diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md index 54a10c7c6a62c..a45e8e3e03eb2 100644 --- a/docs/docs/integrations/providers/databricks.md +++ b/docs/docs/integrations/providers/databricks.md @@ -14,23 +14,13 @@ Databricks embraces the LangChain ecosystem in various ways: Installation ------------ -First-party Databricks integrations are available in the langchain-databricks partner package. +First-party Databricks integrations are now available in the databricks-langchain partner package. ``` -pip install langchain-databricks +pip install databricks-langchain ``` -🚧 Upcoming Package Consolidation Notice - -This package (`langchain-databricks`) will soon be consolidated into a new package: `databricks-langchain`. The new package will serve as the primary hub for all Databricks Langchain integrations. - -What’s Changing? -In the coming months, `databricks-langchain` will include all features currently in `langchain-databricks`, as well as additional integrations to provide a unified experience for Databricks users. - -What You Need to Know -For now, continue to use `langchain-databricks` as usual. When `databricks-langchain` is ready, we’ll provide clear migration instructions to make the transition seamless. During the transition period, `langchain-databricks` will remain operational, and updates will be shared here with timelines and guidance. - -Thank you for your support as we work toward an improved, streamlined experience! +The legacy langchain-databricks partner package is still available but will be soon deprecated. Chat Model ---------- @@ -38,7 +28,7 @@ Chat Model `ChatDatabricks` is a Chat Model class to access chat endpoints hosted on Databricks, including state-of-the-art models such as Llama3, Mixtral, and DBRX, as well as your own fine-tuned models. ``` -from langchain_databricks import ChatDatabricks +from databricks_langchain import ChatDatabricks chat_model = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct") ``` @@ -69,7 +59,7 @@ Embeddings `DatabricksEmbeddings` is an Embeddings class to access text-embedding endpoints hosted on Databricks, including state-of-the-art models such as BGE, as well as your own fine-tuned models. ``` -from langchain_databricks import DatabricksEmbeddings +from databricks_langchain import DatabricksEmbeddings embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") ``` @@ -83,7 +73,7 @@ Vector Search Databricks Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from [Delta](https://docs.databricks.com/en/introduction/delta-comparison.html) tables managed by [Unity Catalog](https://www.databricks.com/product/unity-catalog) and query them with a simple API to return the most similar vectors. ``` -from langchain_databricks.vectorstores import DatabricksVectorSearch +from databricks_langchain import DatabricksVectorSearch dvs = DatabricksVectorSearch( endpoint="",