LangChain Embedding

This documentation describes the integration of MindsDB with LangChain, a framework for developing applications powered by language models. The integration allows for the deployment of LangChain models within MindsDB, providing the models with access to data from various data sources.

Prerequisites

Before proceeding, ensure the following prerequisites are met:

1. Install MindsDB locally via Docker or Docker Desktop.
2. To use LangChain within MindsDB, install the required dependencies following this instruction.

Setup

Create an AI/ML engine from the LangChain Embedding handler.

CREATE ML_ENGINE embedding
FROM langchain_embedding;

Create a model using embedding as an engine and providing your OpenAI API key.

CREATE MODEL embedding_model
PREDICT embeddings
USING
      engine = "embedding",
      class = "embedding_model_provider",
      model = "embedding_model_name",
      openai_api_key = "sk-xxx",
      input_columns = ["column1", "column2", ...];

The following are the required and optional parameters:

• engine is a required parameter. It defines the AI engine, as created with the CREATE ML_ENGINE statement, to be used.
• class is a required parameter. It defines the model provider, such as "OpenAI" or "HuggingFace".
• model is a required parameter. It defined the embedding model, such as text-embedding-3-small.
• openai_api_key is a required parameter when using OpenAI as a provider.
• input_columns is an optional parameter. It defines the column(s) to be processed by the embedding model.

Usage

The following usage examples utilize embedding to create a model with the CREATE MODEL statement.

• Using the OpenAI models:

CREATE MODEL openai_embedding_model
PREDICT embeddings
USING
      engine = "embedding",
      class = "OpenAIEmbeddings",
      model = "text-embedding-3-small",
      openai_api_key = "sk-xxx",
      input_columns = ["content"];

• Using the HuggingFace models:

CREATE MODEL hf_embedding_model
PREDICT embedding
USING
      engine = 'embedding',
      class = 'HuggingFaceEmbeddings',
      model = "sentence-transformers/all-mpnet-base-v2";

Ensure that the model has been created successfully before using it. To do that, use the DESCRIBE command and look at the status column.

DESCRIBE openai_embedding_model;

Go to the Use Cases section to see more examples. </Tip>