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Databricks

Databricks 智能平台是全球首个由生成式AI驱动的数据智能平台。将AI融入您业务的每一个方面。

Databricks以多种方式拥抱LangChain生态系统：

  1. 🚀 模型服务 - 通过高可用性和低延迟的推理端点，访问最先进的大型语言模型，如DBRX、Llama3、Mixtral或您微调的模型，访问Databricks模型服务。LangChain提供LLM（Databricks）、聊天模型（ChatDatabricks）和嵌入（DatabricksEmbeddings）的实现，简化了您在Databricks模型服务上托管的模型与LangChain应用程序的集成。
  2. 📃 向量搜索 - Databricks向量搜索是一个无服务器的向量数据库，完美集成于Databricks平台。使用DatabricksVectorSearch，您可以将高度可扩展和可靠的相似性搜索引擎纳入您的LangChain应用程序。
  3. 📊 MLflow - MLflow是一个开源平台，用于管理完整的机器学习生命周期，包括实验管理、评估、追踪、部署等。MLflow的LangChain集成简化了开发和操作现代复合机器学习系统的过程。
  4. 🌐 SQL数据库 - Databricks SQL与LangChain中的SQLDatabase集成，使您能够访问自动优化、性能卓越的数据仓库。
  5. 💡 开放模型 - Databricks开源模型，如DBRX，可通过Hugging Face Hub获得。这些模型可以直接与LangChain一起使用，利用其与transformers库的集成。

安装​

第一方 Databricks 集成可在 langchain-databricks 第三方库中获得。

pip install langchain-databricks

聊天模型​

ChatDatabricks 是一个聊天模型类，用于访问托管在 Databricks 上的聊天端点，包括最先进的模型，如 Llama3、Mixtral 和 DBRX，以及您自己微调的模型。

from langchain_databricks import ChatDatabricks

chat_model = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")

有关如何在您的 LangChain 应用程序中使用它的更多指导，请参见 使用示例。

大型语言模型​

Databricks is an LLM class to access completion endpoints hosted on Databricks.

caution

Text completion models have been deprecated and the latest and most popular models are chat completion models. Use ChatDatabricks chat model instead to use those models and advanced features such as tool calling.

from langchain_community.llm.databricks import Databricks

llm = Databricks(endpoint="your-completion-endpoint")

See the usage example for more guidance on how to use it within your LangChain application.

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

embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")

See the usage example for more guidance on how to use it within your LangChain application.

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 tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.

from langchain_databricks.vectorstores import DatabricksVectorSearch

dvs = DatabricksVectorSearch(
    endpoint="<YOUT_ENDPOINT_NAME>",
    index_name="<YOUR_INDEX_NAME>",
    index,
    text_column="text",
    embedding=embeddings,
    columns=["source"]
)
docs = dvs.similarity_search("What is vector search?)

See the usage example for how to set up vector indices and integrate them with LangChain.

MLflow Integration​

In the context of LangChain integration, MLflow provides the following capabilities:

  • Experiment Tracking: Tracks and stores models, artifacts, and traces from your LangChain experiments.
  • Dependency Management: Automatically records dependency libraries, ensuring consistency among development, staging, and production environments.
  • Model Evaluation Offers native capabilities for evaluating LangChain applications.
  • Tracing: Visually traces data flows through your LangChain application.

See MLflow LangChain Integration to learn about the full capabilities of using MLflow with LangChain through extensive code examples and guides.

SQLDatabase​

You can connect to Databricks SQL using the SQLDatabase wrapper of LangChain.

from langchain.sql_database import SQLDatabase

db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")

See Databricks SQL Agent for how to connect Databricks SQL with your LangChain Agent as a powerful querying tool.

Open Models​

To directly integrate Databricks's open models hosted on HuggingFace, you can use the HuggingFace Integration of LangChain.

from langchain_huggingface import HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="databricks/dbrx-instruct",
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
llm.invoke("What is DBRX model?")

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