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KB-Agent-Platform — Enterprise Knowledge Base Q&A Platform (RAG + Multi-Agent + MCP)

In one sentence: an enterprise knowledge base Agent based on RAG and LangGraph Supervisor, with SSE streaming Q&A with citation tracing, dual MCP entry points, and one-click deployment via docker compose.

Architecture

Frontend Vue3 ──SSE──> FastAPI ──> Supervisor (LLM routing) ──┬─> KB Agent ──> Qdrant (4096-dim) └─> Chat Agent MCP Host (Cursor/Claude) ──stdio──> MCP Server ──> same Qdrant img.png

Related MCP server: tero-mcp-lite

Quick Start

  1. https://github.com/embracingTheFuture/kb-agent-platform.git && cd kb-agent-platform

  2. cp .env.example .env # fill in your key

  3. docker compose up --build -d

  4. docker compose run --rm api python task2_pipeline.py # ingest data

  5. Open http://localhost:8000/docs

Screenshots

img_1.png

Quality: Evaluation Set Regression

6 test cases covering routing correctness + citation integrity, reproducible inside the container.

ID

First 15 characters

Expected

Actual

Passed

ID: 1

First 15 characters: Phase 2 schedule and main project positioning.

Expected: KB

Actual: KB

PASS

ID: 2

First 15 characters: What Day16 in the roadmap is for

Expected: KB

Actual: KB

PASS

ID: 3

First 15 characters: Introduce yourself in one sentence.

Expected: CHAT

Actual: CHAT

PASS

ID: 4

First 15 characters: Help me write a poem about autumn.

Expected: CHAT

Actual: CHAT

PASS

ID: 5

First 15 characters: How many medals did Team China win at the 2024 Olympics

Expected: CHAT

Actual: CHAT

PASS

ID: 6

First 15 characters:

Expected: CHAT

Actual: CHAT

PASS

6 test cases, 6 passed, 0 failed

Technical Highlights

  • Handwritten ReAct core (tool registry + while loop, no framework)

  • Self-developed chunking-vectorization-retrieval pipeline, Qdrant cosine similarity

  • Citation tracing: numbered context + [i] annotations + citations structure

  • Supervisor multi-agent routing + conversation memory compression (rolling summary)

  • MCP Server dual entry points / SSE streaming / evaluation set regression

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