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python3-langchain-nvidia-ai-endpoints-0.3.8-1.lbn36.noarch: NVIDIA NIM Microservices The langchain-nvidia-ai-endpoints package contains LangChain integrations for chat models and embeddings powered by NVIDIA AI Foundation Models, and hosted on NVIDIA API Catalog. NVIDIA AI Foundation models are community and NVIDIA-built models and are NVIDIA-optimized to deliver the best performance on NVIDIA accelerated infrastructure. Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM™ microservices which is part of NVIDIA AI Enterprise. Models can be exported from NVIDIA’s API catalog with NVIDIA NIM, which is included with the NVIDIA AI Enterprise license, and run them on-premises, giving Enterprises ownership of their customizations and full control of their IP and AI application. NIM microservices are packaged as container images on a per model/model family basis

python3-langchain-mongodb-0.6.1-1.lbn36.noarch: langchain-mongodb Installation pip install -U langchain-mongodb Usage See Getting Started with the LangChain Integration for a walkthrough on using your first LangChain implementation with MongoDB Atlas. Using MongoDBAtlasVectorSearch from langchain_mongodb import MongoDBAtlasVectorSearch MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI") DB_NAME = "langchain_db" COLLECTION_NAME = "test" ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name" MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME] vector_search = MongoDBAtlasVectorSearch.from_connection_string( MONGODB_ATLAS_CLUSTER_URI, DB_NAME + "." + COLLECTION_NAME, OpenAIEmbeddings(disallowed_special=()), index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME, ) client = MongoClient(MONGODB_ATLAS_CLUSTER_URI) vector_search_2 =

python3-langchain-mistralai-0.2.10-1.lbn36.noarch: langchain-mistralai This package contains the LangChain integrations for MistralAI through their mistralai SDK. Installation pip install -U langchain-mistralai Chat Models This package contains the ChatMistralAI class, which is the recommended way to interface with MistralAI models. To use, install the requirements, and configure your environment. export MISTRAL_API_KEY=your-api-key Then initialize from langchain_core.messages import HumanMessage from langchain_mistralai.chat_models import ChatMistralAI chat = ChatMistralAI(model="mistral-small") messages = [HumanMessage(content="say a brief hello")] chat.invoke(messages) ChatMistralAI also supports async and streaming functionality: await chat.ainvoke(messages) for chunk in chat.stream(messages): print(chunk.content, end="", flush=True) Embeddings With MistralAIEmbeddings, you can directly use the default model 'mistral-embed', or set a different one if available. Choose model embedding.mode

python3-langchain-milvus-0.1.7-1.lbn36.noarch: langchain-milvus This is a library integration with Milvus and Zilliz Cloud. Installation pip install -U langchain-milvus Milvus vector database See a usage example from langchain_milvus import Milvus Milvus hybrid search See a usage example. from langchain_milvus import MilvusCollectionHybridSearchRetriever Zilliz Cloud vector database from langchain_milvus import Zilliz Zilliz Cloud Pipeline Retriever from langchain_milvus import ZillizCloudPipelineRetriever

python3-langchain-ibm-0.3.8-1.lbn36.noarch: langchain-ibm This package provides the integration between LangChain and IBM watsonx.ai through the ibm-watsonx-ai SDK. Installation To use the langchain-ibm package, follow these installation steps: pip install langchain-ibm Usage Setting up To use IBM's models, you must have an IBM Cloud user API key. Here's how to obtain and set up your API key: Obtain an API Key: For more details on how to create and manage an API key, refer to IBM's documentation. Set the API Key as an Environment Variable: For security reasons, it's recommended to not hard-code your API key directly in your scripts. Instead, set it up as an environment variable. You can use the following code to prompt for the API key and set it as an environment variable: import os from getpass import getpass watsonx_api_key = getpass() os.environ["WATSONX_APIKEY"] = watsonx_api_key In alternative, you can set the environment variable in your terminal. Linux/macOS: Open your terminal and execute the following command: ex

python3-langchain-groq-0.3.2-1.lbn36.noarch: langchain-groq Welcome 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 our website for more resources and join our Discord community to connect with our developers!

python3-langchain-graph-retriever-0.6.1-1.lbn36.noarch: LangChain Graph Retriever LangChain Graph Retriever is a Python library that supports traversing a document graph on top of vector-based similarity search. It works seamlessly with LangChain's retriever framework and supports various graph traversal strategies for efficient document discovery. Features Vector Search: Perform similarity searches using vector embeddings. Graph Traversal: Apply traversal strategies such as breadth-first (Eager) or Maximal Marginal Relevance (MMR) to explore document relationships. Customizable Strategies: Easily extend and configure traversal strategies to meet your specific use case. Multiple Adapters: Support for various vector stores, including AstraDB, Cassandra, Chroma, OpenSearch, and in-memory storage. Synchronous and Asynchronous Retrieval: Supports both sync and async workflows for flexibility in different applications. Installation Install the library via pip: pip install langchain-graph-retriever Getting Started Here is an example of how to

