Tag: Lang Chain

  • Lang Chain and Lang Graph

    1. Why Do We Need LangChain or LangGraph?

    So far in the series, we’ve learned:

    • LLMs → The brains
    • Embeddings → The “understanding” of meaning
    • Vector DBs → The memory store

    But…
    How do you connect them into a working application?
    How do you manage complex multi-step reasoning?
    That’s where LangChain and LangGraph come in.

    2. What is LangChain?

    LangChain is an AI application framework that makes it easier to:

    • Chain multiple AI calls together
    • Connect LLMs to external tools and APIs
    • Handle retrieval from vector databases
    • Manage prompts and context

    It acts as a middleware layer between your LLM and the rest of your app.

    Example:
    A chatbot that:

    1. Takes user input
    2. Searches a vector database for context
    3. Calls an LLM to generate a response
    4. Optionally hits an API for fresh data

    3. LangGraph — The Next Evolution

    LangGraph is like LangChain’s “flowchart” version:

    • Allows graph-based orchestration of AI agents and tools
    • Built for agentic AI (LLMs that make decisions and choose actions)
    • Makes state management easier for multi-step, branching workflows

    Think of LangChain as linear and LangGraph as non-linear — perfect for complex applications like:

    • Multi-agent systems
    • Research assistants
    • AI-powered workflow automation

    4. Core Concepts in LangChain

    • LLM Wrappers → Interface to models (OpenAI, Anthropic, local models)
    • Prompt Templates → Reusable, parameterized prompts
    • Chains → A sequence of calls (e.g., “Prompt → LLM → Post-process”)
    • Agents → LLMs that decide which tool to use next
    • Memory → Store conversation history or retrieved context
    • Toolkits → Prebuilt integrations (SQL, Google Search, APIs)

    5. Where LangChain/LangGraph Fits in a RAG Pipeline

    1. User Query → Passed to LangChain
    2. Retriever → Pulls embeddings from a vector DB
    3. LLM Call → Uses retrieved docs for context
    4. Response Generation → Returned to user or sent to next step in LangGraph flow

    6. Key Questions

    • Q: How is LangChain different from directly calling an LLM API?
      A: LangChain provides structure, chaining, memory, and tool integration — making large workflows maintainable.
    • Q: When to use LangGraph over LangChain?
      A: LangGraph is better for non-linear, branching, multi-agent applications.
    • Q: What is an Agent in LangChain?
      A: An LLM that dynamically chooses which tool or action to take next based on the current state.