context:
+        query_placeholder = "{{QUERY" + str(uuid.uuid4()) + "}}"
+        template = template.replace("{{QUERY}}", query_placeholder)
+        query_placeholders.append(query_placeholder)
+
+    template = template.replace("[context]", context)
+    template = template.replace("{{CONTEXT}}", context)
+    template = template.replace("[query]", query)
+    template = template.replace("{{QUERY}}", query)
+
+    for query_placeholder in query_placeholders:
+        template = template.replace(query_placeholder, query)
+
+    return template
+
+
+def get_embedding_function(
+    embedding_engine,
+    embedding_model,
+    embedding_function,
+    openai_key,
+    openai_url,
+    embedding_batch_size,
+):
+    if embedding_engine == "":
+        return lambda query: embedding_function.encode(query).tolist()
+    elif embedding_engine in ["ollama", "openai"]:
+        func = lambda query: generate_embeddings(
+            engine=embedding_engine,
+            model=embedding_model,
+            text=query,
+            key=openai_key if embedding_engine == "openai" else "",
+            url=openai_url if embedding_engine == "openai" else "",
+        )
+
+        def generate_multiple(query, func):
+            if isinstance(query, list):
+                embeddings = []
+                for i in range(0, len(query), embedding_batch_size):
+                    embeddings.extend(func(query[i : i + embedding_batch_size]))
+                return embeddings
+            else:
+                return func(query)
+
+        return lambda query: generate_multiple(query, func)
+
+
+def get_rag_context(
+    files,
+    messages,
+    embedding_function,
+    k,
+    reranking_function,
+    r,
+    hybrid_search,
+):
+    log.debug(f"files: {files} {messages} {embedding_function} {reranking_function}")
+    query = get_last_user_message(messages)
+
+    extracted_collections = []
+    relevant_contexts = []
+
+    for file in files:
+        if file.get("context") == "full":
+            context = {
+                "documents": [[file.get("file").get("data", {}).get("content")]],
+                "metadatas": [[{"file_id": file.get("id"), "name": file.get("name")}]],
+            }
+        else:
+            context = None
+
+            collection_names = []
+            if file.get("type") == "collection":
+                if file.get("legacy"):
+                    collection_names = file.get("collection_names", [])
+                else:
+                    collection_names.append(file["id"])
+            elif file.get("collection_name"):
+                collection_names.append(file["collection_name"])
+            elif file.get("id"):
+                if file.get("legacy"):
+                    collection_names.append(f"{file['id']}")
+                else:
+                    collection_names.append(f"file-{file['id']}")
+
+            collection_names = set(collection_names).difference(extracted_collections)
+            if not collection_names:
+                log.debug(f"skipping {file} as it has already been extracted")
+                continue
+
+            try:
+                context = None
+                if file.get("type") == "text":
+                    context = file["content"]
+                else:
+                    if hybrid_search:
+                        try:
+                            context = query_collection_with_hybrid_search(
+                                collection_names=collection_names,
+                                query=query,
+                                embedding_function=embedding_function,
+                                k=k,
+                                reranking_function=reranking_function,
+                                r=r,
+                            )
+                        except Exception as e:
+                            log.debug(
+                                "Error when using hybrid search, using"
+                                " non hybrid search as fallback."
+                            )
+
+                    if (not hybrid_search) or (context is None):
+                        context = query_collection(
+                            collection_names=collection_names,
+                            query=query,
+                            embedding_function=embedding_function,
+                            k=k,
+                        )
+            except Exception as e:
+                log.exception(e)
+
+            extracted_collections.extend(collection_names)
+
+        if context:
+            if "data" in file:
+                del file["data"]
+            relevant_contexts.append({**context, "file": file})
+
+    contexts = []
+    citations = []
+    for context in relevant_contexts:
+        try:
+            if "documents" in context:
+                file_names = list(
+                    set(
+                        [
+                            metadata["name"]
+                            for metadata in context["metadatas"][0]
+                            if metadata is not None and "name" in metadata
+                        ]
+                    )
+                )
+                contexts.append(
+                    ((", ".join(file_names) + ":\n\n") if file_names else "")
+                    + "\n\n".join(
+                        [text for text in context["documents"][0] if text is not None]
+                    )
+                )
+
+                if "metadatas" in context:
+                    citation = {
+                        "source": context["file"],
+                        "document": context["documents"][0],
+                        "metadata": context["metadatas"][0],
+                    }
+                    if "distances" in context and context["distances"]:
+                        citation["distances"] = context["distances"][0]
+                    citations.append(citation)
+        except Exception as e:
+            log.exception(e)
+
+    print("contexts", contexts)
+    print("citations", citations)
+
+    return contexts, citations
+
+
+def get_model_path(model: str, update_model: bool = False):
+    # Construct huggingface_hub kwargs with local_files_only to return the snapshot path
+    cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
+
+    local_files_only = not update_model
+
+    snapshot_kwargs = {
+        "cache_dir": cache_dir,
+        "local_files_only": local_files_only,
+    }
+
+    log.debug(f"model: {model}")
+    log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
+
+    # Inspiration from upstream sentence_transformers
+    if (
+        os.path.exists(model)
+        or ("\\" in model or model.count("/") > 1)
+        and local_files_only
+    ):
+        # If fully qualified path exists, return input, else set repo_id
+        return model
+    elif "/" not in model:
+        # Set valid repo_id for model short-name
+        model = "sentence-transformers" + "/" + model
+
+    snapshot_kwargs["repo_id"] = model
+
+    # Attempt to query the huggingface_hub library to determine the local path and/or to update
+    try:
+        model_repo_path = snapshot_download(**snapshot_kwargs)
+        log.debug(f"model_repo_path: {model_repo_path}")
+        return model_repo_path
+    except Exception as e:
+        log.exception(f"Cannot determine model snapshot path: {e}")
+        return model
+
+
+def generate_openai_batch_embeddings(
+    model: str, texts: list[str], key: str, url: str = "https://api.openai.com/v1"
+) -> Optional[list[list[float]]]:
+    try:
+        r = requests.post(
+            f"{url}/embeddings",
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {key}",
+            },
+            json={"input": texts, "model": model},
+        )
+        r.raise_for_status()
+        data = r.json()
+        if "data" in data:
+            return [elem["embedding"] for elem in data["data"]]
+        else:
+            raise "Something went wrong :/"
+    except Exception as e:
+        print(e)
+        return None
+
+
+def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **kwargs):
+    if engine == "ollama":
+        if isinstance(text, list):
+            embeddings = generate_ollama_batch_embeddings(
+                GenerateEmbedForm(**{"model": model, "input": text})
+            )
+        else:
+            embeddings = generate_ollama_batch_embeddings(
+                GenerateEmbedForm(**{"model": model, "input": [text]})
+            )
+        return (
+            embeddings["embeddings"][0]
+            if isinstance(text, str)
+            else embeddings["embeddings"]
+        )
+    elif engine == "openai":
+        key = kwargs.get("key", "")
+        url = kwargs.get("url", "https://api.openai.com/v1")
+
+        if isinstance(text, list):
+            embeddings = generate_openai_batch_embeddings(model, text, key, url)
+        else:
+            embeddings = generate_openai_batch_embeddings(model, [text], key, url)
+
+        return embeddings[0] if isinstance(text, str) else embeddings
+
+
+import operator
+from typing import Optional, Sequence
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+
+
+class RerankCompressor(BaseDocumentCompressor):
+    embedding_function: Any
+    top_n: int
+    reranking_function: Any
+    r_score: float
+
+    class Config:
+        extra = "forbid"
+        arbitrary_types_allowed = True
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        reranking = self.reranking_function is not None
+
+        if reranking:
+            scores = self.reranking_function.predict(
+                [(query, doc.page_content) for doc in documents]
+            )
+        else:
+            from sentence_transformers import util
+
+            query_embedding = self.embedding_function(query)
+            document_embedding = self.embedding_function(
+                [doc.page_content for doc in documents]
+            )
+            scores = util.cos_sim(query_embedding, document_embedding)[0]
+
+        docs_with_scores = list(zip(documents, scores.tolist()))
+        if self.r_score:
+            docs_with_scores = [
+                (d, s) for d, s in docs_with_scores if s >= self.r_score
+            ]
+
+        result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
+        final_results = []
+        for doc, doc_score in result[: self.top_n]:
+            metadata = doc.metadata
+            metadata["score"] = doc_score
+            doc = Document(
+                page_content=doc.page_content,
+                metadata=metadata,
+            )
+            final_results.append(doc)
+        return final_results

backend/open_webui/apps/retrieval/vector/connector.py

@@ -1,14 +1,14 @@
-from open_webui.config import VECTOR_DB
-
-if VECTOR_DB == "milvus":
-    from open_webui.apps.retrieval.vector.dbs.milvus import MilvusClient
-
-    VECTOR_DB_CLIENT = MilvusClient()
-elif VECTOR_DB == "qdrant":
-    from open_webui.apps.retrieval.vector.dbs.qdrant import QdrantClient
-
-    VECTOR_DB_CLIENT = QdrantClient()
-else:
-    from open_webui.apps.retrieval.vector.dbs.chroma import ChromaClient
-
-    VECTOR_DB_CLIENT = ChromaClient()
+from open_webui.config import VECTOR_DB
+
+if VECTOR_DB == "milvus":
+    from open_webui.apps.retrieval.vector.dbs.milvus import MilvusClient
+
+    VECTOR_DB_CLIENT = MilvusClient()
+elif VECTOR_DB == "qdrant":
+    from open_webui.apps.retrieval.vector.dbs.qdrant import QdrantClient
+
+    VECTOR_DB_CLIENT = QdrantClient()
+else:
+    from open_webui.apps.retrieval.vector.dbs.chroma import ChromaClient
+
+    VECTOR_DB_CLIENT = ChromaClient()