python3-langchain-google-vertexai-2.0.7-1.lbn36.noarch: langchain-google-vertexai This package contains the LangChain integrations for Google Cloud generative models. Installation pip install -U langchain-google-vertexai Chat Models ChatVertexAI class exposes models such as gemini-pro and chat-bison. To use, you should have Google Cloud project with APIs enabled, and configured credentials. Initialize the model as: from langchain_google_vertexai import ChatVertexAI llm = ChatVertexAI(model_name="gemini-pro") llm.invoke("Sing a ballad of LangChain.") You can use other models, e.g. chat-bison: from langchain_google_vertexai import ChatVertexAI llm = ChatVertexAI(model_name="chat-bison", temperature=0.3) llm.invoke("Sing a ballad of LangChain.") Multimodal inputs Gemini vision model supports image inputs when providing a single chat message. Example: from langchain_core.messages import HumanMessage from langchain_google_vertexai import ChatVertexAI llm = ChatVertexAI(model_name="gemini-pro-vision") message = HumanMessage( c

python3-langchain-google-community-2.0.3-1.lbn36.noarch: langchain-google-community This package contains the LangChain integrations for Google products that are not part of langchain-google-vertexai or langchain-google-genai packages. Installation pip install -U langchain-google-community

python3-langchain-google-calendar-tools-0.0.1-1.lbn36.noarch: Langchain Google Calendar Tools This repo walks through connecting to the Google Calendar API. Installation pip install langchain-google-calendar-tools For local development: pip install -e . How to use Create a Google Cloud project and enable Google Calendar API. To get Oauth credentials for the Desktop app, please refer https:/developers.google.com/calendar/api/guides/overview for detail. Download the credentials file to ./notebooks and rename it to credentials.json. If you want to keep its original file name, please replace the value of client_secrets_file in demo.ipynb with the valid path which points to the credentials file. Run this notebook to perform the listed functions Limitations Due to the short development time, some of the following parts have not been completed and will be improved in the future: Timezone: Currently being fixed to Vietnam's timezone, it will be taken from the user's Calendar or the system in the future Update recurring events: has not been implemen

python3-langchain-fireworks-0.3.0-1.lbn36.noarch: LangChain-Fireworks This is the partner package for tying Fireworks.ai and LangChain. Fireworks really strive to provide good support for LangChain use cases, so if you run into any issues please let us know. You can reach out to us in our Discord channel Installation To use the langchain-fireworks package, follow these installation steps: pip install langchain-fireworks Basic usage Setting up Sign in to Fireworks AI to obtain an API Key to access the models, and make sure it is set as the FIREWORKS_API_KEY environment variable. Once you've signed in and obtained an API key, follow these steps to set the FIREWORKS_API_KEY environment variable: Linux/macOS: Open your terminal and execute the following command: export FIREWORKS_API_KEY='your_api_key' Note: To make this environment variable persistent across terminal sessions, add the above line to your ~/.bashrc, ~/.bash_profile, or ~/.zshrc file. Windows: For Command Prompt, use: set FIREWORKS_API_KEY=your_api_key Set up your

python3-langchain-elasticsearch-0.3.2-1.lbn36.noarch: langchain-elasticsearch This package contains the LangChain integration with Elasticsearch. Installation pip install -U langchain-elasticsearch Elasticsearch setup Elastic Cloud You need a running Elasticsearch deployment. The easiest way to start one is through Elastic Cloud. You can sign up for a free trial. Create a deployment Get your Cloud ID: In the Elastic Cloud console, click "Manage" next to your deployment Copy the Cloud ID and paste it into the es_cloud_id parameter below Create an API key: In the Elastic Cloud console, click "Open" next to your deployment In the left-hand side menu, go to "Stack Management", then to "API Keys" Click "Create API key" Enter a name for the API key and click "Create" Copy the API key and paste it into the es_api_key parameter below Alternatively, you can run Elasticsearch via Docker as described in the docs. Usage ElasticsearchStore The ElasticsearchStore class exposes Elasticsearch as a vector store. from langchain_elasticsearch impor

python3-langchain-deepseek-0.1.3-1.lbn36.noarch: langchain-deepseek This package contains the LangChain integration with the DeepSeek API Installation pip install -U langchain-deepseek And you should configure credentials by setting the following environment variables: DEEPSEEK_API_KEY Chat Models ChatDeepSeek class exposes chat models from DeepSeek. from langchain_deepseek import ChatDeepSeek llm = ChatDeepSeek(model="deepseek-chat") llm.invoke("Sing a ballad of LangChain.")

