🤖 RAG Knowledge Base Chatbot with MCP Servers
Build a production-ready RAG chatbot that answers questions from your documentation using vector search MCP servers and AI agents.
🛠️ Tools Used in This Workflow
📝 Step-by-Step Guide
Step 1: Prepare Your Knowledge Base
Collect all documentation sources: product docs, API references, FAQs, and internal wikis. Structure them as Markdown files. The RAG Docs MCP server will handle chunking, embedding, and vector storage automatically.
Step 2: Configure Vector Search
Set up the RAG Docs MCP server pointing to your documentation directory. It creates vector embeddings using local models (no API key needed for basic setup). Configure chunk size (500-1000 tokens) and overlap (100 tokens) for optimal retrieval.
Step 3: Build the Retrieval Pipeline
When a user asks a question, the workflow: (1) Embeds the query, (2) Searches for top-5 relevant chunks via MCP, (3) Re-ranks results by relevance, (4) Passes context + question to the LLM. This ensures answers are grounded in your actual documentation.
Step 4: Add Source Citations
Configure the agent to always cite sources in its responses. Each answer should include links to the relevant documentation pages. This builds user trust and allows them to read more context.
Step 5: Deploy and Monitor
Wrap the workflow in a simple API endpoint. Add logging for all queries and responses. Monitor: answer quality (user feedback), retrieval relevance (hit rate), and latency. Continuously add new documentation to improve coverage.
💡 Use Cases
- Product teams building self-service documentation bots
- Internal IT helpdesks reducing ticket volume
- Developer tools companies offering AI-powered docs search
🔗 Related Tools
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