backend/open_webui/apps/retrieval/vector/dbs/chroma.py

@@ -1,161 +1,161 @@
-import chromadb
-from chromadb import Settings
-from chromadb.utils.batch_utils import create_batches
-
-from typing import Optional
-
-from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
-from open_webui.config import (
-    CHROMA_DATA_PATH,
-    CHROMA_HTTP_HOST,
-    CHROMA_HTTP_PORT,
-    CHROMA_HTTP_HEADERS,
-    CHROMA_HTTP_SSL,
-    CHROMA_TENANT,
-    CHROMA_DATABASE,
-)
-
-
-class ChromaClient:
-    def __init__(self):
-        if CHROMA_HTTP_HOST != "":
-            self.client = chromadb.HttpClient(
-                host=CHROMA_HTTP_HOST,
-                port=CHROMA_HTTP_PORT,
-                headers=CHROMA_HTTP_HEADERS,
-                ssl=CHROMA_HTTP_SSL,
-                tenant=CHROMA_TENANT,
-                database=CHROMA_DATABASE,
-                settings=Settings(allow_reset=True, anonymized_telemetry=False),
-            )
-        else:
-            self.client = chromadb.PersistentClient(
-                path=CHROMA_DATA_PATH,
-                settings=Settings(allow_reset=True, anonymized_telemetry=False),
-                tenant=CHROMA_TENANT,
-                database=CHROMA_DATABASE,
-            )
-
-    def has_collection(self, collection_name: str) -> bool:
-        # Check if the collection exists based on the collection name.
-        collections = self.client.list_collections()
-        return collection_name in [collection.name for collection in collections]
-
-    def delete_collection(self, collection_name: str):
-        # Delete the collection based on the collection name.
-        return self.client.delete_collection(name=collection_name)
-
-    def search(
-        self, collection_name: str, vectors: list[list[float | int]], limit: int
-    ) -> Optional[SearchResult]:
-        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
-        try:
-            collection = self.client.get_collection(name=collection_name)
-            if collection:
-                result = collection.query(
-                    query_embeddings=vectors,
-                    n_results=limit,
-                )
-
-                return SearchResult(
-                    **{
-                        "ids": result["ids"],
-                        "distances": result["distances"],
-                        "documents": result["documents"],
-                        "metadatas": result["metadatas"],
-                    }
-                )
-            return None
-        except Exception as e:
-            return None
-
-    def query(
-        self, collection_name: str, filter: dict, limit: Optional[int] = None
-    ) -> Optional[GetResult]:
-        # Query the items from the collection based on the filter.
-        try:
-            collection = self.client.get_collection(name=collection_name)
-            if collection:
-                result = collection.get(
-                    where=filter,
-                    limit=limit,
-                )
-
-                return GetResult(
-                    **{
-                        "ids": [result["ids"]],
-                        "documents": [result["documents"]],
-                        "metadatas": [result["metadatas"]],
-                    }
-                )
-            return None
-        except Exception as e:
-            print(e)
-            return None
-
-    def get(self, collection_name: str) -> Optional[GetResult]:
-        # Get all the items in the collection.
-        collection = self.client.get_collection(name=collection_name)
-        if collection:
-            result = collection.get()
-            return GetResult(
-                **{
-                    "ids": [result["ids"]],
-                    "documents": [result["documents"]],
-                    "metadatas": [result["metadatas"]],
-                }
-            )
-        return None
-
-    def insert(self, collection_name: str, items: list[VectorItem]):
-        # Insert the items into the collection, if the collection does not exist, it will be created.
-        collection = self.client.get_or_create_collection(
-            name=collection_name, metadata={"hnsw:space": "cosine"}
-        )
-
-        ids = [item["id"] for item in items]
-        documents = [item["text"] for item in items]
-        embeddings = [item["vector"] for item in items]
-        metadatas = [item["metadata"] for item in items]
-
-        for batch in create_batches(
-            api=self.client,
-            documents=documents,
-            embeddings=embeddings,
-            ids=ids,
-            metadatas=metadatas,
-        ):
-            collection.add(*batch)
-
-    def upsert(self, collection_name: str, items: list[VectorItem]):
-        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
-        collection = self.client.get_or_create_collection(
-            name=collection_name, metadata={"hnsw:space": "cosine"}
-        )
-
-        ids = [item["id"] for item in items]
-        documents = [item["text"] for item in items]
-        embeddings = [item["vector"] for item in items]
-        metadatas = [item["metadata"] for item in items]
-
-        collection.upsert(
-            ids=ids, documents=documents, embeddings=embeddings, metadatas=metadatas
-        )
-
-    def delete(
-        self,
-        collection_name: str,
-        ids: Optional[list[str]] = None,
-        filter: Optional[dict] = None,
-    ):
-        # Delete the items from the collection based on the ids.
-        collection = self.client.get_collection(name=collection_name)
-        if collection:
-            if ids:
-                collection.delete(ids=ids)
-            elif filter:
-                collection.delete(where=filter)
-
-    def reset(self):
-        # Resets the database. This will delete all collections and item entries.
-        return self.client.reset()
+import chromadb
+from chromadb import Settings
+from chromadb.utils.batch_utils import create_batches
+
+from typing import Optional
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import (
+    CHROMA_DATA_PATH,
+    CHROMA_HTTP_HOST,
+    CHROMA_HTTP_PORT,
+    CHROMA_HTTP_HEADERS,
+    CHROMA_HTTP_SSL,
+    CHROMA_TENANT,
+    CHROMA_DATABASE,
+)
+
+
+class ChromaClient:
+    def __init__(self):
+        if CHROMA_HTTP_HOST != "":
+            self.client = chromadb.HttpClient(
+                host=CHROMA_HTTP_HOST,
+                port=CHROMA_HTTP_PORT,
+                headers=CHROMA_HTTP_HEADERS,
+                ssl=CHROMA_HTTP_SSL,
+                tenant=CHROMA_TENANT,
+                database=CHROMA_DATABASE,
+                settings=Settings(allow_reset=True, anonymized_telemetry=False),
+            )
+        else:
+            self.client = chromadb.PersistentClient(
+                path=CHROMA_DATA_PATH,
+                settings=Settings(allow_reset=True, anonymized_telemetry=False),
+                tenant=CHROMA_TENANT,
+                database=CHROMA_DATABASE,
+            )
+
+    def has_collection(self, collection_name: str) -> bool:
+        # Check if the collection exists based on the collection name.
+        collections = self.client.list_collections()
+        return collection_name in [collection.name for collection in collections]
+
+    def delete_collection(self, collection_name: str):
+        # Delete the collection based on the collection name.
+        return self.client.delete_collection(name=collection_name)
+
+    def search(
+        self, collection_name: str, vectors: list[list[float | int]], limit: int
+    ) -> Optional[SearchResult]:
+        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
+        try:
+            collection = self.client.get_collection(name=collection_name)
+            if collection:
+                result = collection.query(
+                    query_embeddings=vectors,
+                    n_results=limit,
+                )
+
+                return SearchResult(
+                    **{
+                        "ids": result["ids"],
+                        "distances": result["distances"],
+                        "documents": result["documents"],
+                        "metadatas": result["metadatas"],
+                    }
+                )
+            return None
+        except Exception as e:
+            return None
+
+    def query(
+        self, collection_name: str, filter: dict, limit: Optional[int] = None
+    ) -> Optional[GetResult]:
+        # Query the items from the collection based on the filter.
+        try:
+            collection = self.client.get_collection(name=collection_name)
+            if collection:
+                result = collection.get(
+                    where=filter,
+                    limit=limit,
+                )
+
+                return GetResult(
+                    **{
+                        "ids": [result["ids"]],
+                        "documents": [result["documents"]],
+                        "metadatas": [result["metadatas"]],
+                    }
+                )
+            return None
+        except Exception as e:
+            print(e)
+            return None
+
+    def get(self, collection_name: str) -> Optional[GetResult]:
+        # Get all the items in the collection.
+        collection = self.client.get_collection(name=collection_name)
+        if collection:
+            result = collection.get()
+            return GetResult(
+                **{
+                    "ids": [result["ids"]],
+                    "documents": [result["documents"]],
+                    "metadatas": [result["metadatas"]],
+                }
+            )
+        return None
+
+    def insert(self, collection_name: str, items: list[VectorItem]):
+        # Insert the items into the collection, if the collection does not exist, it will be created.
+        collection = self.client.get_or_create_collection(
+            name=collection_name, metadata={"hnsw:space": "cosine"}
+        )
+
+        ids = [item["id"] for item in items]
+        documents = [item["text"] for item in items]
+        embeddings = [item["vector"] for item in items]
+        metadatas = [item["metadata"] for item in items]
+
+        for batch in create_batches(
+            api=self.client,
+            documents=documents,
+            embeddings=embeddings,
+            ids=ids,
+            metadatas=metadatas,
+        ):
+            collection.add(*batch)
+
+    def upsert(self, collection_name: str, items: list[VectorItem]):
+        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
+        collection = self.client.get_or_create_collection(
+            name=collection_name, metadata={"hnsw:space": "cosine"}
+        )
+
+        ids = [item["id"] for item in items]
+        documents = [item["text"] for item in items]
+        embeddings = [item["vector"] for item in items]
+        metadatas = [item["metadata"] for item in items]
+
+        collection.upsert(
+            ids=ids, documents=documents, embeddings=embeddings, metadatas=metadatas
+        )
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[list[str]] = None,
+        filter: Optional[dict] = None,
+    ):
+        # Delete the items from the collection based on the ids.
+        collection = self.client.get_collection(name=collection_name)
+        if collection:
+            if ids:
+                collection.delete(ids=ids)
+            elif filter:
+                collection.delete(where=filter)
+
+    def reset(self):
+        # Resets the database. This will delete all collections and item entries.
+        return self.client.reset()

