MCP Chat Project is an end-to-end conversational AI platform that lets teams spin up production-grade chat assistants quickly, then plug them into any product or workflow.
Goal- Deliver fast, context-aware conversations that feel natural and solve real world problems. Tech Stack- Backend: FastAPI · LangChain (LLM orchestration) · PostgreSQL · Redis Frontend- Next.js · React · Tailwind · Chakra UI Messaging- WebSocket + Server Sent Events for low-latency streaming ML- OpenAI / Ollama-compatible adapters, custom fine-tuned models, Whisper ASR, AWS Polly TTS Core Features - Multi-channel (web, Slack, WhatsApp)
- Retrieval Augmented Generation (RAG) with vector stores
- Voice in / voice out
- Session memory and user profiles
- Admin dashboard with analytics and feedback loop Target Users- Product teams that need rapid Q&A bots, campus help desks, healthcare triage flows, and hobby projects that want an LLM back end without the heavy lifting.
1 · Why it exists Chatbots usually hit one of two walls: they are either too shallow (scripted flows) or too heavy (complex infra). MCP Chat Project fills the middle lane — opinionated defaults, but every layer is hackable.
2 · High-level architecture
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Client (Next.js) <——WebSocket——> Gateway (FastAPI)
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RAG Service LLM Service
(vector store, (OpenAI, local
doc loaders) GGUF models)
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PostgreSQL + Redis Task Queue
Events stream from the gateway to the UI so tokens appear as soon as the model generates them.
A lightweight plugin system lets you inject custom tools — SQL queries, Python snippets, GraphQL calls — straight into the prompt chain.
git clone https://github.com/your-org/mcp-chat-project cd mcp-chat-project docker compose up --build The stack boots with demo endpoints, an in-memory vector store, and a playground UI at localhost:3000.