Release 0.1.0 (#23)

* Release 0.1.0 Signed-off-by: B-Step62 <[email protected]> * Update README Signed-off-by: B-Step62 <[email protected]> * Update README.md Co-authored-by: Ben Wilson <[email protected]> --------- Signed-off-by: B-Step62 <[email protected]> Co-authored-by: Ben Wilson <[email protected]>
langchain-ai · Sep 26, 2024 · 7ec9326 · 7ec9326
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diff --git a/README.md b/README.md
@@ -7,6 +7,7 @@ This repository provides LangChain components to connect your LangChain applicat
 - **🤖 LLMs**: The `ChatDatabricks` component allows you to access chat endpoints hosted on [Databricks Model Serving](https://www.databricks.com/product/model-serving), including state-of-the-art models such as Llama3, Mixtral, and DBRX, as well as your own fine-tuned models.
 - **📐 Vector Store**: [Databricks Vector Search](https://www.databricks.com/product/machine-learning/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 tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.
 - **🔢 Embeddings**: Provides components for working with embedding models hosted on [Databricks Model Serving](https://www.databricks.com/product/model-serving).
+- **📊 MLflow Integration**: LangChain Databricks components are fully integrated with [MLflow](https://python.langchain.com/docs/integrations/providers/mlflow_tracking/), providing various LLMOps capabilities such as experiment tracking, dependency management, evaluation, and tracing (observability).
 
 **Note**: This repository will replace all Databricks integrations currently present in the `langchain-community` package. Users are encouraged to migrate to this repository as soon as possible.
 

diff --git a/libs/databricks/README.md b/libs/databricks/README.md
@@ -1,24 +1,81 @@
-# langchain-databricks
+# 🦜️🔗 LangChain Databricks
 
-This package contains the LangChain integration with Databricks
+This repository provides LangChain components to connect your LangChain application with various Databricks services.
+
+## Features
+
+- **🤖 LLMs**: The `ChatDatabricks` component allows you to access chat endpoints hosted on [Databricks Model Serving](https://www.databricks.com/product/model-serving), including state-of-the-art models such as Llama3, Mixtral, and DBRX, as well as your own fine-tuned models.
+- **📐 Vector Store**: [Databricks Vector Search](https://www.databricks.com/product/machine-learning/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 tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.
+- **🔢 Embeddings**: Provides components for working with embedding models hosted on [Databricks Model Serving](https://www.databricks.com/product/model-serving).
+- **📊 MLflow Integration**: LangChain Databricks components is fully integrated with [MLflow](https://python.langchain.com/docs/integrations/providers/mlflow_tracking/), providing various LLMOps capabilities such as experiment tracking, dependency management, evaluation, and tracing (observability).
+
+**Note**: This repository will replace all Databricks integrations currently present in the `langchain-community` package. Users are encouraged to migrate to this repository as soon as possible.
 
 ## Installation
 
+You can install the `langchain-databricks` package from PyPI.
+
 ```bash
 pip install -U langchain-databricks
 ```
 
-And you should configure credentials by setting the following environment variables:
+If you are using this package outside Databricks workspace, you should configure credentials by setting the following environment variables:
 
-* TODO: fill this out
+```bash
+export DATABRICKS_HOSTNAME="https://your-databricks-workspace"
+export DATABRICKS_TOKEN="your-personal-access-token"
+```
+
+Instead of personal access token (PAT), you can also use [OAuth M2M authentication](https://docs.databricks.com/en/dev-tools/auth/oauth-m2m.html#language-Environment):
+
+```bash
+export DATABRICKS_HOSTNAME="https://your-databricks-workspace"
+export DATABRICKS_CLIENT_ID="your-service-principle-client-id"
+export DATABRICKS_CLIENT_SECRET="your-service-principle-secret"
+```
 
 ## Chat Models
 
-`ChatDatabricks` class exposes chat models from Databricks.
+`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.
 
 ```python
 from langchain_databricks import ChatDatabricks
 
-llm = ChatDatabricks()
-llm.invoke("Sing a ballad of LangChain.")
-```
+chat_model = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
+chat_model.invoke("Sing a ballad of LangChain.")
+```
+
+See the [usage example](https://python.langchain.com/docs/integrations/chat/databricks/) for more guidance on how to use it within your LangChain application.
+
+**Note**: The LLM class [Databricks](https://python.langchain.com/docs/integrations/llms/databricks/) still lives in the `langchain-community` library. However, this class will be deprecated in the future and it is recommended to use `ChatDatabricks` to get the latest features.
+
+## 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.
+
+
+```python
+from langchain_databricks import DatabricksEmbeddings
+
+embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
+```
+
+See the [usage example](https://python.langchain.com/docs/integrations/text_embedding/databricks) for more guidance on how to use it within your LangChain application.
+
+
+## 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.
+
+```python
+from langchain_databricks.vectorstores import DatabricksVectorSearch
+
+dvs = DatabricksVectorSearch(
+    index_name="<YOUR_INDEX_NAME>",
+    text_column="text",
+    columns=["source"]
+)
+docs = dvs.similarity_search("What is vector search?")
+```
+
+See the [usage example](https://python.langchain.com/docs/integrations/vectorstores/databricks_vector_search) for how to set up vector indices and integrate them with LangChain.
diff --git a/libs/databricks/pyproject.toml b/libs/databricks/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-databricks"
-version = "0.1.0rc2"
+version = "0.1.0"
 description = "An integration package connecting Databricks and LangChain"
 authors = []
 readme = "README.md"