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🦜🔗 Databricks LangChain Integration

The databricks-langchain package provides seamless integration of Databricks AI features into LangChain applications. This repository is now the central hub for all Databricks-related LangChain components, consolidating previous packages such as langchain-databricks and langchain-community.

Installation

From PyPI

pip install databricks-langchain

From Source

pip install git+https://git@github.com/databricks/databricks-ai-bridge.git#subdirectory=integrations/langchain

Key Features

  • LLMs Integration: Use Databricks-hosted large language models (LLMs) like Llama and Mixtral through ChatDatabricks.
  • Vector Search: Store and query vector representations using DatabricksVectorSearch.
  • Embeddings: Generate embeddings with DatabricksEmbeddings.
  • Genie: Use Genie in Langchain.

Getting Started

Use LLMs on Databricks

from databricks_langchain import ChatDatabricks

llm = ChatDatabricks(endpoint="databricks-meta-llama-3-1-70b-instruct")

Use a Genie Space as an Agent (Preview)

Note: Requires Genie API Private Preview. Contact your Databricks account team for enablement.

from databricks_langchain.genie import GenieAgent

genie_agent = GenieAgent(
    "space-id", "Genie",
    description="This Genie space has access to sales data in Europe"
)

Contribution Guide

We welcome contributions! Please see our contribution guidelines for details.

License

This project is licensed under the MIT License.

Thank you for using Databricks LangChain!

Metadata

Release files for databricks-langchain 0.21.0

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Source distribution (sdist)

Source distribution for databricks-langchain 0.21.0
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Table of built distributions (wheels) for databricks-langchain 0.21.0
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databricks_langchain-0.21.0-py3-none-any.whl Python 3 none any Details

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Release files / databricks_langchain-0.21.0.tar.gz

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