| Name | databricks-langchain JSON |
| Version |
0.7.0
JSON |
| download |
| home_page | None |
| Summary | Support for Databricks AI support in LangChain |
| upload_time | 2025-08-13 18:25:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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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
```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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