backend/open_webui/apps/retrieval/vector/dbs/milvus.py

@@ -1,286 +1,286 @@
-from pymilvus import MilvusClient as Client
-from pymilvus import FieldSchema, DataType
-import json
-
-from typing import Optional
-
-from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
-from open_webui.config import (
-    MILVUS_URI,
-)
-
-
-class MilvusClient:
-    def __init__(self):
-        self.collection_prefix = "open_webui"
-        self.client = Client(uri=MILVUS_URI)
-
-    def _result_to_get_result(self, result) -> GetResult:
-        ids = []
-        documents = []
-        metadatas = []
-
-        for match in result:
-            _ids = []
-            _documents = []
-            _metadatas = []
-            for item in match:
-                _ids.append(item.get("id"))
-                _documents.append(item.get("data", {}).get("text"))
-                _metadatas.append(item.get("metadata"))
-
-            ids.append(_ids)
-            documents.append(_documents)
-            metadatas.append(_metadatas)
-
-        return GetResult(
-            **{
-                "ids": ids,
-                "documents": documents,
-                "metadatas": metadatas,
-            }
-        )
-
-    def _result_to_search_result(self, result) -> SearchResult:
-        ids = []
-        distances = []
-        documents = []
-        metadatas = []
-
-        for match in result:
-            _ids = []
-            _distances = []
-            _documents = []
-            _metadatas = []
-
-            for item in match:
-                _ids.append(item.get("id"))
-                _distances.append(item.get("distance"))
-                _documents.append(item.get("entity", {}).get("data", {}).get("text"))
-                _metadatas.append(item.get("entity", {}).get("metadata"))
-
-            ids.append(_ids)
-            distances.append(_distances)
-            documents.append(_documents)
-            metadatas.append(_metadatas)
-
-        return SearchResult(
-            **{
-                "ids": ids,
-                "distances": distances,
-                "documents": documents,
-                "metadatas": metadatas,
-            }
-        )
-
-    def _create_collection(self, collection_name: str, dimension: int):
-        schema = self.client.create_schema(
-            auto_id=False,
-            enable_dynamic_field=True,
-        )
-        schema.add_field(
-            field_name="id",
-            datatype=DataType.VARCHAR,
-            is_primary=True,
-            max_length=65535,
-        )
-        schema.add_field(
-            field_name="vector",
-            datatype=DataType.FLOAT_VECTOR,
-            dim=dimension,
-            description="vector",
-        )
-        schema.add_field(field_name="data", datatype=DataType.JSON, description="data")
-        schema.add_field(
-            field_name="metadata", datatype=DataType.JSON, description="metadata"
-        )
-
-        index_params = self.client.prepare_index_params()
-        index_params.add_index(
-            field_name="vector",
-            index_type="HNSW",
-            metric_type="COSINE",
-            params={"M": 16, "efConstruction": 100},
-        )
-
-        self.client.create_collection(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            schema=schema,
-            index_params=index_params,
-        )
-
-    def has_collection(self, collection_name: str) -> bool:
-        # Check if the collection exists based on the collection name.
-        collection_name = collection_name.replace("-", "_")
-        return self.client.has_collection(
-            collection_name=f"{self.collection_prefix}_{collection_name}"
-        )
-
-    def delete_collection(self, collection_name: str):
-        # Delete the collection based on the collection name.
-        collection_name = collection_name.replace("-", "_")
-        return self.client.drop_collection(
-            collection_name=f"{self.collection_prefix}_{collection_name}"
-        )
-
-    def search(
-        self, collection_name: str, vectors: list[list[float | int]], limit: int
-    ) -> Optional[SearchResult]:
-        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
-        collection_name = collection_name.replace("-", "_")
-        result = self.client.search(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            data=vectors,
-            limit=limit,
-            output_fields=["data", "metadata"],
-        )
-
-        return self._result_to_search_result(result)
-
-    def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
-        # Construct the filter string for querying
-        collection_name = collection_name.replace("-", "_")
-        if not self.has_collection(collection_name):
-            return None
-
-        filter_string = " && ".join(
-            [
-                f'metadata["{key}"] == {json.dumps(value)}'
-                for key, value in filter.items()
-            ]
-        )
-
-        max_limit = 16383  # The maximum number of records per request
-        all_results = []
-
-        if limit is None:
-            limit = float("inf")  # Use infinity as a placeholder for no limit
-
-        # Initialize offset and remaining to handle pagination
-        offset = 0
-        remaining = limit
-
-        try:
-            # Loop until there are no more items to fetch or the desired limit is reached
-            while remaining > 0:
-                print("remaining", remaining)
-                current_fetch = min(
-                    max_limit, remaining
-                )  # Determine how many items to fetch in this iteration
-
-                results = self.client.query(
-                    collection_name=f"{self.collection_prefix}_{collection_name}",
-                    filter=filter_string,
-                    output_fields=["*"],
-                    limit=current_fetch,
-                    offset=offset,
-                )
-
-                if not results:
-                    break
-
-                all_results.extend(results)
-                results_count = len(results)
-                remaining -= (
-                    results_count  # Decrease remaining by the number of items fetched
-                )
-                offset += results_count
-
-                # Break the loop if the results returned are less than the requested fetch count
-                if results_count < current_fetch:
-                    break
-
-            print(all_results)
-            return self._result_to_get_result([all_results])
-        except Exception as e:
-            print(e)
-            return None
-
-    def get(self, collection_name: str) -> Optional[GetResult]:
-        # Get all the items in the collection.
-        collection_name = collection_name.replace("-", "_")
-        result = self.client.query(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            filter='id != ""',
-        )
-        return self._result_to_get_result([result])
-
-    def insert(self, collection_name: str, items: list[VectorItem]):
-        # Insert the items into the collection, if the collection does not exist, it will be created.
-        collection_name = collection_name.replace("-", "_")
-        if not self.client.has_collection(
-            collection_name=f"{self.collection_prefix}_{collection_name}"
-        ):
-            self._create_collection(
-                collection_name=collection_name, dimension=len(items[0]["vector"])
-            )
-
-        return self.client.insert(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            data=[
-                {
-                    "id": item["id"],
-                    "vector": item["vector"],
-                    "data": {"text": item["text"]},
-                    "metadata": item["metadata"],
-                }
-                for item in items
-            ],
-        )
-
-    def upsert(self, collection_name: str, items: list[VectorItem]):
-        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
-        collection_name = collection_name.replace("-", "_")
-        if not self.client.has_collection(
-            collection_name=f"{self.collection_prefix}_{collection_name}"
-        ):
-            self._create_collection(
-                collection_name=collection_name, dimension=len(items[0]["vector"])
-            )
-
-        return self.client.upsert(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            data=[
-                {
-                    "id": item["id"],
-                    "vector": item["vector"],
-                    "data": {"text": item["text"]},
-                    "metadata": item["metadata"],
-                }
-                for item in items
-            ],
-        )
-
-    def delete(
-        self,
-        collection_name: str,
-        ids: Optional[list[str]] = None,
-        filter: Optional[dict] = None,
-    ):
-        # Delete the items from the collection based on the ids.
-        collection_name = collection_name.replace("-", "_")
-        if ids:
-            return self.client.delete(
-                collection_name=f"{self.collection_prefix}_{collection_name}",
-                ids=ids,
-            )
-        elif filter:
-            # Convert the filter dictionary to a string using JSON_CONTAINS.
-            filter_string = " && ".join(
-                [
-                    f'metadata["{key}"] == {json.dumps(value)}'
-                    for key, value in filter.items()
-                ]
-            )
-
-            return self.client.delete(
-                collection_name=f"{self.collection_prefix}_{collection_name}",
-                filter=filter_string,
-            )
-
-    def reset(self):
-        # Resets the database. This will delete all collections and item entries.
-        collection_names = self.client.list_collections()
-        for collection_name in collection_names:
-            if collection_name.startswith(self.collection_prefix):
-                self.client.drop_collection(collection_name=collection_name)
+from pymilvus import MilvusClient as Client
+from pymilvus import FieldSchema, DataType
+import json
+
+from typing import Optional
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import (
+    MILVUS_URI,
+)
+
+
+class MilvusClient:
+    def __init__(self):
+        self.collection_prefix = "open_webui"
+        self.client = Client(uri=MILVUS_URI)
+
+    def _result_to_get_result(self, result) -> GetResult:
+        ids = []
+        documents = []
+        metadatas = []
+
+        for match in result:
+            _ids = []
+            _documents = []
+            _metadatas = []
+            for item in match:
+                _ids.append(item.get("id"))
+                _documents.append(item.get("data", {}).get("text"))
+                _metadatas.append(item.get("metadata"))
+
+            ids.append(_ids)
+            documents.append(_documents)
+            metadatas.append(_metadatas)
+
+        return GetResult(
+            **{
+                "ids": ids,
+                "documents": documents,
+                "metadatas": metadatas,
+            }
+        )
+
+    def _result_to_search_result(self, result) -> SearchResult:
+        ids = []
+        distances = []
+        documents = []
+        metadatas = []
+
+        for match in result:
+            _ids = []
+            _distances = []
+            _documents = []
+            _metadatas = []
+
+            for item in match:
+                _ids.append(item.get("id"))
+                _distances.append(item.get("distance"))
+                _documents.append(item.get("entity", {}).get("data", {}).get("text"))
+                _metadatas.append(item.get("entity", {}).get("metadata"))
+
+            ids.append(_ids)
+            distances.append(_distances)
+            documents.append(_documents)
+            metadatas.append(_metadatas)
+
+        return SearchResult(
+            **{
+                "ids": ids,
+                "distances": distances,
+                "documents": documents,
+                "metadatas": metadatas,
+            }
+        )
+
+    def _create_collection(self, collection_name: str, dimension: int):
+        schema = self.client.create_schema(
+            auto_id=False,
+            enable_dynamic_field=True,
+        )
+        schema.add_field(
+            field_name="id",
+            datatype=DataType.VARCHAR,
+            is_primary=True,
+            max_length=65535,
+        )
+        schema.add_field(
+            field_name="vector",
+            datatype=DataType.FLOAT_VECTOR,
+            dim=dimension,
+            description="vector",
+        )
+        schema.add_field(field_name="data", datatype=DataType.JSON, description="data")
+        schema.add_field(
+            field_name="metadata", datatype=DataType.JSON, description="metadata"
+        )
+
+        index_params = self.client.prepare_index_params()
+        index_params.add_index(
+            field_name="vector",
+            index_type="HNSW",
+            metric_type="COSINE",
+            params={"M": 16, "efConstruction": 100},
+        )
+
+        self.client.create_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            schema=schema,
+            index_params=index_params,
+        )
+
+    def has_collection(self, collection_name: str) -> bool:
+        # Check if the collection exists based on the collection name.
+        collection_name = collection_name.replace("-", "_")
+        return self.client.has_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def delete_collection(self, collection_name: str):
+        # Delete the collection based on the collection name.
+        collection_name = collection_name.replace("-", "_")
+        return self.client.drop_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def search(
+        self, collection_name: str, vectors: list[list[float | int]], limit: int
+    ) -> Optional[SearchResult]:
+        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
+        collection_name = collection_name.replace("-", "_")
+        result = self.client.search(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            data=vectors,
+            limit=limit,
+            output_fields=["data", "metadata"],
+        )
+
+        return self._result_to_search_result(result)
+
+    def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
+        # Construct the filter string for querying
+        collection_name = collection_name.replace("-", "_")
+        if not self.has_collection(collection_name):
+            return None
+
+        filter_string = " && ".join(
+            [
+                f'metadata["{key}"] == {json.dumps(value)}'
+                for key, value in filter.items()
+            ]
+        )
+
+        max_limit = 16383  # The maximum number of records per request
+        all_results = []
+
+        if limit is None:
+            limit = float("inf")  # Use infinity as a placeholder for no limit
+
+        # Initialize offset and remaining to handle pagination
+        offset = 0
+        remaining = limit
+
+        try:
+            # Loop until there are no more items to fetch or the desired limit is reached
+            while remaining > 0:
+                print("remaining", remaining)
+                current_fetch = min(
+                    max_limit, remaining
+                )  # Determine how many items to fetch in this iteration
+
+                results = self.client.query(
+                    collection_name=f"{self.collection_prefix}_{collection_name}",
+                    filter=filter_string,
+                    output_fields=["*"],
+                    limit=current_fetch,
+                    offset=offset,
+                )
+
+                if not results:
+                    break
+
+                all_results.extend(results)
+                results_count = len(results)
+                remaining -= (
+                    results_count  # Decrease remaining by the number of items fetched
+                )
+                offset += results_count
+
+                # Break the loop if the results returned are less than the requested fetch count
+                if results_count < current_fetch:
+                    break
+
+            print(all_results)
+            return self._result_to_get_result([all_results])
+        except Exception as e:
+            print(e)
+            return None
+
+    def get(self, collection_name: str) -> Optional[GetResult]:
+        # Get all the items in the collection.
+        collection_name = collection_name.replace("-", "_")
+        result = self.client.query(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            filter='id != ""',
+        )
+        return self._result_to_get_result([result])
+
+    def insert(self, collection_name: str, items: list[VectorItem]):
+        # Insert the items into the collection, if the collection does not exist, it will be created.
+        collection_name = collection_name.replace("-", "_")
+        if not self.client.has_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        ):
+            self._create_collection(
+                collection_name=collection_name, dimension=len(items[0]["vector"])
+            )
+
+        return self.client.insert(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            data=[
+                {
+                    "id": item["id"],
+                    "vector": item["vector"],
+                    "data": {"text": item["text"]},
+                    "metadata": item["metadata"],
+                }
+                for item in items
+            ],
+        )
+
+    def upsert(self, collection_name: str, items: list[VectorItem]):
+        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
+        collection_name = collection_name.replace("-", "_")
+        if not self.client.has_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        ):
+            self._create_collection(
+                collection_name=collection_name, dimension=len(items[0]["vector"])
+            )
+
+        return self.client.upsert(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            data=[
+                {
+                    "id": item["id"],
+                    "vector": item["vector"],
+                    "data": {"text": item["text"]},
+                    "metadata": item["metadata"],
+                }
+                for item in items
+            ],
+        )
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[list[str]] = None,
+        filter: Optional[dict] = None,
+    ):
+        # Delete the items from the collection based on the ids.
+        collection_name = collection_name.replace("-", "_")
+        if ids:
+            return self.client.delete(
+                collection_name=f"{self.collection_prefix}_{collection_name}",
+                ids=ids,
+            )
+        elif filter:
+            # Convert the filter dictionary to a string using JSON_CONTAINS.
+            filter_string = " && ".join(
+                [
+                    f'metadata["{key}"] == {json.dumps(value)}'
+                    for key, value in filter.items()
+                ]
+            )
+
+            return self.client.delete(
+                collection_name=f"{self.collection_prefix}_{collection_name}",
+                filter=filter_string,
+            )
+
+    def reset(self):
+        # Resets the database. This will delete all collections and item entries.
+        collection_names = self.client.list_collections()
+        for collection_name in collection_names:
+            if collection_name.startswith(self.collection_prefix):
+                self.client.drop_collection(collection_name=collection_name)

