LangChain chatbot

LangChain chatbot is a conversational agent built with the LangChain framework that combines large language models (LLMs), memory, tool calling, and retrieval-augmented generation (RAG) to deliver context-aware answers. The bot flows through a chain: it ingests a user prompt, enriches it with conversation history held in memory, optionally queries a vector database for relevant documents, and passes the compiled context to an LLM such as GPT-4. Integrated tools—SQL, API wrappers, code execution—let it fetch live data or run calculations before crafting a response. Guardrails filter disallowed content, while streaming callbacks return tokens in real time for low latency. Developers configure the chatbot in Python or TypeScript, swap models or vector stores with one line of code, and deploy via FastAPI, AWS Lambda, or Vercel edge functions. Use cases range from customer-support assistants that cite knowledge-base articles to internal copilots that summarize ticket queues or draft Jira updates. LangChain’s modular abstractions slash boilerplate, enabling teams to ship secure, scalable chatbots in days instead of months.