Saravanan666/pypi-org huggingface dataset:
Package: langchain-google-cloud-sql-mysql
Cloud SQL for MySQL for LangChainThis package contains theLangChainintegrations for Cloud SQL for MySQL.🧪 Preview:This feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Please note that pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see thelaunch stage descriptionsDocumentationAPI ReferenceGetting StartedIn order to use this library, you first need to go through the following steps:Select or create a Cloud Platform project.Enable billing for your project.Enable the Google Cloud SQL API.Setup Authentication.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.pipinstallvirtualenv
virtualenv<your-env>source<your-env>/bin/activate
<your-env>/bin/pipinstalllangchain-google-cloud-sql-mysqlDocument Loader UsageUse adocument loaderto load data as LangChainDocuments.fromlangchain_google_cloud_sql_mysqlimportMySQLEngine,MySQLLoaderengine=MySQLEngine.from_instance("project-id","region","my-instance","my-database")loader=MySQLLoader(engine,table_name="my-table-name")docs=loader.lazy_load()See the fullDocument Loadertutorial.Chat Message History UsageUseChatMessageHistoryto store messages and provide conversation history to LLMs.fromlangchain_google_cloud_sql_mysqlimportMySQLChatMessageHistory,MySQLEngineengine=MySQLEngine.from_instance("project-id","region","my-instance","my-database")history=MySQLChatMessageHistory(engine,table_name="my-message-store",session_id="my-session_id")See the fullChat Message Historytutorial.ContributingContributions to this library are always welcome and highly encouraged.SeeCONTRIBUTINGfor more information how to get started.Please note that this project is released with a Contributor Code of Conduct. By participating in
this project you agree to abide by its terms. SeeCode of Conductfor more
information.LicenseApache 2.0 - SeeLICENSEfor more information.DisclaimerThis is not an officially supported Google product.
Package: langchain-google-datastore
Datastore for LangChainThis package contains theLangChainintegrations for Datastore.🧪 Preview:This feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Please note that pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see thelaunch stage descriptionsDocumentationAPI ReferenceGetting StartedIn order to use this library, you first need to go through the following steps:Select or create a Cloud Platform project.Enable billing for your project.Enable the Google Cloud Datastore API.Setup Authentication.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.pipinstallvirtualenv
virtualenv<your-env>source<your-env>/bin/activate
<your-env>/bin/pipinstalllangchain-google-datastoreDocument Loader UsageUse a document loader to load data as LangChainDocuments.fromlangchain_google_datastoreimportDatastoreLoaderloader=DatastoreLoader(source="MyKind")docs=loader.lazy_load()See the fullDocument Loadertutorial.Chat Message History UsageUseChatMessageHistoryto store messages and provide conversation history to LLMs.fromlangchain_google_datastoreimportDatastoreChatMessageHistoryhistory=DatastoreChatMessageHistory(session_id="my-session_id",kind="HistoryMessages")See the fullChat Message Historytutorial.ContributingContributions to this library are always welcome and highly encouraged.SeeCONTRIBUTINGfor more information how to get started.Please note that this project is released with a Contributor Code of Conduct. By participating in
this project you agree to abide by its terms. SeeCode of Conductfor more
information.LicenseApache 2.0 - SeeLICENSEfor more information.DisclaimerThis is not an officially supported Google product.
Package: langchain-googledrive
This is a more advanced integration of Google Drive with langchain.Installpip install langchain-googledriveFor debugpoetry install -with test
make testFeatures:Langchain component:Document LoadersRetrieversToolkitsFully compatible with Google Drive APIManage file in trashManage shortcutManage file descriptionPaging with request GDrive list()Multiple kinds of template for request GDriveConvert a lot of mime type (can be configured). The list is adjusted according to the packages availablesCan use only the description of files, without loading and conversion of the bodyLambda fine filterRemove duplicate documents (in case of shortcut)Add Url to documents (or part of documents like specific slide)Use environment variable for reference an API tokensManage different king of strange state with Google File (absence of URL, etc.)Use fully lazy strategy to save memoryConvert GDoc, GSheet and GSlide with different modesExtract text, bullet point, tables, titles, linkslangchain Pull-requestI couldn't get apull-requestaccepted because
the project is too big.
Sorry for that.