backend/open_webui/apps/retrieval/vector/dbs/qdrant.py

@@ -1,179 +1,179 @@
-from typing import Optional
-
-from qdrant_client import QdrantClient as Qclient
-from qdrant_client.http.models import PointStruct
-from qdrant_client.models import models
-
-from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
-from open_webui.config import QDRANT_URI
-
-NO_LIMIT = 999999999
-
-
-class QdrantClient:
-    def __init__(self):
-        self.collection_prefix = "open-webui"
-        self.QDRANT_URI = QDRANT_URI
-        self.client = Qclient(url=self.QDRANT_URI) if self.QDRANT_URI else None
-
-    def _result_to_get_result(self, points) -> GetResult:
-        ids = []
-        documents = []
-        metadatas = []
-
-        for point in points:
-            payload = point.payload
-            ids.append(point.id)
-            documents.append(payload["text"])
-            metadatas.append(payload["metadata"])
-
-        return GetResult(
-            **{
-                "ids": [ids],
-                "documents": [documents],
-                "metadatas": [metadatas],
-            }
-        )
-
-    def _create_collection(self, collection_name: str, dimension: int):
-        collection_name_with_prefix = f"{self.collection_prefix}_{collection_name}"
-        self.client.create_collection(
-            collection_name=collection_name_with_prefix,
-            vectors_config=models.VectorParams(
-                size=dimension, distance=models.Distance.COSINE
-            ),
-        )
-
-        print(f"collection {collection_name_with_prefix} successfully created!")
-
-    def _create_collection_if_not_exists(self, collection_name, dimension):
-        if not self.has_collection(collection_name=collection_name):
-            self._create_collection(
-                collection_name=collection_name, dimension=dimension
-            )
-
-    def _create_points(self, items: list[VectorItem]):
-        return [
-            PointStruct(
-                id=item["id"],
-                vector=item["vector"],
-                payload={"text": item["text"], "metadata": item["metadata"]},
-            )
-            for item in items
-        ]
-
-    def has_collection(self, collection_name: str) -> bool:
-        return self.client.collection_exists(
-            f"{self.collection_prefix}_{collection_name}"
-        )
-
-    def delete_collection(self, collection_name: str):
-        return self.client.delete_collection(
-            collection_name=f"{self.collection_prefix}_{collection_name}"
-        )
-
-    def search(
-        self, collection_name: str, vectors: list[list[float | int]], limit: int
-    ) -> Optional[SearchResult]:
-        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
-        if limit is None:
-            limit = NO_LIMIT  # otherwise qdrant would set limit to 10!
-
-        query_response = self.client.query_points(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            query=vectors[0],
-            limit=limit,
-        )
-        get_result = self._result_to_get_result(query_response.points)
-        return SearchResult(
-            ids=get_result.ids,
-            documents=get_result.documents,
-            metadatas=get_result.metadatas,
-            distances=[[point.score for point in query_response.points]],
-        )
-
-    def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
-        # Construct the filter string for querying
-        if not self.has_collection(collection_name):
-            return None
-        try:
-            if limit is None:
-                limit = NO_LIMIT  # otherwise qdrant would set limit to 10!
-
-            field_conditions = []
-            for key, value in filter.items():
-                field_conditions.append(
-                    models.FieldCondition(
-                        key=f"metadata.{key}", match=models.MatchValue(value=value)
-                    )
-                )
-
-            points = self.client.query_points(
-                collection_name=f"{self.collection_prefix}_{collection_name}",
-                query_filter=models.Filter(should=field_conditions),
-                limit=limit,
-            )
-            return self._result_to_get_result(points.points)
-        except Exception as e:
-            print(e)
-            return None
-
-    def get(self, collection_name: str) -> Optional[GetResult]:
-        # Get all the items in the collection.
-        points = self.client.query_points(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            limit=NO_LIMIT,  # otherwise qdrant would set limit to 10!
-        )
-        return self._result_to_get_result(points.points)
-
-    def insert(self, collection_name: str, items: list[VectorItem]):
-        # Insert the items into the collection, if the collection does not exist, it will be created.
-        self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
-        points = self._create_points(items)
-        self.client.upload_points(f"{self.collection_prefix}_{collection_name}", points)
-
-    def upsert(self, collection_name: str, items: list[VectorItem]):
-        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
-        self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
-        points = self._create_points(items)
-        return self.client.upsert(f"{self.collection_prefix}_{collection_name}", points)
-
-    def delete(
-        self,
-        collection_name: str,
-        ids: Optional[list[str]] = None,
-        filter: Optional[dict] = None,
-    ):
-        # Delete the items from the collection based on the ids.
-        field_conditions = []
-
-        if ids:
-            for id_value in ids:
-                field_conditions.append(
-                    models.FieldCondition(
-                        key="metadata.id",
-                        match=models.MatchValue(value=id_value),
-                    ),
-                ),
-        elif filter:
-            for key, value in filter.items():
-                field_conditions.append(
-                    models.FieldCondition(
-                        key=f"metadata.{key}",
-                        match=models.MatchValue(value=value),
-                    ),
-                ),
-
-        return self.client.delete(
-            collection_name=f"{self.collection_prefix}_{collection_name}",
-            points_selector=models.FilterSelector(
-                filter=models.Filter(must=field_conditions)
-            ),
-        )
-
-    def reset(self):
-        # Resets the database. This will delete all collections and item entries.
-        collection_names = self.client.get_collections().collections
-        for collection_name in collection_names:
-            if collection_name.name.startswith(self.collection_prefix):
-                self.client.delete_collection(collection_name=collection_name.name)
+from typing import Optional
+
+from qdrant_client import QdrantClient as Qclient
+from qdrant_client.http.models import PointStruct
+from qdrant_client.models import models
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import QDRANT_URI
+
+NO_LIMIT = 999999999
+
+
+class QdrantClient:
+    def __init__(self):
+        self.collection_prefix = "open-webui"
+        self.QDRANT_URI = QDRANT_URI
+        self.client = Qclient(url=self.QDRANT_URI) if self.QDRANT_URI else None
+
+    def _result_to_get_result(self, points) -> GetResult:
+        ids = []
+        documents = []
+        metadatas = []
+
+        for point in points:
+            payload = point.payload
+            ids.append(point.id)
+            documents.append(payload["text"])
+            metadatas.append(payload["metadata"])
+
+        return GetResult(
+            **{
+                "ids": [ids],
+                "documents": [documents],
+                "metadatas": [metadatas],
+            }
+        )
+
+    def _create_collection(self, collection_name: str, dimension: int):
+        collection_name_with_prefix = f"{self.collection_prefix}_{collection_name}"
+        self.client.create_collection(
+            collection_name=collection_name_with_prefix,
+            vectors_config=models.VectorParams(
+                size=dimension, distance=models.Distance.COSINE
+            ),
+        )
+
+        print(f"collection {collection_name_with_prefix} successfully created!")
+
+    def _create_collection_if_not_exists(self, collection_name, dimension):
+        if not self.has_collection(collection_name=collection_name):
+            self._create_collection(
+                collection_name=collection_name, dimension=dimension
+            )
+
+    def _create_points(self, items: list[VectorItem]):
+        return [
+            PointStruct(
+                id=item["id"],
+                vector=item["vector"],
+                payload={"text": item["text"], "metadata": item["metadata"]},
+            )
+            for item in items
+        ]
+
+    def has_collection(self, collection_name: str) -> bool:
+        return self.client.collection_exists(
+            f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def delete_collection(self, collection_name: str):
+        return self.client.delete_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def search(
+        self, collection_name: str, vectors: list[list[float | int]], limit: int
+    ) -> Optional[SearchResult]:
+        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
+        if limit is None:
+            limit = NO_LIMIT  # otherwise qdrant would set limit to 10!
+
+        query_response = self.client.query_points(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            query=vectors[0],
+            limit=limit,
+        )
+        get_result = self._result_to_get_result(query_response.points)
