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| LangGraph is a library for building stateful, multi-step workflows using LangChain agents and tools. It introduces a graph-based abstraction where nodes represent steps (e.g., retrieval, generation, decision-making) and edges define transitions between them. | |
| Key Concepts: | |
| - Nodes: Functional units like retrievers, LLMs, or tool wrappers. | |
| - Edges: Conditional or static paths that control flow between nodes. | |
| - State: A shared dictionary passed and updated across nodes. | |
| LangGraph supports asynchronous execution, retries, and streaming, making it ideal for complex agentic workflows. | |
| Retrieval-Augmented Generation (RAG) is a technique that enhances LLM responses by injecting external knowledge. It typically involves: | |
| 1. Querying a vector store to retrieve relevant documents. | |
| 2. Passing those documents as context to the LLM. | |
| 3. Generating a grounded, context-aware response. | |
| Benefits of RAG: | |
| - Reduces hallucinations by anchoring responses in real data. | |
| - Enables domain-specific QA without fine-tuning. | |
| - Improves transparency and traceability. | |
| LangChain provides the building blocks for RAG: | |
| - Document loaders and chunkers | |
| - Embedding models and vector stores (e.g., FAISS, Chroma) | |
| - Chains and agents for orchestration | |
| LangGraph extends LangChain by enabling: | |
| - Explicit control flow | |
| - Modular design | |
| - Debuggable, reproducible workflows | |
| Use Cases: | |
| - Academic QA bots | |
| - Internal knowledge assistants | |
| - Modular agent systems with tool use | |
| This primer serves as a foundational reference for building explainable, modular GenAI systems using LangGraph and RAG. |