Upload folder using huggingface_hub

.env

@@ -0,0 +1,6 @@
+PERSIST_DIRECTORY=db
+MODEL_TYPE=GPT4All
+MODEL_PATH=models/ggml-gpt4all-j-v1.3-groovy.bin
+EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2
+MODEL_N_CTX=1000
+TARGET_SOURCE_CHUNKS=4

.gitattributes

@@ -32,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ggml-gpt4all-j-v1.3-groovy.bin.1 filter=lfs diff=lfs merge=lfs -text
+source_documents/Nahjul-Balagha.pdf filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,24 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: bug
+assignees: ''
+
+---
+
+Note: if you'd like to *ask a question* or *open a discussion*, head over to the [Discussions](https://github.com/imartinez/privateGPT/discussions) section and post it there.
+
+**Describe the bug and how to reproduce it**
+A clear and concise description of what the bug is and the steps to reproduce the behavior.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Environment (please complete the following information):**
+ - OS / hardware: [e.g. macOS 12.6 / M1]
+ - Python version [e.g. 3.11.3]
+- Other relevant information
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,22 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: enhancement
+assignees: ''
+
+---
+
+Note: if you'd like to *ask a question* or *open a discussion*, head over to the [Discussions](https://github.com/imartinez/privateGPT/discussions) section and post it there.
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -0,0 +1,174 @@
+# OSX
+.DS_STORE
+
+# Models
+models/
+
+# Source Documents
+source_documents/*
+!source_documents/state_of_the_union.txt
+
+# Local Chroma db
+.chroma/
+db/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+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/

.pre-commit-config.yaml

@@ -0,0 +1,44 @@
+---
+files: ^(.*\.(py|json|md|sh|yaml|cfg|txt))$
+exclude: ^(\.[^/]*cache/.*|.*/_user.py|source_documents/)$
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      #- id: no-commit-to-branch
+      #  args: [--branch, main]
+      - id: check-yaml
+        args: [--unsafe]
+      # - id: debug-statements
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+        exclude-files: \.md$
+      - id: check-json
+      - id: mixed-line-ending
+      # - id: check-builtin-literals
+      # - id: check-ast
+      - id: check-merge-conflict
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: check-docstring-first
+      - id: fix-byte-order-marker
+      - id: check-case-conflict
+      # - id: check-toml
+  - repo: https://github.com/adrienverge/yamllint.git
+    rev: v1.29.0
+    hooks:
+      - id: yamllint
+        args:
+          - --no-warnings
+          - -d
+          - '{extends: relaxed, rules: {line-length: {max: 90}}}'
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.2
+    hooks:
+      - id: codespell
+        args:
+          # - --builtin=clear,rare,informal,usage,code,names,en-GB_to_en-US
+          - --builtin=clear,rare,informal,usage,code,names
+          - --ignore-words-list=hass,master
+          - --skip="./.*"
+          - --quiet-level=2

LICENSE

@@ -0,0 +1,201 @@
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+
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README.md

