Lang Chain: Generative AI Application and Ecosystem Overview

Introduction

• Presenter: Krishak.
• Platform: YouTube Channel focused on AI and ML tutorials.

Focus of the Video

• Objective: Learn how to create generative AI and LLM applications using Lang Chain.
• Topics Covered:
  • Overview of the Lang Chain framework.
  • Discussing both paid (OpenAI) and open (Hugging Face) LLM models.
  • Explanation of the Lang Chain ecosystem.
  • Introducing tools, agents, chains, and different concepts in Lang Chain.
  • Practical project implementation.

Lang Chain Framework Overview

• Lang Chain: A framework to help create LLM-based applications like Q&A chatbots and more.
• Features: Supports various LLM models and offers tools for monitoring, debugging, testing, and deployment.
• Primary Modules:
  • lsmith for MLOps activities: monitoring, debugging, testing, etc.
  • langser for deploying applications using FastAPI.
  • Concepts in Lang Chain including chains, agents, retrieval strategies, and LangChain Expression Language (LC).

Monitoring and Deploying

• lsmith: Provides monitoring, debugging, testing, and evaluation tools. Includes a dashboard for easy visibility of analytics.
• langser: Converts chains to REST APIs for deploying applications easily.

Lang Chain Core Concepts

• Chains: Manage the flow from data injection to data transformation and retrieval, with core concepts like model I/O, retrievers, and agents.
• LangChain Expression Language (LC): Handles compositions, fallbacks, parallelization, tracing, etc.
• Vectors and Embeddings: Uses various data sources for vector embedding pathways for efficient query responses.

Using Lang Chain for Projects

• Tools and Agents: Tools are external APIs or functionalities, and agents manage the sequence of actions to handle user requests using tools. Examples include Google Search API, Wikipedia, etc.
• Practical Example: Building a multi-search agent rag application, integrating data from various platforms (like Wikipedia and research papers) into a generative AI application.

Practical Implementation Section

1. Read Data: Using web-based loader, reading documents and transforming them into smaller chunks for processing.
2. Embedding and Vector Store: Converting these chunks into vectors using embeddings like OpenAI or HuggingFace and storing in vector stores (Chroma or Faiss).
3. Creating Prompt Templates: Designing prompts to answer questions specifically using context from provided data.
4. Chains and Retrieval: Integrating LLM models, chains, and retrievers to manage Q&A setups, and showcasing the retrieval of data using Lang Chain's functionalities.
5. Using Tools: Demonstrating the use of external tools (e.g., Wikipedia) and incorporating them using Lang Chain's agents.
6. End-to-End Deployment: Showing how to transform LLM applications into REST APIs using langser and FastAPI.
7. Open Source Application Example: Implementing a Q&A system using open source LLM models from HuggingFace and deploying it using Lang Chain.

Conclusion

• Summary: Emphasized the versatility and simplicity of Lang Chain for building, monitoring, and deploying LLM-based generative AI applications. Encouraged viewers to experiment with both paid and open-source LLM models.
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Final Remarks

• Learning Outcome: Provided a comprehensive walk-through of Lang Chain's ecosystem from data ingestion to creating interactive, scalable generative AI applications.