Langchain Series: Creating Generative AI Applications

Jul 11, 2024

Mindmap

Langchain Series: Creating Generative AI Applications

Introduction

  • Presenter: Krishak
  • Platform: YouTube Channel
  • Topic: Langchain framework for creating generative AI applications.
  • Objectives: To create Q&A chatbots, RAG (Retrieval Augmented Generation) applications using both paid and open-source LLM (Large Language Models).
  • Instruction: Like the video, share it, and comment to support the creator.

Introduction to Langchain Ecosystem

  • Updated modules in Langchain documentation revolve around LLM models.
  • Modules Covered: LSmith (MLOps activities), Langchain Ecosystem, API creation using FastAPI.
  • Langchain Modules:
    • LSmith: For monitoring, debugging, testing ML applications.
    • Langserv: Deployment and API creation using FastAPI.
    • Chains, Agents, Retrieval: Key concepts in Langchain for chaining tasks, using agents, and retrieving data.
    • Langchain Expression Language (LC): For building generative AI applications.
    • Tools: Model IO, Retriever, Tooling.

Practical Implementation

Creating a Simple Chatbot

  1. Setup: Create virtual environment, set API keys, load environment variables.
  2. Modules Used: Chat prompt templates, Chat OpenAI, String Output Parser, Streamlit for the web interface.
  3. Steps:
  • Define prompt templates.
  • Setup Streamlit framework.
  • Integrate Chat OpenAI (OpenAI models) and test.
  • Use LSmith monitoring for observability.

Using Olama for Local LLM Models

  1. Setup: Download and install Olama for running LLM models locally.
  2. Implement Similar Code: To integrate LLM models using Olama.
  3. Run Streamlit: For chatbot application, interact with local models such as Llama2 or other open-source models.
  4. Example Code: Provided for Olama run and local Llama2 integration.

Creating APIs for LLM Applications

  1. Objective: Create an API for deploying LLM models using Langchain and FastAPI.
  2. Modules Used: FastAPI, Langchain Community (for tools), Langserv for API creation, Streamlit for API testing.
  3. Steps:
  • Define routes in FastAPI app file (app.py).
  • Create multiple routes for interacting with different LLM models (OpenAI, Llama2).
  • Use Uvicorn to run the FastAPI application.
  • Define input/output formats and API structure.
  1. Testing: Use client-side code to test APIs using Streamlit (client.py).
  2. API Documentation: Swagger UI for API documentation and testing.

Advanced RAG Pipeline Using Transformations

  1. Objective: Implement an advanced RAG pipeline to query multiple data sources.
  2. Steps:
  • Data Injection: Load data from PDFs, web-based sources using Langchain document loaders.
  • Data Transformation: Split data into chunks using recursive character splitter.
  • Embedding Vectors: Convert chunks into vectors using OpenAI embeddings or AMA embeddings.
  • Vector Store: Store vectors in a database such as Chroma or FES.
  • Query Vector: Perform similarity search on vector store to retrieve relevant documents.
  1. Tools Used: Langchain Chains, Retrievers, Embeddings, and Vector Stores modules.

Integrating Tools and Agents for Multisearch Agent

  1. Objective: Use multiple tools and agents to create a powerful Q&A system searching across multiple sources.
  2. Tools: Wikipedia, Research Articles (Rift), and custom PDF sources.
  3. Agent Execution: Use Langchain agents to determine the sequence of actions.
  4. Implementation: Define tools, integrate agents, and use Langchain's AgentExecutor for querying multiple sources.

End-to-End RAG Project Using Open Source LLM Models and Groq Inferencing Engine

  1. Setup: Set up Groq API access, use Groq inferencing engine for LLM models.
  2. Open Source Models: Use only open-source LLM models like Llama, Mistral.
  3. Steps:
  • Create a web application using Streamlit.
  • Load data from web sources, transform and embed data into vectors.
  • Integrate open-source models with Groq inferencing engine.
  • Use chain and retriever concepts to enable querying against embedded data.
  1. Performance Testing: Evaluate speed and response quality of LLM models.

Integration with Hugging Face Libraries and Open Source Models

  1. Objective: Create a RAG application using only open-source models from Hugging Face.
  2. Tools: Langchain, Hugging Face libraries, Open-source LLM models (Mistral).
  3. Steps:
  • Load and embed data using open-source models and embeddings from Hugging Face.
  • Use retriever interfaces to enhance querying capabilities.
  • Set up Hugging Face token for API access.
  1. Implementation: Define prompts, create retrieval chains, and execute queries to retrieve relevant documents from PDF sources.

Summary

  • Langchain provides a comprehensive framework for creating, managing, and deploying generative AI applications.
  • Using both paid and open-source models, it supports multiple data injection techniques, transformation, embedding, vector storage, and querying capabilities.
  • Integration with tools and agents allows for the creation of advanced Q&A systems and RAG pipelines.
  • Practical examples demonstrate the application of Langchain in real-world scenarios, making it a valuable tool for AI development.

Final Notes

  • Practice and experimentation are crucial for mastering these concepts.
  • Exploring different modules, models, and configurations will help in developing robust AI applications.
  • Keep up with the latest updates and modules in Langchain to leverage new features and improvements.
  • Support the content by liking, sharing, and subscribing to the YouTube channel.