Developing a corporate documentation chatbot
In this section, we will build a corporate documentation chatbot that leverages LangChain for LLM interactions and LangGraph for state management and workflow orchestration. LangGraph complements the implementation in several critical ways:
- Explicit state management: Unlike basic RAG pipelines that operate as linear sequences, LangGraph maintains a formal state object containing all relevant information (queries, retrieved documents, intermediate results, etc.).
- Conditional processing: LangGraph enables conditional branching based on the quality of retrieved documents or other evaluation criteria—essential for ensuring reliable output.
- Multi-step reasoning: For complex documentation tasks, LangGraph allows breaking the process into discrete steps (retrieval, generation, validation, refinement) while maintaining context throughout.
- Human-in-the-loop integration: When document quality or compliance cannot be automatically...