+        return SearchResult(
+            ids=get_result.ids,
+            documents=get_result.documents,
+            metadatas=get_result.metadatas,
+            distances=[[point.score for point in query_response.points]],
+        )
+
+    def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
+        # Construct the filter string for querying
+        if not self.has_collection(collection_name):
+            return None
+        try:
+            if limit is None:
+                limit = NO_LIMIT  # otherwise qdrant would set limit to 10!
+
+            field_conditions = []
+            for key, value in filter.items():
+                field_conditions.append(
+                    models.FieldCondition(
+                        key=f"metadata.{key}", match=models.MatchValue(value=value)
+                    )
+                )
+
+            points = self.client.query_points(
+                collection_name=f"{self.collection_prefix}_{collection_name}",
+                query_filter=models.Filter(should=field_conditions),
+                limit=limit,
+            )
+            return self._result_to_get_result(points.points)
+        except Exception as e:
+            print(e)
+            return None
+
+    def get(self, collection_name: str) -> Optional[GetResult]:
+        # Get all the items in the collection.
+        points = self.client.query_points(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            limit=NO_LIMIT,  # otherwise qdrant would set limit to 10!
+        )
+        return self._result_to_get_result(points.points)
+
+    def insert(self, collection_name: str, items: list[VectorItem]):
+        # Insert the items into the collection, if the collection does not exist, it will be created.
+        self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
+        points = self._create_points(items)
+        self.client.upload_points(f"{self.collection_prefix}_{collection_name}", points)
+
+    def upsert(self, collection_name: str, items: list[VectorItem]):
+        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
+        self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
+        points = self._create_points(items)
+        return self.client.upsert(f"{self.collection_prefix}_{collection_name}", points)
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[list[str]] = None,
+        filter: Optional[dict] = None,
+    ):
+        # Delete the items from the collection based on the ids.
+        field_conditions = []
+
+        if ids:
+            for id_value in ids:
+                field_conditions.append(
+                    models.FieldCondition(
+                        key="metadata.id",
+                        match=models.MatchValue(value=id_value),
+                    ),
+                ),
+        elif filter:
+            for key, value in filter.items():
+                field_conditions.append(
+                    models.FieldCondition(
+                        key=f"metadata.{key}",
+                        match=models.MatchValue(value=value),
+                    ),
+                ),
+
+        return self.client.delete(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            points_selector=models.FilterSelector(
+                filter=models.Filter(must=field_conditions)
+            ),
+        )
+
+    def reset(self):
+        # Resets the database. This will delete all collections and item entries.
+        collection_names = self.client.get_collections().collections
+        for collection_name in collection_names:
+            if collection_name.name.startswith(self.collection_prefix):
+                self.client.delete_collection(collection_name=collection_name.name)

backend/open_webui/apps/retrieval/vector/main.py

@@ -1,19 +1,19 @@
-from pydantic import BaseModel
-from typing import Optional, List, Any
-
-
-class VectorItem(BaseModel):
-    id: str
-    text: str
-    vector: List[float | int]
-    metadata: Any
-
-
-class GetResult(BaseModel):
-    ids: Optional[List[List[str]]]
-    documents: Optional[List[List[str]]]
-    metadatas: Optional[List[List[Any]]]
-
-
-class SearchResult(GetResult):
-    distances: Optional[List[List[float | int]]]
+from pydantic import BaseModel
+from typing import Optional, List, Any
+
+
+class VectorItem(BaseModel):
+    id: str
+    text: str
+    vector: List[float | int]
+    metadata: Any
+
+
+class GetResult(BaseModel):
+    ids: Optional[List[List[str]]]
+    documents: Optional[List[List[str]]]
+    metadatas: Optional[List[List[Any]]]
+
+
+class SearchResult(GetResult):
+    distances: Optional[List[List[float | int]]]

backend/open_webui/apps/retrieval/web/brave.py

@@ -1,42 +1,42 @@
-import logging
-from typing import Optional
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_brave(
-    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
-) -> list[SearchResult]:
-    """Search using Brave's Search API and return the results as a list of SearchResult objects.
-
-    Args:
-        api_key (str): A Brave Search API key
-        query (str): The query to search for
-    """
-    url = "https://api.search.brave.com/res/v1/web/search"
-    headers = {
-        "Accept": "application/json",
-        "Accept-Encoding": "gzip",
-        "X-Subscription-Token": api_key,
-    }
-    params = {"q": query, "count": count}
-
-    response = requests.get(url, headers=headers, params=params)
-    response.raise_for_status()
-
-    json_response = response.json()
-    results = json_response.get("web", {}).get("results", [])
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-
-    return [
-        SearchResult(
-            link=result["url"], title=result.get("title"), snippet=result.get("snippet")
-        )
-        for result in results[:count]
-    ]
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_brave(
+    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """Search using Brave's Search API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Brave Search API key
+        query (str): The query to search for
+    """
+    url = "https://api.search.brave.com/res/v1/web/search"
+    headers = {
+        "Accept": "application/json",
+        "Accept-Encoding": "gzip",
+        "X-Subscription-Token": api_key,
+    }
+    params = {"q": query, "count": count}
+
+    response = requests.get(url, headers=headers, params=params)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = json_response.get("web", {}).get("results", [])
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("snippet")
+        )
+        for result in results[:count]
+    ]

backend/open_webui/apps/retrieval/web/duckduckgo.py

@@ -1,50 +1,50 @@
-import logging
-from typing import Optional
-
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from duckduckgo_search import DDGS
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_duckduckgo(
-    query: str, count: int, filter_list: Optional[list[str]] = None
-) -> list[SearchResult]:
-    """
-    Search using DuckDuckGo's Search API and return the results as a list of SearchResult objects.
-    Args:
-        query (str): The query to search for
-        count (int): The number of results to return
-
-    Returns:
-        list[SearchResult]: A list of search results
-    """
-    # Use the DDGS context manager to create a DDGS object
-    with DDGS() as ddgs:
-        # Use the ddgs.text() method to perform the search
-        ddgs_gen = ddgs.text(
-            query, safesearch="moderate", max_results=count, backend="api"
-        )
-        # Check if there are search results
-        if ddgs_gen:
-            # Convert the search results into a list
-            search_results = [r for r in ddgs_gen]
-
-    # Create an empty list to store the SearchResult objects
-    results = []
-    # Iterate over each search result
-    for result in search_results:
-        # Create a SearchResult object and append it to the results list
-        results.append(
-            SearchResult(
-                link=result["href"],
-                title=result.get("title"),
-                snippet=result.get("body"),
-            )
-        )
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-    # Return the list of search results
-    return results
+import logging
+from typing import Optional
+
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from duckduckgo_search import DDGS
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_duckduckgo(
+    query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """
+    Search using DuckDuckGo's Search API and return the results as a list of SearchResult objects.
+    Args:
+        query (str): The query to search for
+        count (int): The number of results to return
+
+    Returns:
+        list[SearchResult]: A list of search results
+    """
+    # Use the DDGS context manager to create a DDGS object
+    with DDGS() as ddgs:
+        # Use the ddgs.text() method to perform the search
+        ddgs_gen = ddgs.text(
+            query, safesearch="moderate", max_results=count, backend="api"
+        )
+        # Check if there are search results
+        if ddgs_gen:
+            # Convert the search results into a list
+            search_results = [r for r in ddgs_gen]
+
+    # Create an empty list to store the SearchResult objects
+    results = []
+    # Iterate over each search result
+    for result in search_results:
+        # Create a SearchResult object and append it to the results list
+        results.append(
+            SearchResult(
+                link=result["href"],
+                title=result.get("title"),
+                snippet=result.get("body"),
+            )
+        )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    # Return the list of search results
+    return results

