Registry / llm-agents / langchain-databricks

langchain-databricks

library0.1.2pypypi✓ verified 84d ago

The `langchain-databricks` package was an integration library connecting LangChain with Databricks AI features, including LLMs, vector search, and MLflow. It has been superseded by `databricks-langchain` to consolidate Databricks-related LangChain components. The last published version of `langchain-databricks` is 0.1.2, and it is no longer actively maintained or receiving new features. Users should migrate to the `databricks-langchain` package for continued support and new functionality.

llm-agents ai-ml aws

pip install langchain-databricks

github ↗package ↗changelog ↗

INSTALL

IMPORT

SIG · LANGCHAIN-DATABRIC

langchain-databricks

llm-agentspythonv0.1.2

Install

46.5s avg

Import

14598ms

Disk

832MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 51.19s

py 3.11

✕ build_error

✓ 45.81s

py 3.12

✕ build_error

✓ 41.13s

py 3.13

✕ build_error

4/8 runs

py 3.9

✕ build_error

✓ 47.75s

832MB installed

● package 832MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ChatDatabricks

✓ from langchain_databricks import ChatDatabricks

✗ from databricks_langchain import ChatDatabricks

DatabricksEmbeddings

✓ from langchain_databricks import DatabricksEmbeddings

DatabricksVectorSearch

✓ from langchain_databricks import DatabricksVectorSearch

This quickstart demonstrates how to instantiate and use `ChatDatabricks` from the *recommended* `databricks-langchain` package to interact with a Databricks Model Serving endpoint. If running outside a Databricks Workspace, ensure `DATABRICKS_HOST` and `DATABRICKS_TOKEN` environment variables are set for authentication. The code for the deprecated `langchain-databricks` is shown for historical context but should not be used in new projects.

import os from databricks_langchain import ChatDatabricks from langchain_core.messages import HumanMessage # --- IMPORTANT: FOR RUNNING OUTSIDE DATABRICKS WORKSPACE --- # Set these environment variables or pass them directly to the ChatDatabricks constructor. # os.environ["DATABRICKS_HOST"] = os.environ.get("DATABRICKS_HOST", "https://your-databricks-workspace.cloud.databricks.com") # os.environ["DATABRICKS_TOKEN"] = os.environ.get("DATABRICKS_TOKEN", "dapi********************************") # Instantiate the chat model using a Databricks Model Serving endpoint # Replace 'databricks-meta-llama-3-70b-instruct' with your actual endpoint name. # Ensure DATABRICKS_HOST and DATABRICKS_TOKEN are set if running outside Databricks workspace. chat_model = ChatDatabricks( endpoint="databricks-meta-llama-3-70b-instruct", temperature=0.1, max_tokens=256 ) # Invoke the model response = chat_model.invoke([HumanMessage(content="What is MLflow?")]) print(response.content) # --- DEPRECATED PACKAGE USAGE (for context, prefer databricks-langchain) --- # from langchain_databricks import ChatDatabricks # deprecated_chat_model = ChatDatabricks(endpoint="your-deprecated-endpoint") # print("WARNING: Using deprecated langchain-databricks package.")

Debug

Known issues

breakingThe `langchain-databricks` package is deprecated. All features have been consolidated into the new `databricks-langchain` package. Future updates and new features will be released exclusively in `databricks-langchain`.

fix

Migrate your `pip install` command to `pip install databricks-langchain` and update all imports from `langchain_databricks` to `databricks_langchain`.

affects: <0.1.2

gotchaWhen running outside a Databricks workspace, you must explicitly set Databricks authentication credentials (hostname and personal access token).

fix

Set `DATABRICKS_HOST` and `DATABRICKS_TOKEN` environment variables or pass `databricks_host` and `databricks_token` directly during class instantiation (e.g., `ChatDatabricks(endpoint=..., databricks_host=..., databricks_token=...)`).

affects: All

gotchaModel serving endpoint name mismatches or incorrect API key/configuration are common causes for invocation failures.

fix

Double-check that the `endpoint` name passed to `ChatDatabricks` or `DatabricksEmbeddings` exactly matches the configured endpoint name in your Databricks workspace. Verify associated API keys and resource configurations.

affects: All

gotchaWhen logging LangChain models with MLflow, ensure the model name adheres to the three-level Unity Catalog path (`<catalog>.<schema>.<model>`) and that `chain_type` is passed in `model_config` for proper reconstruction.

fix

Use `mlflow.langchain.log_model(lc_model=..., registered_model_name='your_catalog.your_schema.your_model', model_config={'chain_type': 'stuff'})`.

affects: All

Upgrade

Version history

0.1.2latest on PyPI · released Dec 20, 2024

Audit

Dependencies

langchainrequiredCore LangChain framework, implicitly required for using the integrations.

langchain-communityoptionalMany Databricks integrations were initially in or moved to langchain-community before the dedicated databricks-langchain package. Often installed alongside.

mlflow>=2.9.0optionalRequired for MLflow integration features, such as logging and tracking LangChain models.

databricks-sdkoptionalOften needed for authentication and programmatic interaction with Databricks services, especially when running outside a Databricks Workspace.

Agent activity

26 hits · last 30 days

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OpenAI (training)

Resources

githubhttps://github.com/langchain-ai/langchain-databricks ↗

packagehttps://pypi.org/project/langchain-databricks/ ↗

changeloghttps://github.com/langchain-ai/langchain-databricks/releases ↗