databricks-langchain

Name	databricks-langchain JSON
Version	0.2.0 JSON
	download
home_page	None
Summary	Support for Databricks AI support in LangChain
upload_time	2025-01-22 01:00:38
maintainer	None
docs_url	None
author	None
requires_python	>=3.9
license	Apache-2.0
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # 🦜🔗 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
```sh
pip install databricks-langchain
```

### From Source
```sh
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](https://www.databricks.com/product/ai-bi/genie) in Langchain.

## Getting Started

### Use LLMs on Databricks
```python
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.

```python
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](https://github.com/databricks/databricks-ai-bridge/tree/main/integrations/langchain) for details.

## License
This project is licensed under the [MIT License](LICENSE).

Thank you for using Databricks LangChain!


            

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    "description": "# \ud83e\udd9c\ud83d\udd17 Databricks LangChain Integration\n\nThe `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`.\n\n## Installation\n\n### From PyPI\n```sh\npip install databricks-langchain\n```\n\n### From Source\n```sh\npip install git+https://git@github.com/databricks/databricks-ai-bridge.git#subdirectory=integrations/langchain\n```\n\n## Key Features\n\n- **LLMs Integration:** Use Databricks-hosted large language models (LLMs) like Llama and Mixtral through `ChatDatabricks`.\n- **Vector Search:** Store and query vector representations using `DatabricksVectorSearch`.\n- **Embeddings:** Generate embeddings with `DatabricksEmbeddings`.\n- **Genie:** Use [Genie](https://www.databricks.com/product/ai-bi/genie) in Langchain.\n\n## Getting Started\n\n### Use LLMs on Databricks\n```python\nfrom databricks_langchain import ChatDatabricks\n\nllm = ChatDatabricks(endpoint=\"databricks-meta-llama-3-1-70b-instruct\")\n```\n\n### Use a Genie Space as an Agent (Preview)\n> **Note:** Requires Genie API Private Preview. Contact your Databricks account team for enablement.\n\n```python\nfrom databricks_langchain.genie import GenieAgent\n\ngenie_agent = GenieAgent(\n    \"space-id\", \"Genie\",\n    description=\"This Genie space has access to sales data in Europe\"\n)\n```\n\n---\n\n## Contribution Guide\nWe welcome contributions! Please see our [contribution guidelines](https://github.com/databricks/databricks-ai-bridge/tree/main/integrations/langchain) for details.\n\n## License\nThis project is licensed under the [MIT License](LICENSE).\n\nThank you for using Databricks LangChain!\n\n",
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