backend/open_webui/apps/retrieval/web/google_pse.py

@@ -1,50 +1,50 @@
-import logging
-from typing import Optional
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_google_pse(
-    api_key: str,
-    search_engine_id: str,
-    query: str,
-    count: int,
-    filter_list: Optional[list[str]] = None,
-) -> list[SearchResult]:
-    """Search using Google's Programmable Search Engine API and return the results as a list of SearchResult objects.
-
-    Args:
-        api_key (str): A Programmable Search Engine API key
-        search_engine_id (str): A Programmable Search Engine ID
-        query (str): The query to search for
-    """
-    url = "https://www.googleapis.com/customsearch/v1"
-
-    headers = {"Content-Type": "application/json"}
-    params = {
-        "cx": search_engine_id,
-        "q": query,
-        "key": api_key,
-        "num": count,
-    }
-
-    response = requests.request("GET", url, headers=headers, params=params)
-    response.raise_for_status()
-
-    json_response = response.json()
-    results = json_response.get("items", [])
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-    return [
-        SearchResult(
-            link=result["link"],
-            title=result.get("title"),
-            snippet=result.get("snippet"),
-        )
-        for result in results
-    ]
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_google_pse(
+    api_key: str,
+    search_engine_id: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    """Search using Google's Programmable Search Engine API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Programmable Search Engine API key
+        search_engine_id (str): A Programmable Search Engine ID
+        query (str): The query to search for
+    """
+    url = "https://www.googleapis.com/customsearch/v1"
+
+    headers = {"Content-Type": "application/json"}
+    params = {
+        "cx": search_engine_id,
+        "q": query,
+        "key": api_key,
+        "num": count,
+    }
+
+    response = requests.request("GET", url, headers=headers, params=params)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = json_response.get("items", [])
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"],
+            title=result.get("title"),
+            snippet=result.get("snippet"),
+        )
+        for result in results
+    ]

backend/open_webui/apps/retrieval/web/jina_search.py

@@ -1,41 +1,41 @@
-import logging
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult
-from open_webui.env import SRC_LOG_LEVELS
-from yarl import URL
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_jina(query: str, count: int) -> list[SearchResult]:
-    """
-    Search using Jina's Search API and return the results as a list of SearchResult objects.
-    Args:
-        query (str): The query to search for
-        count (int): The number of results to return
-
-    Returns:
-        list[SearchResult]: A list of search results
-    """
-    jina_search_endpoint = "https://s.jina.ai/"
-    headers = {
-        "Accept": "application/json",
-    }
-    url = str(URL(jina_search_endpoint + query))
-    response = requests.get(url, headers=headers)
-    response.raise_for_status()
-    data = response.json()
-
-    results = []
-    for result in data["data"][:count]:
-        results.append(
-            SearchResult(
-                link=result["url"],
-                title=result.get("title"),
-                snippet=result.get("content"),
-            )
-        )
-
-    return results
+import logging
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult
+from open_webui.env import SRC_LOG_LEVELS
+from yarl import URL
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_jina(query: str, count: int) -> list[SearchResult]:
+    """
+    Search using Jina's Search API and return the results as a list of SearchResult objects.
+    Args:
+        query (str): The query to search for
+        count (int): The number of results to return
+
+    Returns:
+        list[SearchResult]: A list of search results
+    """
+    jina_search_endpoint = "https://s.jina.ai/"
+    headers = {
+        "Accept": "application/json",
+    }
+    url = str(URL(jina_search_endpoint + query))
+    response = requests.get(url, headers=headers)
+    response.raise_for_status()
+    data = response.json()
+
+    results = []
+    for result in data["data"][:count]:
+        results.append(
+            SearchResult(
+                link=result["url"],
+                title=result.get("title"),
+                snippet=result.get("content"),
+            )
+        )
+
+    return results

backend/open_webui/apps/retrieval/web/main.py

@@ -1,22 +1,22 @@
-from typing import Optional
-from urllib.parse import urlparse
-
-from pydantic import BaseModel
-
-
-def get_filtered_results(results, filter_list):
-    if not filter_list:
-        return results
-    filtered_results = []
-    for result in results:
-        url = result.get("url") or result.get("link", "")
-        domain = urlparse(url).netloc
-        if any(domain.endswith(filtered_domain) for filtered_domain in filter_list):
-            filtered_results.append(result)
-    return filtered_results
-
-
-class SearchResult(BaseModel):
-    link: str
-    title: Optional[str]
-    snippet: Optional[str]
+from typing import Optional
+from urllib.parse import urlparse
+
+from pydantic import BaseModel
+
+
+def get_filtered_results(results, filter_list):
+    if not filter_list:
+        return results
+    filtered_results = []
+    for result in results:
+        url = result.get("url") or result.get("link", "")
+        domain = urlparse(url).netloc
+        if any(domain.endswith(filtered_domain) for filtered_domain in filter_list):
+            filtered_results.append(result)
+    return filtered_results
+
+
+class SearchResult(BaseModel):
+    link: str
+    title: Optional[str]
+    snippet: Optional[str]

backend/open_webui/apps/retrieval/web/searchapi.py

@@ -1,48 +1,48 @@
-import logging
-from typing import Optional
-from urllib.parse import urlencode
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_searchapi(
-    api_key: str,
-    engine: str,
-    query: str,
-    count: int,
-    filter_list: Optional[list[str]] = None,
-) -> list[SearchResult]:
-    """Search using searchapi.io's API and return the results as a list of SearchResult objects.
-
-    Args:
-      api_key (str): A searchapi.io API key
-      query (str): The query to search for
-    """
-    url = "https://www.searchapi.io/api/v1/search"
-
-    engine = engine or "google"
-
-    payload = {"engine": engine, "q": query, "api_key": api_key}
-
-    url = f"{url}?{urlencode(payload)}"
-    response = requests.request("GET", url)
-
-    json_response = response.json()
-    log.info(f"results from searchapi search: {json_response}")
-
-    results = sorted(
-        json_response.get("organic_results", []), key=lambda x: x.get("position", 0)
-    )
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-    return [
-        SearchResult(
-            link=result["link"], title=result["title"], snippet=result["snippet"]
-        )
-        for result in results[:count]
-    ]
+import logging
+from typing import Optional
+from urllib.parse import urlencode
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_searchapi(
+    api_key: str,
+    engine: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    """Search using searchapi.io's API and return the results as a list of SearchResult objects.
+
+    Args:
+      api_key (str): A searchapi.io API key
+      query (str): The query to search for
+    """
+    url = "https://www.searchapi.io/api/v1/search"
+
+    engine = engine or "google"
+
+    payload = {"engine": engine, "q": query, "api_key": api_key}
+
+    url = f"{url}?{urlencode(payload)}"
+    response = requests.request("GET", url)
+
+    json_response = response.json()
+    log.info(f"results from searchapi search: {json_response}")
+
+    results = sorted(
+        json_response.get("organic_results", []), key=lambda x: x.get("position", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"], title=result["title"], snippet=result["snippet"]
+        )
+        for result in results[:count]
+    ]