@@ -1,12 +1,140 @@
 ---
 title: NehjulBalaghaGPT
-emoji: 🔥
-colorFrom: green
-colorTo: yellow
+app_file: privateGPT.py
 sdk: gradio
 sdk_version: 3.33.1
-app_file: app.py
-pinned: false
 ---
+# privateGPT
+Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection!
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Built with [LangChain](https://github.com/hwchase17/langchain), [GPT4All](https://github.com/nomic-ai/gpt4all), [LlamaCpp](https://github.com/ggerganov/llama.cpp), [Chroma](https://www.trychroma.com/) and [SentenceTransformers](https://www.sbert.net/).
+
+<img width="902" alt="demo" src="https://user-images.githubusercontent.com/721666/236942256-985801c9-25b9-48ef-80be-3acbb4575164.png">
+
+# Environment Setup
+In order to set your environment up to run the code here, first install all requirements:
+
+```shell
+pip3 install -r requirements.txt
+```
+
+Then, download the LLM model and place it in a directory of your choice:
+- LLM: default to [ggml-gpt4all-j-v1.3-groovy.bin](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin). If you prefer a different GPT4All-J compatible model, just download it and reference it in your `.env` file.
+
+Rename `example.env` to `.env` and edit the variables appropriately.
+```
+MODEL_TYPE: supports LlamaCpp or GPT4All
+PERSIST_DIRECTORY: is the folder you want your vectorstore in
+MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM
+MODEL_N_CTX: Maximum token limit for the LLM model
+EMBEDDINGS_MODEL_NAME: SentenceTransformers embeddings model name (see https://www.sbert.net/docs/pretrained_models.html)
+TARGET_SOURCE_CHUNKS: The amount of chunks (sources) that will be used to answer a question
+```
+
+Note: because of the way `langchain` loads the `SentenceTransformers` embeddings, the first time you run the script it will require internet connection to download the embeddings model itself.
+
+## Test dataset
+This repo uses a [state of the union transcript](https://github.com/imartinez/privateGPT/blob/main/source_documents/state_of_the_union.txt) as an example.
+
+## Instructions for ingesting your own dataset
+
+Put any and all your files into the `source_documents` directory
+
+The supported extensions are:
+
+   - `.csv`: CSV,
+   - `.docx`: Word Document,
+   - `.doc`: Word Document,
+   - `.enex`: EverNote,
+   - `.eml`: Email,
+   - `.epub`: EPub,
+   - `.html`: HTML File,
+   - `.md`: Markdown,
+   - `.msg`: Outlook Message,
+   - `.odt`: Open Document Text,
+   - `.pdf`: Portable Document Format (PDF),
+   - `.pptx` : PowerPoint Document,
+   - `.ppt` : PowerPoint Document,
+   - `.txt`: Text file (UTF-8),
+
+Run the following command to ingest all the data.
+
+```shell
+python ingest.py
+```
+
+Output should look like this:
+
+```shell
+Creating new vectorstore
+Loading documents from source_documents
+Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00,  1.73s/it]
+Loaded 1 new documents from source_documents
+Split into 90 chunks of text (max. 500 tokens each)
+Creating embeddings. May take some minutes...
+Using embedded DuckDB with persistence: data will be stored in: db
+Ingestion complete! You can now run privateGPT.py to query your documents
+```
+
+It will create a `db` folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document.
+You can ingest as many documents as you want, and all will be accumulated in the local embeddings database.
+If you want to start from an empty database, delete the `db` folder.
+
+Note: during the ingest process no data leaves your local environment. You could ingest without an internet connection, except for the first time you run the ingest script, when the embeddings model is downloaded.
+
+## Ask questions to your documents, locally!
+In order to ask a question, run a command like:
+
+```shell
+python privateGPT.py
+```
+
+And wait for the script to require your input.
+
+```plaintext
+> Enter a query:
+```
+
+Hit enter. You'll need to wait 20-30 seconds (depending on your machine) while the LLM model consumes the prompt and prepares the answer. Once done, it will print the answer and the 4 sources it used as context from your documents; you can then ask another question without re-running the script, just wait for the prompt again.
+
+Note: you could turn off your internet connection, and the script inference would still work. No data gets out of your local environment.
+
+Type `exit` to finish the script.
+
+
+### CLI
+The script also supports optional command-line arguments to modify its behavior. You can see a full list of these arguments by running the command ```python privateGPT.py --help``` in your terminal.
+
+
+# How does it work?
+Selecting the right local models and the power of `LangChain` you can run the entire pipeline locally, without any data leaving your environment, and with reasonable performance.
+
+- `ingest.py` uses `LangChain` tools to parse the document and create embeddings locally using `HuggingFaceEmbeddings` (`SentenceTransformers`). It then stores the result in a local vector database using `Chroma` vector store.
+- `privateGPT.py` uses a local LLM based on `GPT4All-J` or `LlamaCpp` to understand questions and create answers. The context for the answers is extracted from the local vector store using a similarity search to locate the right piece of context from the docs.
+- `GPT4All-J` wrapper was introduced in LangChain 0.0.162.
+
+# System Requirements
+
+## Python Version
+To use this software, you must have Python 3.10 or later installed. Earlier versions of Python will not compile.
+
+## C++ Compiler
+If you encounter an error while building a wheel during the `pip install` process, you may need to install a C++ compiler on your computer.
+
+### For Windows 10/11
+To install a C++ compiler on Windows 10/11, follow these steps:
+
+1. Install Visual Studio 2022.
+2. Make sure the following components are selected:
+   * Universal Windows Platform development
+   * C++ CMake tools for Windows
+3. Download the MinGW installer from the [MinGW website](https://sourceforge.net/projects/mingw/).
+4. Run the installer and select the `gcc` component.
+
+## Mac Running Intel
+When running a Mac with Intel hardware (not M1), you may run into _clang: error: the clang compiler does not support '-march=native'_ during pip install.
+
+If so set your archflags during pip install. eg: _ARCHFLAGS="-arch x86_64" pip3 install -r requirements.txt_
+
+# Disclaimer
+This is a test project to validate the feasibility of a fully private solution for question answering using LLMs and Vector embeddings. It is not production ready, and it is not meant to be used in production. The models selection is not optimized for performance, but for privacy; but it is possible to use different models and vectorstores to improve performance.