Package: langchain-google-firestore
Firestore for LangChainThis package contains theLangChainintegrations for Firestore.🧪 Preview:This feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Please note that pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see thelaunch stage descriptionsDocumentationAPI ReferenceGetting StartedIn order to use this library, you first need to go through the following steps:Select or create a Cloud Platform project.Enable billing for your project.Enable the Google Cloud Firestore API.Setup Authentication.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.pipinstallvirtualenv
virtualenv<your-env>source<your-env>/bin/activate
<your-env>/bin/pipinstalllangchain-google-firestoreDocument Loader UsageUse a document loader to load data as LangChainDocuments.fromlangchain_google_firestoreimportFirestoreLoaderloader=FirestoreLoader("Collection")docs=loader.lazy_load()See the fullDocument Loadertutorial.Chat Message History UsageUseChatMessageHistoryto store messages and provide conversation history to LLMs.fromlangchain_google_firestoreimportFirestoreChatMessageHistoryhistory=FirestoreChatMessageHistory(session_id="my-session-id",collection="HistoryMessages")See the fullChat Message Historytutorial.ContributingContributions to this library are always welcome and highly encouraged.SeeCONTRIBUTINGfor more information how to get started.Please note that this project is released with a Contributor Code of Conduct. By participating in
this project you agree to abide by its terms. SeeCode of Conductfor more
information.LicenseApache 2.0 - SeeLICENSEfor more information.DisclaimerThis is not an officially supported Google product.
Package: langchain-google-genai
langchain-google-genaiThis package contains the LangChain integrations for Gemini through their generative-ai SDK.Installationpipinstall-Ulangchain-google-genaiImage utilitiesTo use image utility methods, like loading images from GCS urls, install with extras group 'images':pipinstall-e"langchain-google-genai[images]"Chat ModelsThis package contains theChatGoogleGenerativeAIclass, which is the recommended way to interface with the Google Gemini series of models.To use, install the requirements, and configure your environment.exportGOOGLE_API_KEY=your-api-keyThen initializefromlangchain_google_genaiimportChatGoogleGenerativeAIllm=ChatGoogleGenerativeAI(model="gemini-pro")llm.invoke("Sing a ballad of LangChain.")Multimodal inputsGemini vision model supports image inputs when providing a single chat message. Example:from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="gemini-pro-vision")
# example
message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": "What's in this image?",
        },  # You can optionally provide text parts
        {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"},
    ]
)
llm.invoke([message])The value ofimage_urlcan be any of the following:A public image URLAn accessible gcs file (e.g., "gcs://path/to/file.png")A local file pathA base64 encoded image (e.g.,data:image/png;base64,abcd124)A PIL imageEmbeddingsThis package also adds support for google's embeddings models.from langchain_google_genai import GoogleGenerativeAIEmbeddings

embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
embeddings.embed_query("hello, world!")
Package: langchain-google-memorystore-redis
Memorystore for Redis for LangChainThis package contains theLangChainintegrations for Memorystore for Redis.🧪 Preview:This feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Please note that pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see thelaunch stage descriptionsDocumentationAPI ReferenceGetting StartedIn order to use this library, you first need to go through the following steps:Select or create a Cloud Platform project.Enable billing for your project.Enable the Google Cloud Memorystore API.Setup Authentication.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.pipinstallvirtualenv
virtualenv<your-env>source<your-env>/bin/activate
<your-env>/bin/pipinstalllangchain-google-memorystore-redisVector Store UsageUse a vector store to store embedded data and perform vector search.fromlangchain_google_memorystore_redisimportRedisVectorStorefromlangchain_community.embeddings.fakeimportFakeEmbeddingsembeddings=FakeEmbeddings(size=128)redis_client=redis.from_url("redis://127.0.0.1:6379")embeddings_service=VertexAIEmbeddings()vectorstore=RedisVectorStore(client=redis_client,index_name="my_vector_index",embeddings=embeddings)See the fullVector Storetutorial.Document Loader UsageUse a document loader to load data as LangChainDocuments.fromlangchain_google_memorystore_redisimportMemorystoreDocumentLoaderloader=MemorystoreDocumentLoader(client=redis_client,key_prefix="docs:",content_fields=set(["page_content"]),)docs=loader.lazy_load()See the fullDocument Loadertutorial.Chat Message History UsageUseChatMessageHistoryto store messages and provide conversation history to LLMs.fromlangchain_google_memorystore_redisimportMemorystoreChatMessageHistoryhistory=MemorystoreChatMessageHistory(client=redis_client,session_id="my-session_id")See the fullChat Message Historytutorial.ContributingContributions to this library are always welcome and highly encouraged.SeeCONTRIBUTINGfor more information how to get started.Please note that this project is released with a Contributor Code of Conduct. By participating in
this project you agree to abide by its terms. SeeCode of Conductfor more
information.LicenseApache 2.0 - SeeLICENSEfor more information.DisclaimerThis is not an officially supported Google product.