backend/open_webui/apps/retrieval/web/searxng.py

@@ -1,91 +1,91 @@
-import logging
-from typing import Optional
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_searxng(
-    query_url: str,
-    query: str,
-    count: int,
-    filter_list: Optional[list[str]] = None,
-    **kwargs,
-) -> list[SearchResult]:
-    """
-    Search a SearXNG instance for a given query and return the results as a list of SearchResult objects.
-
-    The function allows passing additional parameters such as language or time_range to tailor the search result.
-
-    Args:
-        query_url (str): The base URL of the SearXNG server.
-        query (str): The search term or question to find in the SearXNG database.
-        count (int): The maximum number of results to retrieve from the search.
-
-    Keyword Args:
-        language (str): Language filter for the search results; e.g., "en-US". Defaults to an empty string.
-        safesearch (int): Safe search filter for safer web results; 0 = off, 1 = moderate, 2 = strict. Defaults to 1 (moderate).
-        time_range (str): Time range for filtering results by date; e.g., "2023-04-05..today" or "all-time". Defaults to ''.
-        categories: (Optional[list[str]]): Specific categories within which the search should be performed, defaulting to an empty string if not provided.
-
-    Returns:
-        list[SearchResult]: A list of SearchResults sorted by relevance score in descending order.
-
-    Raise:
-        requests.exceptions.RequestException: If a request error occurs during the search process.
-    """
-
-    # Default values for optional parameters are provided as empty strings or None when not specified.
-    language = kwargs.get("language", "en-US")
-    safesearch = kwargs.get("safesearch", "1")
-    time_range = kwargs.get("time_range", "")
-    categories = "".join(kwargs.get("categories", []))
-
-    params = {
-        "q": query,
-        "format": "json",
-        "pageno": 1,
-        "safesearch": safesearch,
-        "language": language,
-        "time_range": time_range,
-        "categories": categories,
-        "theme": "simple",
-        "image_proxy": 0,
-    }
-
-    # Legacy query format
-    if "<query>" in query_url:
-        # Strip all query parameters from the URL
-        query_url = query_url.split("?")[0]
-
-    log.debug(f"searching {query_url}")
-
-    response = requests.get(
-        query_url,
-        headers={
-            "User-Agent": "Open WebUI (https://github.com/open-webui/open-webui) RAG Bot",
-            "Accept": "text/html",
-            "Accept-Encoding": "gzip, deflate",
-            "Accept-Language": "en-US,en;q=0.5",
-            "Connection": "keep-alive",
-        },
-        params=params,
-    )
-
-    response.raise_for_status()  # Raise an exception for HTTP errors.
-
-    json_response = response.json()
-    results = json_response.get("results", [])
-    sorted_results = sorted(results, key=lambda x: x.get("score", 0), reverse=True)
-    if filter_list:
-        sorted_results = get_filtered_results(sorted_results, filter_list)
-    return [
-        SearchResult(
-            link=result["url"], title=result.get("title"), snippet=result.get("content")
-        )
-        for result in sorted_results[:count]
-    ]
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_searxng(
+    query_url: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+    **kwargs,
+) -> list[SearchResult]:
+    """
+    Search a SearXNG instance for a given query and return the results as a list of SearchResult objects.
+
+    The function allows passing additional parameters such as language or time_range to tailor the search result.
+
+    Args:
+        query_url (str): The base URL of the SearXNG server.
+        query (str): The search term or question to find in the SearXNG database.
+        count (int): The maximum number of results to retrieve from the search.
+
+    Keyword Args:
+        language (str): Language filter for the search results; e.g., "en-US". Defaults to an empty string.
+        safesearch (int): Safe search filter for safer web results; 0 = off, 1 = moderate, 2 = strict. Defaults to 1 (moderate).
+        time_range (str): Time range for filtering results by date; e.g., "2023-04-05..today" or "all-time". Defaults to ''.
+        categories: (Optional[list[str]]): Specific categories within which the search should be performed, defaulting to an empty string if not provided.
+
+    Returns:
+        list[SearchResult]: A list of SearchResults sorted by relevance score in descending order.
+
+    Raise:
+        requests.exceptions.RequestException: If a request error occurs during the search process.
+    """
+
+    # Default values for optional parameters are provided as empty strings or None when not specified.
+    language = kwargs.get("language", "en-US")
+    safesearch = kwargs.get("safesearch", "1")
+    time_range = kwargs.get("time_range", "")
+    categories = "".join(kwargs.get("categories", []))
+
+    params = {
+        "q": query,
+        "format": "json",
+        "pageno": 1,
+        "safesearch": safesearch,
+        "language": language,
+        "time_range": time_range,
+        "categories": categories,
+        "theme": "simple",
+        "image_proxy": 0,
+    }
+
+    # Legacy query format
+    if "<query>" in query_url:
+        # Strip all query parameters from the URL
+        query_url = query_url.split("?")[0]
+
+    log.debug(f"searching {query_url}")
+
+    response = requests.get(
+        query_url,
+        headers={
+            "User-Agent": "Open WebUI (https://github.com/open-webui/open-webui) RAG Bot",
+            "Accept": "text/html",
+            "Accept-Encoding": "gzip, deflate",
+            "Accept-Language": "en-US,en;q=0.5",
+            "Connection": "keep-alive",
+        },
+        params=params,
+    )
+
+    response.raise_for_status()  # Raise an exception for HTTP errors.
+
+    json_response = response.json()
+    results = json_response.get("results", [])
+    sorted_results = sorted(results, key=lambda x: x.get("score", 0), reverse=True)
+    if filter_list:
+        sorted_results = get_filtered_results(sorted_results, filter_list)
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("content")
+        )
+        for result in sorted_results[:count]
+    ]

backend/open_webui/apps/retrieval/web/serper.py

@@ -1,43 +1,43 @@
-import json
-import logging
-from typing import Optional
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_serper(
-    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
-) -> list[SearchResult]:
-    """Search using serper.dev's API and return the results as a list of SearchResult objects.
-
-    Args:
-        api_key (str): A serper.dev API key
-        query (str): The query to search for
-    """
-    url = "https://google.serper.dev/search"
-
-    payload = json.dumps({"q": query})
-    headers = {"X-API-KEY": api_key, "Content-Type": "application/json"}
-
-    response = requests.request("POST", url, headers=headers, data=payload)
-    response.raise_for_status()
-
-    json_response = response.json()
-    results = sorted(
-        json_response.get("organic", []), key=lambda x: x.get("position", 0)
-    )
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-    return [
-        SearchResult(
-            link=result["link"],
-            title=result.get("title"),
-            snippet=result.get("description"),
-        )
-        for result in results[:count]
-    ]
+import json
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_serper(
+    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """Search using serper.dev's API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A serper.dev API key
+        query (str): The query to search for
+    """
+    url = "https://google.serper.dev/search"
+
+    payload = json.dumps({"q": query})
+    headers = {"X-API-KEY": api_key, "Content-Type": "application/json"}
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = sorted(
+        json_response.get("organic", []), key=lambda x: x.get("position", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"],
+            title=result.get("title"),
+            snippet=result.get("description"),
+        )
+        for result in results[:count]
+    ]

backend/open_webui/apps/retrieval/web/serply.py

@@ -1,69 +1,69 @@
-import logging
-from typing import Optional
-from urllib.parse import urlencode
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_serply(
-    api_key: str,
-    query: str,
-    count: int,
-    hl: str = "us",
-    limit: int = 10,
-    device_type: str = "desktop",
-    proxy_location: str = "US",
-    filter_list: Optional[list[str]] = None,
-) -> list[SearchResult]:
-    """Search using serper.dev's API and return the results as a list of SearchResult objects.
-
-    Args:
-        api_key (str): A serply.io API key
-        query (str): The query to search for
-        hl (str): Host Language code to display results in (reference https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages)
-        limit (int): The maximum number of results to return [10-100, defaults to 10]
-    """
-    log.info("Searching with Serply")
-
-    url = "https://api.serply.io/v1/search/"
-
-    query_payload = {
-        "q": query,
-        "language": "en",
-        "num": limit,
-        "gl": proxy_location.upper(),
-        "hl": hl.lower(),
-    }
-
-    url = f"{url}{urlencode(query_payload)}"
-    headers = {
-        "X-API-KEY": api_key,
-        "X-User-Agent": device_type,
-        "User-Agent": "open-webui",
-        "X-Proxy-Location": proxy_location,
-    }
-
-    response = requests.request("GET", url, headers=headers)
-    response.raise_for_status()
-
-    json_response = response.json()
-    log.info(f"results from serply search: {json_response}")
-
-    results = sorted(
-        json_response.get("results", []), key=lambda x: x.get("realPosition", 0)
-    )
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-    return [
-        SearchResult(
-            link=result["link"],
-            title=result.get("title"),
-            snippet=result.get("description"),
-        )
-        for result in results[:count]
-    ]
+import logging
+from typing import Optional
+from urllib.parse import urlencode
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_serply(
+    api_key: str,
+    query: str,
+    count: int,
+    hl: str = "us",
+    limit: int = 10,
+    device_type: str = "desktop",
+    proxy_location: str = "US",
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    """Search using serper.dev's API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A serply.io API key
+        query (str): The query to search for
+        hl (str): Host Language code to display results in (reference https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages)
+        limit (int): The maximum number of results to return [10-100, defaults to 10]
+    """
+    log.info("Searching with Serply")
+
+    url = "https://api.serply.io/v1/search/"
+
+    query_payload = {
+        "q": query,
+        "language": "en",
+        "num": limit,
+        "gl": proxy_location.upper(),
+        "hl": hl.lower(),
+    }
+
+    url = f"{url}{urlencode(query_payload)}"
+    headers = {
+        "X-API-KEY": api_key,
+        "X-User-Agent": device_type,
+        "User-Agent": "open-webui",
+        "X-Proxy-Location": proxy_location,
+    }
+
+    response = requests.request("GET", url, headers=headers)
+    response.raise_for_status()
+
+    json_response = response.json()
+    log.info(f"results from serply search: {json_response}")
+
+    results = sorted(
+        json_response.get("results", []), key=lambda x: x.get("realPosition", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"],
+            title=result.get("title"),
+            snippet=result.get("description"),
+        )
+        for result in results[:count]
+    ]

backend/open_webui/apps/retrieval/web/serpstack.py

@@ -1,48 +1,48 @@
-import logging
-from typing import Optional
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_serpstack(
-    api_key: str,
-    query: str,
-    count: int,
-    filter_list: Optional[list[str]] = None,
-    https_enabled: bool = True,
-) -> list[SearchResult]:
-    """Search using serpstack.com's and return the results as a list of SearchResult objects.
-
-    Args:
-        api_key (str): A serpstack.com API key
-        query (str): The query to search for
-        https_enabled (bool): Whether to use HTTPS or HTTP for the API request
-    """
-    url = f"{'https' if https_enabled else 'http'}://api.serpstack.com/search"
-
-    headers = {"Content-Type": "application/json"}
-    params = {
-        "access_key": api_key,
-        "query": query,
-    }
-
-    response = requests.request("POST", url, headers=headers, params=params)
-    response.raise_for_status()
-
-    json_response = response.json()
-    results = sorted(
-        json_response.get("organic_results", []), key=lambda x: x.get("position", 0)
-    )
-    if filter_list:
-        results = get_filtered_results(results, filter_list)
-    return [
-        SearchResult(
-            link=result["url"], title=result.get("title"), snippet=result.get("snippet")
-        )
-        for result in results[:count]
-    ]
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_serpstack(
+    api_key: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+    https_enabled: bool = True,
+) -> list[SearchResult]:
+    """Search using serpstack.com's and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A serpstack.com API key
+        query (str): The query to search for
+        https_enabled (bool): Whether to use HTTPS or HTTP for the API request
+    """
+    url = f"{'https' if https_enabled else 'http'}://api.serpstack.com/search"
+
+    headers = {"Content-Type": "application/json"}
+    params = {
+        "access_key": api_key,
+        "query": query,
+    }
+
+    response = requests.request("POST", url, headers=headers, params=params)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = sorted(
+        json_response.get("organic_results", []), key=lambda x: x.get("position", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("snippet")
+        )
+        for result in results[:count]
+    ]