constants.py

@@ -0,0 +1,15 @@
+import os
+from dotenv import load_dotenv
+from chromadb.config import Settings
+
+load_dotenv()
+
+# Define the folder for storing database
+PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY')
+
+# Define the Chroma settings
+CHROMA_SETTINGS = Settings(
+        chroma_db_impl='duckdb+parquet',
+        persist_directory=PERSIST_DIRECTORY,
+        anonymized_telemetry=False
+)

db/chroma-collections.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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ggml-gpt4all-j-v1.3-groovy.bin

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ingest.py

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+#!/usr/bin/env python3
+import os
+import glob
+from typing import List
+from dotenv import load_dotenv
+from multiprocessing import Pool
+from tqdm import tqdm
+from utils import *
+
+from langchain.document_loaders import (
+    CSVLoader,
+    EverNoteLoader,
+    PDFMinerLoader,
+    TextLoader,
+    UnstructuredEmailLoader,
+    UnstructuredEPubLoader,
+    UnstructuredHTMLLoader,
+    UnstructuredMarkdownLoader,
+    UnstructuredODTLoader,
+    UnstructuredPowerPointLoader,
+    UnstructuredWordDocumentLoader,
+)
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.docstore.document import Document
+from constants import CHROMA_SETTINGS
+
+
+load_dotenv()
+
+
+# Load environment variables
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
+embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
+chunk_size = 500
+chunk_overlap = 50
+
+
+# Custom document loaders
+class MyElmLoader(UnstructuredEmailLoader):
+    """Wrapper to fallback to text/plain when default does not work"""
+
+    def load(self) -> List[Document]:
+        """Wrapper adding fallback for elm without html"""
+        try:
+            try:
+                doc = UnstructuredEmailLoader.load(self)
+            except ValueError as e:
+                if 'text/html content not found in email' in str(e):
+                    # Try plain text
+                    self.unstructured_kwargs["content_source"]="text/plain"
+                    doc = UnstructuredEmailLoader.load(self)
+                else:
+                    raise
+        except Exception as e:
+            # Add file_path to exception message
+            raise type(e)(f"{self.file_path}: {e}") from e
+
+        return doc
+
+
+# Map file extensions to document loaders and their arguments
+LOADER_MAPPING = {
+    ".csv": (CSVLoader, {}),
+    # ".docx": (Docx2txtLoader, {}),
+    ".doc": (UnstructuredWordDocumentLoader, {}),
+    ".docx": (UnstructuredWordDocumentLoader, {}),
+    ".enex": (EverNoteLoader, {}),
+    ".eml": (MyElmLoader, {}),
+    ".epub": (UnstructuredEPubLoader, {}),
+    ".html": (UnstructuredHTMLLoader, {}),
+    ".md": (UnstructuredMarkdownLoader, {}),
+    ".odt": (UnstructuredODTLoader, {}),
+    ".pdf": (PDFMinerLoader, {}),
+    ".ppt": (UnstructuredPowerPointLoader, {}),
+    ".pptx": (UnstructuredPowerPointLoader, {}),
+    ".txt": (TextLoader, {"encoding": "utf8"}),
+    # Add more mappings for other file extensions and loaders as needed
+}
+
+
+def load_single_document(file_path: str) -> List[Document]:
+    ext = "." + file_path.rsplit(".", 1)[-1]
+    if ext in LOADER_MAPPING:
+        loader_class, loader_args = LOADER_MAPPING[ext]
+        loader = loader_class(file_path, **loader_args)
+        return loader.load()
+
+    raise ValueError(f"Unsupported file extension '{ext}'")
+
+def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
+    """
+    Loads all documents from the source documents directory, ignoring specified files
+    """
+    all_files = []
+    for ext in LOADER_MAPPING:
+        all_files.extend(
+            glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
+        )
+    filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
+
+    with Pool(processes=os.cpu_count()) as pool:
+        results = []
+        with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
+            for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
+                results.extend(docs)
+                pbar.update()
+
+    return results
+
+def process_documents(ignored_files: List[str] = []) -> List[Document]:
+    """
+    Load documents and split in chunks
+    """
+    print(f"Loading documents from {source_directory}")
+    documents = load_documents(source_directory, ignored_files)
+    if not documents:
+        print("No new documents to load")
+        exit(0)
+    print(f"Loaded {len(documents)} new documents from {source_directory}")
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    texts = text_splitter.split_documents(documents)
+    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
+    return texts
+
+def main():
+    # Create embeddings
+    ensure_integrity(persist_directory, True)
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    collection = db.get()
+    texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
+    print(f"Creating embeddings. May take some minutes...")
+    db.add_documents(texts)
+    db.persist()
+    db = None
+
+    print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
+
+
+if __name__ == "__main__":
+    main()