Package: langchain-google-spanner
Spanner for LangChainThis package contains theLangChainintegrations for Spanner.🧪 Preview:This feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Please note that pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see thelaunch stage descriptionsDocumentationAPI ReferenceGetting StartedIn order to use this library, you first need to go through the following steps:Select or create a Cloud Platform project.Enable billing for your project.Enable the Google Cloud Spanner API.Setup Authentication.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.pipinstallvirtualenv
virtualenv<your-env>source<your-env>/bin/activate
<your-env>/bin/pipinstalllangchain-google-spannerVector Store UsageUse a vector store to store embedded data and perform vector search.fromlangchain_google_sapnnerimportSpannerVectorstorefromlangchain.embeddingsimportVertexAIEmbeddingsembeddings_service=VertexAIEmbeddings()vectorstore=SpannerVectorStore(instance_id="my-instance",database_id="my-database",table_name="my-table",embeddings=embedding_service)See the fullVector Storetutorial.Document Loader UsageUse a document loader to load data as LangChainDocuments.fromlangchain_google_spannerimportSpannerLoaderloader=SpannerLoader(instance_id="my-instance",database_id="my-database",query="SELECT * from my_table_name")docs=loader.lazy_load()See the fullDocument Loadertutorial.Chat Message History UsageUseChatMessageHistoryto store messages and provide conversation history to LLMs.fromlangchain_google_spannerimportSpannerChatMessageHistoryhistory=SpannerChatMessageHistory(instance_id="my-instance",database_id="my-database",table_name="my_table_name",session_id="my-session_id")See the fullChat Message Historytutorial.ContributingContributions to this library are always welcome and highly encouraged.SeeCONTRIBUTINGfor more information how to get started.Please note that this project is released with a Contributor Code of Conduct. By participating in
this project you agree to abide by its terms. SeeCode of Conductfor more
information.LicenseApache 2.0 - SeeLICENSEfor more information.DisclaimerThis is not an officially supported Google product.
Package: langchain-google-vertexai
langchain-google-vertexaiThis package contains the LangChain integrations for Google Cloud generative models.Installationpipinstall-Ulangchain-google-vertexaiChat ModelsChatVertexAIclass exposes models such asgemini-proandchat-bison.To use, you should have Google Cloud project with APIs enabled, and configured credentials. Initialize the model as:fromlangchain_google_vertexaiimportChatVertexAIllm=ChatVertexAI(model_name="gemini-pro")llm.invoke("Sing a ballad of LangChain.")You can use other models, e.g.chat-bison:fromlangchain_google_vertexaiimportChatVertexAIllm=ChatVertexAI(model_name="chat-bison",temperature=0.3)llm.invoke("Sing a ballad of LangChain.")Multimodal inputsGemini vision model supports image inputs when providing a single chat message. Example:fromlangchain_core.messagesimportHumanMessagefromlangchain_google_vertexaiimportChatVertexAIllm=ChatVertexAI(model_name="gemini-pro-vision")# examplemessage=HumanMessage(content=[{"type":"text","text":"What's in this image?",},# You can optionally provide text parts{"type":"image_url","image_url":{"url":"https://picsum.photos/seed/picsum/200/300"}},])llm.invoke([message])The value ofimage_urlcan be any of the following:A public image URLAn accessible gcs file (e.g., "gcs://path/to/file.png")A local file pathA base64 encoded image (e.g.,data:image/png;base64,abcd124)EmbeddingsYou can use Google Cloud's embeddings models as:fromlangchain_google_vertexaiimportVertexAIEmbeddingsembeddings=VertexAIEmbeddings()embeddings.embed_query("hello, world!")LLMsYou can use Google Cloud's generative AI models as Langchain LLMs:fromlangchain.promptsimportPromptTemplatefromlangchain_google_vertexaiimportVertexAItemplate="""Question:{question}Answer: Let's think step by step."""prompt=PromptTemplate.from_template(template)chain=prompt|llmquestion="Who was the president in the year Justin Beiber was born?"print(chain.invoke({"question":question}))You can use Gemini and Palm models, including code-generations ones:fromlangchain_google_vertexaiimportVertexAIllm=VertexAI(model_name="code-bison",max_output_tokens=1000,temperature=0.3)question="Write a python function that checks if a string is a valid email address"output=llm(question)
Package: langchain-groq
