LangChain with OpenAI, RAG, SQL Database, LangSmith, and Chroma

This Jupyter Notebook demonstrates the implementation of LangChain with OpenAI, Retrieval-Augmented Generation (RAG), a generative process using an SQL database, LangSmith for monitoring, and Chroma for vector storage.

Overview

The notebook provides a step-by-step guide to:

1. LangChain Integration: Utilize LangChain to manage and orchestrate language model interactions.
2. OpenAI API: Leverage OpenAI's powerful language models for text generation.
3. Retrieval-Augmented Generation (RAG): Combine retrieval-based methods with generative models to enhance the quality of generated text.
4. SQL Database Interaction: Connect to an SQL database to retrieve and manipulate data, which is then used in the generative process.
5. LangSmith Monitoring: Use LangSmith to monitor and debug the LangChain pipeline.
6. Chroma Vector Storage: Store and retrieve embeddings using Chroma, a vector database.

Prerequisites

Before running the notebook, ensure you have the following installed:

• Python 3.7 or higher
• Jupyter Notebook
• Required Python packages:

  pip install langchain openai sqlalchemy langsmith chromadb

Setup

1. OpenAI API Key: Obtain an API key from OpenAI and set it as an environment variable:

   export OPENAI_API_KEY='your-api-key'

2. LangSmith API Key: Obtain an API key from LangSmith and set it as an environment variable:

   export LANGCHAIN_API_KEY='your-langsmith-api-key'

3. SQL Database: Ensure you have access to an SQL database. Update the connection string in the notebook to match your database configuration.

4. Chroma Setup: Chroma will run locally by default. No additional setup is required unless you want to use a remote instance.

Notebook Structure

1. Environment Setup: Import necessary libraries and set up the environment.
2. LangChain Initialization: Initialize LangChain with OpenAI's language model.
3. LangSmith Monitoring: Configure LangSmith for monitoring and debugging.
4. RAG Implementation: Implement the Retrieval-Augmented Generation process.
5. Chroma Vector Storage: Store and retrieve embeddings using Chroma.
6. SQL Database Interaction: Connect to the SQL database, retrieve data, and use it in the generative process.
7. Generative Process: Generate text based on the retrieved data and user queries.

Usage

1. Open the Jupyter Notebook.
2. Run each cell sequentially to set up the environment, initialize components, and execute the generative process.
3. Modify the queries and parameters as needed to interact with different datasets or generate varied outputs.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.