backend/open_webui/apps/retrieval/web/tavily.py

@@ -1,38 +1,38 @@
-import logging
-
-import requests
-from open_webui.apps.retrieval.web.main import SearchResult
-from open_webui.env import SRC_LOG_LEVELS
-
-log = logging.getLogger(__name__)
-log.setLevel(SRC_LOG_LEVELS["RAG"])
-
-
-def search_tavily(api_key: str, query: str, count: int) -> list[SearchResult]:
-    """Search using Tavily's Search API and return the results as a list of SearchResult objects.
-
-    Args:
-        api_key (str): A Tavily Search API key
-        query (str): The query to search for
-
-    Returns:
-        list[SearchResult]: A list of search results
-    """
-    url = "https://api.tavily.com/search"
-    data = {"query": query, "api_key": api_key}
-
-    response = requests.post(url, json=data)
-    response.raise_for_status()
-
-    json_response = response.json()
-
-    raw_search_results = json_response.get("results", [])
-
-    return [
-        SearchResult(
-            link=result["url"],
-            title=result.get("title", ""),
-            snippet=result.get("content"),
-        )
-        for result in raw_search_results[:count]
-    ]
+import logging
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_tavily(api_key: str, query: str, count: int) -> list[SearchResult]:
+    """Search using Tavily's Search API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Tavily Search API key
+        query (str): The query to search for
+
+    Returns:
+        list[SearchResult]: A list of search results
+    """
+    url = "https://api.tavily.com/search"
+    data = {"query": query, "api_key": api_key}
+
+    response = requests.post(url, json=data)
+    response.raise_for_status()
+
+    json_response = response.json()
+
+    raw_search_results = json_response.get("results", [])
+
+    return [
+        SearchResult(
+            link=result["url"],
+            title=result.get("title", ""),
+            snippet=result.get("content"),
+        )
+        for result in raw_search_results[:count]
+    ]

backend/open_webui/apps/retrieval/web/testdata/google_pse.json

@@ -1,442 +1,442 @@
-{
-	"kind": "customsearch#search",
-	"url": {
-		"type": "application/json",
-		"template": "https://www.googleapis.com/customsearch/v1?q={searchTerms}&num={count?}&start={startIndex?}&lr={language?}&safe={safe?}&cx={cx?}&sort={sort?}&filter={filter?}&gl={gl?}&cr={cr?}&googlehost={googleHost?}&c2coff={disableCnTwTranslation?}&hq={hq?}&hl={hl?}&siteSearch={siteSearch?}&siteSearchFilter={siteSearchFilter?}&exactTerms={exactTerms?}&excludeTerms={excludeTerms?}&linkSite={linkSite?}&orTerms={orTerms?}&dateRestrict={dateRestrict?}&lowRange={lowRange?}&highRange={highRange?}&searchType={searchType}&fileType={fileType?}&rights={rights?}&imgSize={imgSize?}&imgType={imgType?}&imgColorType={imgColorType?}&imgDominantColor={imgDominantColor?}&alt=json"
-	},
-	"queries": {
-		"request": [
-			{
-				"title": "Google Custom Search - lectures",
-				"totalResults": "2450000000",
-				"searchTerms": "lectures",
-				"count": 10,
-				"startIndex": 1,
-				"inputEncoding": "utf8",
-				"outputEncoding": "utf8",
-				"safe": "off",
-				"cx": "0473ef98502d44e18"
-			}
-		],
-		"nextPage": [
-			{
-				"title": "Google Custom Search - lectures",
-				"totalResults": "2450000000",
-				"searchTerms": "lectures",
-				"count": 10,
-				"startIndex": 11,
-				"inputEncoding": "utf8",
-				"outputEncoding": "utf8",
-				"safe": "off",
-				"cx": "0473ef98502d44e18"
-			}
-		]
-	},
-	"context": {
-		"title": "LLM Search"
-	},
-	"searchInformation": {
-		"searchTime": 0.445959,
-		"formattedSearchTime": "0.45",
-		"totalResults": "2450000000",
-		"formattedTotalResults": "2,450,000,000"
-	},
-	"items": [
-		{
-			"kind": "customsearch#result",
-			"title": "The Feynman Lectures on Physics",
-			"htmlTitle": "The Feynman \u003cb\u003eLectures\u003c/b\u003e on Physics",
-			"link": "https://www.feynmanlectures.caltech.edu/",
-			"displayLink": "www.feynmanlectures.caltech.edu",
-			"snippet": "This edition has been designed for ease of reading on devices of any size or shape; text, figures and equations can all be zoomed without degradation.",
-			"htmlSnippet": "This edition has been designed for ease of reading on devices of any size or shape; text, figures and equations can all be zoomed without degradation.",
-			"cacheId": "CyXMWYWs9UEJ",
-			"formattedUrl": "https://www.feynmanlectures.caltech.edu/",
-			"htmlFormattedUrl": "https://www.feynman\u003cb\u003electures\u003c/b\u003e.caltech.edu/",
-			"pagemap": {
-				"metatags": [
-					{
-						"viewport": "width=device-width, initial-scale=1.0"
-					}
-				]
-			}
-		},
-		{
-			"kind": "customsearch#result",
-			"title": "Video Lectures",
-			"htmlTitle": "Video \u003cb\u003eLectures\u003c/b\u003e",
-			"link": "https://www.reddit.com/r/lectures/",
-			"displayLink": "www.reddit.com",
-			"snippet": "r/lectures: This subreddit is all about video lectures, talks and interesting public speeches. The topics include mathematics, physics, computer…",
-			"htmlSnippet": "r/\u003cb\u003electures\u003c/b\u003e: This subreddit is all about video \u003cb\u003electures\u003c/b\u003e, talks and interesting public speeches. The topics include mathematics, physics, computer…",
-			"formattedUrl": "https://www.reddit.com/r/lectures/",
-			"htmlFormattedUrl": "https://www.reddit.com/r/\u003cb\u003electures\u003c/b\u003e/",
-			"pagemap": {
-				"cse_thumbnail": [
-					{
-						"src": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTZtOjhfkgUKQbL3DZxe5F6OVsgeDNffleObjJ7n9RllKQTSsimax7VIaY&s",
-						"width": "192",
-						"height": "192"
-					}
-				],
-				"metatags": [
-					{
-						"og:image": "https://www.redditstatic.com/shreddit/assets/favicon/192x192.png",
-						"theme-color": "#000000",
-						"og:image:width": "256",
-						"og:type": "website",
-						"twitter:card": "summary",
-						"twitter:title": "r/lectures",
-						"og:site_name": "Reddit",
-						"og:title": "r/lectures",
-						"og:image:height": "256",
-						"bingbot": "noarchive",
-						"msapplication-navbutton-color": "#000000",
-						"og:description": "This subreddit is all about video lectures, talks and interesting public speeches.\n\nThe topics include mathematics, physics, computer science, programming, engineering, biology, medicine, economics, politics, social sciences, and any other subjects!",
-						"twitter:image": "https://www.redditstatic.com/shreddit/assets/favicon/192x192.png",
-						"apple-mobile-web-app-status-bar-style": "black",
-						"twitter:site": "@reddit",
-						"viewport": "width=device-width, initial-scale=1, viewport-fit=cover",
-						"apple-mobile-web-app-capable": "yes",
-						"og:ttl": "600",
-						"og:url": "https://www.reddit.com/r/lectures/"
-					}
-				],
-				"cse_image": [
-					{
-						"src": "https://www.redditstatic.com/shreddit/assets/favicon/192x192.png"
-					}
-				]
-			}
-		},
-		{
-			"kind": "customsearch#result",
-			"title": "Lectures & Discussions | Flint Institute of Arts",
-			"htmlTitle": "\u003cb\u003eLectures\u003c/b\u003e & Discussions | Flint Institute of Arts",
-			"link": "https://flintarts.org/events/lectures",
-			"displayLink": "flintarts.org",
-			"snippet": "It will trace the intricate relationship between jewelry, attire, and the expression of personal identity, social hierarchy, and spiritual belief systems that ...",
-			"htmlSnippet": "It will trace the intricate relationship between jewelry, attire, and the expression of personal identity, social hierarchy, and spiritual belief systems that ...",
-			"cacheId": "jvpb9DxrfxoJ",
-			"formattedUrl": "https://flintarts.org/events/lectures",
-			"htmlFormattedUrl": "https://flintarts.org/events/\u003cb\u003electures\u003c/b\u003e",
-			"pagemap": {
-				"cse_thumbnail": [
-					{
-						"src": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS23tMtAeNhJbOWdGxShYsmnyzFdzOC9Hb7lRykA9Pw72z1IlKTkjTdZw&s",
-						"width": "447",
-						"height": "113"
-					}
-				],
-				"metatags": [
-					{
-						"og:image": "https://flintarts.org/uploads/images/page-headers/_headerImage/nightshot.jpg",
-						"og:type": "website",
-						"viewport": "width=device-width, initial-scale=1",
-						"og:title": "Lectures & Discussions | Flint Institute of Arts",
-						"og:description": "The Flint Institute of Arts is the second largest art museum in Michigan and one of the largest museum art schools in the nation."
-					}
-				],
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