langchain-groqWelcome to Groq! 🚀At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload.Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can:Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥Know the exact performance and compute time for any given workload 🔮Take advantage of our cutting-edge technology to stay ahead of the competition 💪Want more Groq? Check out ourwebsitefor more resources and join ourDiscord communityto connect with our developers!Installation and SetupInstall the integration package:pipinstalllangchain-groqRequest anAPI keyand set it as an environment variableexportGROQ_API_KEY=gsk_...Chat ModelSee ausage example.DevelopmentTo develop thelangchain-groqpackage, you'll need to follow these instructions:Install dev dependenciespoetryinstall--withtest,test_integration,lint,codespellBuild the packagepoetrybuildRun unit testsUnit tests live intests/unit_testsand SHOULD NOT require an internet connection or a valid API KEY.  Run unit tests withmaketestsRun integration testsIntegration tests live intests/integration_testsand require a connection to the Groq API and a valid API KEY.makeintegration_testsLint & FormatRun additional tests and linters to ensure your code is up to standard.makelintspell_checkcheck_imports
Package: langchain-ibis
🦜️🔗 LangChain⚡ Building applications with LLMs through composability ⚡Production Support:As you move your LangChains into production, we'd love to offer more comprehensive support.
Please fill outthis formand we'll set up a dedicated support Slack channel.Quick Installpip install langchain🤔 What is this?Large language models (LLMs) are emerging as a transformative technology, enabling
developers to build applications that they previously could not.
But using these LLMs in isolation is often not enough to
create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:❓ Question Answering over specific documentsDocumentationEnd-to-end Example:Question Answering over Notion Database💬 ChatbotsDocumentationEnd-to-end Example:Chat-LangChain🤖 AgentsDocumentationEnd-to-end Example:GPT+WolframAlpha📖 DocumentationPlease seeherefor full documentation on:Getting started (installation, setting up the environment, simple examples)How-To examples (demos, integrations, helper functions)Reference (full API docs)Resources (high-level explanation of core concepts)🚀 What can this help with?There are six main areas that LangChain is designed to help with.
These are, in increasing order of complexity:📃 LLMs and Prompts:This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.🔗 Chains:Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.📚 Data Augmented Generation:Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.🤖 Agents:Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.🧠 Memory:Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.🧐 Evaluation:[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.For more information on these concepts, please see ourfull documentation.💁 ContributingAs an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.For detailed information on how to contribute, seehere.
Package: langchain-interpreter
Langchain InterpreterThis project aims to allow for langchain pipelines to be created from a config file.Documentation:https://langchain-interpreter.readthedocs.io/To publishUpdate version number inpyproject.tomlpython -m buildtwine upload --skip-existing dist/*DocumentationThis command generates docs for each module? (run in project dir)sphinx-apidoc -f -o docs src/langchain_interpreter/This command builds the docs:cd docs
make htmlThis command cleans the html out:cd docs
make cleanFor examplesInstall the project:pip install -e .
Package: langchain-kinetica
langchain-kineticaKinetica intefrace for Langchain. See theLLM documentationfor an overview of the Kinetica LLM.1. Prerequisites2. Package Contents3. Installation4. Usage5. Building6. See Also1. PrerequisitesTo use langchain with Kinetica you will need:Python runtime >3.10Kinetica SqlAssist LLMKinetica instance >7.2.0 configured to use SqlAssist.2. Package ContentsKineticaChatLLM: ChatModel for converting natural language to SQL.KineticaSqlOutputParser: OutputParser that will execute SQL from theKineticaChatLLM.SqlResponse: If the Kinetica chain ends withKineticaSqlOutputParserthen this response will contain the generated SQL and results from its execution.3. InstallationThis project is not yet available on pypi. You can install it directly from the repository.$pipinstall"langchain-kinetica @ git+ssh://git@github.com/kineticadb/langchain-kinetica.git"4. UsageSee theKinetica LLM Demo notebookfor examples.5. BuildingInstall the project locally.$pipinstall--editable.You will need to install the build utility.$pipinstall--upgradebuildBuild the project$python3-mbuildThe build will generate a.whlfile that can be distributed.$ls-1./dist
langchain-kinetica-1.0.tar.gz
langchain_kinetica-1.0-py3-none-any.whl6. See AlsoKinetica LLM DocumentationLangChain PromptsLancChain Chat Models