python3-langchain-core-0.3.58-1.lbn36.noarch: 🦜🍎️ LangChain Core Quick Install pip install langchain-core What is it? LangChain Core contains the base abstractions that power the rest of the LangChain ecosystem. These abstractions are designed to be as modular and simple as possible. Examples of these abstractions include those for language models, document loaders, embedding models, vectorstores, retrievers, and more. The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem. For full documentation see the API reference. 1️⃣ Core Interface: Runnables The concept of a Runnable is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them a common invocation interface (invoke, batch, stream, etc.) built-in utilities for retries, fallbacks, schemas and runtime configurability easy deployment with LangServe For more check out the runnable docs.

python3-langchain-community-0.3.23-1.lbn36.noarch: 🦜️🧑‍🤝‍🧑 LangChain Community Quick Install pip install langchain-community What is it? LangChain Community contains third-party integrations that implement the base interfaces defined in LangChain Core, making them ready-to-use in any LangChain application. For full documentation see the API reference. 📕 Releases & Versioning langchain-community is currently on version 0.0.x All changes will be accompanied by a patch version increase. 💁 Contributing As 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 infrastructure, or better documentation. For detailed information on how to contribute, see the Contributing Guide.

python3-langchain-cohere-0.4.4-1.lbn36.noarch: Langchain-Cohere This package contains the LangChain integrations for Cohere. Cohere empowers every developer and enterprise to build amazing products and capture true business value with language AI. Installation Install the langchain-cohere package: pip install langchain-cohere Get a Cohere API key and set it as an environment variable (COHERE_API_KEY) Migration from langchain-community Cohere's integrations used to be part of the langchain-community package, but since version 0.0.30 the integration in langchain-community has been deprecated in favour langchain-cohere. The two steps to migrate are: Import from langchain_cohere instead of langchain_community, for example: from langchain_community.chat_models import ChatCohere -> from langchain_cohere import ChatCohere from langchain_community.retrievers import CohereRagRetriever -> from langchain_cohere import CohereRagRetriever from langchain.embeddings import CohereEmbeddings -> from langchain_cohere import CohereEmbeddings

python3-langchain-aws-0.2.22-1.lbn36.noarch: langchain-aws This package contains the LangChain integrations with AWS. Installation pip install -U langchain-aws All integrations in this package assume that you have the credentials setup to connect with AWS services. Chat Models ChatBedrock class exposes chat models from Bedrock. from langchain_aws import ChatBedrock llm = ChatBedrock() llm.invoke("Sing a ballad of LangChain.") Embeddings BedrockEmbeddings class exposes embeddings from Bedrock. from langchain_aws import BedrockEmbeddings embeddings = BedrockEmbeddings() embeddings.embed_query("What is the meaning of life?") LLMs BedrockLLM class exposes LLMs from Bedrock. from langchain_aws import BedrockLLM llm = BedrockLLM() llm.invoke("The meaning of life is") Retrievers AmazonKendraRetriever class provides a retriever to connect with Amazon Kendra. from langchain_aws import AmazonKendraRetriever retriever = AmazonKendraRetriever( index_id="561be2b6d-9804c7e7-f6a0fbb8-5ccd350" ) retriever.get_relevant_documents(quer

python3-langchain-astradb-0.6.0-1.lbn36.noarch: langchain-astradb This package contains the LangChain integrations for using DataStax Astra DB. DataStax Astra DB is a serverless vector-capable database built on Apache Cassandra® and made conveniently available through an easy-to-use JSON API. Installation and Setup Installation of this partner package: pip install langchain-astradb Integrations overview See the LangChain docs page and the API reference for more details. Vector Store from langchain_astradb import AstraDBVectorStore my_store = AstraDBVectorStore( embedding=my_embedding, collection_name="my_store", api_endpoint="https:/...", token="AstraCS:...", ) Chat message history from langchain_astradb import AstraDBChatMessageHistory message_history = AstraDBChatMessageHistory( session_id="test-session", api_endpoint="https:/...", token="AstraCS:...", ) LLM Cache from langchain_astradb import AstraDBCache cache = AstraDBCache( api_endpoint="https:/...", token="AstraCS:...", ) Semantic LLM Cach

python3-langchain-anthropic-0.3.12-1.lbn36.noarch: langchain-anthropic This package contains the LangChain integration for Anthropic's generative models. Installation pip install -U langchain-anthropic Chat Models Anthropic recommends using their chat models over text completions. You can see their recommended models here. To use, you should have an Anthropic API key configured. Initialize the model as: from langchain_anthropic import ChatAnthropic from langchain_core.messages import AIMessage, HumanMessage model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0, max_tokens=1024) Define the input message message = HumanMessage(content="What is the capital of France?") Generate a response using the model response = model.invoke([message]) For a more detailed walkthrough see here. LLMs (Legacy) You can use the Claude 2 models for text completions. from langchain_anthropic import AnthropicLLM model = AnthropicLLM(model="claude-2.1", temperature=0, max_tokens=1024) response = model.invoke("The best restaurant in San Francisc

python3-langchain+openai-0.3.25-1.lbn36.noarch: This is a metapackage bringing in openai extras requires for python3-langchain. It makes sure the dependencies are installed.

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