models/ggml-gpt4all-j-v1.3-groovy.bin

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privateGPT.py

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+#!/usr/bin/env python3
+from dotenv import load_dotenv
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.vectorstores import Chroma
+from langchain.llms import GPT4All, LlamaCpp
+import os
+import argparse
+from utils import *
+import gradio as gr
+
+load_dotenv()
+
+embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+
+model_type = os.environ.get('MODEL_TYPE')
+model_path = os.environ.get('MODEL_PATH')
+model_n_ctx = os.environ.get('MODEL_N_CTX')
+target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
+
+from constants import CHROMA_SETTINGS
+
+def main():
+
+  demo = gr.Interface(
+    fn=GPT,
+    inputs=gr.Textbox(label = "Ask your questions", lines=2, placeholder="Question?"),
+    outputs=gr.Textbox(lines=20, placeholder=""),
+  )
+  demo.launch(share=True)
+  
+
+def GPT(name):
+    ensure_integrity(persist_directory, False)
+    # Parse the command line arguments
+    args = parse_arguments()
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
+    # activate/deactivate the streaming StdOut callback for LLMs
+    callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
+    # Prepare the LLM
+    match model_type:
+        case "LlamaCpp":
+            llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False)
+        case "GPT4All":
+            llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=False)
+        case _default:
+            print(f"Model {model_type} not supported!")
+            exit;
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
+    # Interactive questions and answers
+    while True:
+        query = name
+        print(query)
+        # query = input("\nEnter a query: ")
+        if query == "exit":
+            break
+
+        # Get the answer from the chain
+        res = qa(query)
+        answer, docs = res['result'], [] if args.hide_source else res['source_documents']
+
+        # Print the result
+        print("\n\n> Question:")
+        print(query)
+        print("\n> Answer:")
+        print(answer)
+
+        # Print the relevant sources used for the answer
+        for document in docs:
+            print("\n> " + document.metadata["source"] + ":")
+            print(document.page_content)
+        
+        return answer
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
+                                                 'using the power of LLMs.')
+    parser.add_argument("--hide-source", "-S", action='store_true',
+                        help='Use this flag to disable printing of source documents used for answers.')
+
+    parser.add_argument("--mute-stream", "-M",
+                        action='store_true',
+                        help='Use this flag to disable the streaming StdOut callback for LLMs.')
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+langchain==0.0.177
+gpt4all==0.2.3
+chromadb==0.3.23
+llama-cpp-python==0.1.50
+urllib3==2.0.2
+pdfminer.six==20221105
+python-dotenv==1.0.0
+unstructured==0.6.6
+extract-msg==0.41.1
+tabulate==0.9.0
+pandoc==2.3
+pypandoc==1.11
+tqdm==4.65.0

source_documents/Nahjul-Balagha.pdf

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+size 3167124

utils.py

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+import os
+import glob
+
+def ensure_integrity(persist_directory: str, is_caller_ingest: bool) -> None:
+    """
+    Checks if vectorstore exists, and if it does, if it is valid
+    """
+    if not os.path.exists(os.path.join(persist_directory, 'index')) and not is_caller_ingest:
+        print("No vectorstore found. Please run ingest.py first.")
+        exit(1)
+    list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
+    list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
+    if os.path.exists(os.path.join(persist_directory, 'index')) and (not os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) or not os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')) or not len(list_index_files) > 3):
+        print(f"Current vectorstore is not valid. Aborting.")
+        print(f"If you deleted any files in the '{persist_directory}' folder, please restore them (if possible) and run privateGPT.py again.")
+        print("If you want to start from scratch, delete the '{persist_directory}' folder and its contents and run ingest.py again.")
+        exit(1)