一款企业级的AI助手，能回答公司问题 内部文档和业务数据。它展示了生产形态 实现了检索增强生成（RAG），一个AI代理 工具调用，一个模型上下文协议（MCP）服务器，以及一个 评估套件——基于TypeScript单存储库，带有六边形 建筑。

投资组合项目。人工智能提供商位于一个接口（接缝）后面，所以 整个系统现在运行在确定性模拟上，并替换了OpenAI 设置一个环境变量。详见 PENDING.md。

精彩片段

• RAG 流水线 — 文档摄取（PDF，Markdown，纯文本），递归 文本分块、向量嵌入和余弦相似语义搜索 PostgreSQL + pgvector，带有HNSW索引。
• 带工具调用的AI代理 — 多步代理（最多5步），选择 介于四个只读的业务数据工具和文档搜索之间 使用Vercel AI SDK的工具。
• 模型上下文协议（MCP）服务器 — 在

AI Business Assistant

An enterprise-style AI assistant that answers questions over a company’s internal documentation and business data. It demonstrates a production-shaped implementation of Retrieval-Augmented Generation (RAG), an AI agent with tool calling, a Model Context Protocol (MCP) server, and an evaluation suite — built on a TypeScript monorepo with hexagonal architecture.

Portfolio project. The AI provider sits behind an interface (a seam), so the whole system runs today against a deterministic mock and swaps in OpenAI by setting a single environment variable. See PENDING.md.

Highlights

• RAG pipeline — document ingestion (PDF, Markdown, plain text), recursive text chunking, vector embeddings, and cosine-similarity semantic search on PostgreSQL + pgvector with an HNSW index.
• AI agent with tool calling — a multi-step agent (max 5 steps) that chooses between four read-only business-data tools and a documentation search tool using the Vercel AI SDK.
• Model Context Protocol (MCP) server — exposes the same business tools over the official MCP TypeScript SDK, reusing the exact same domain logic as the HTTP API (one source of truth).
• Evaluation suite — a 22-case dataset scoring retrieval hit rate, tool-selection accuracy and answer accuracy, runnable in CI.
• Hexagonal architecture — framework-agnostic business logic behind ports and adapters, making the code testable, AI-navigable and provider-agnostic.
• Fully typed, fully tested — 51 automated tests (unit + integration) and an end-to-end Docker Compose stack verified against real PostgreSQL.

Tech stack

Architecture

The repository is a pnpm monorepo. Business logic is written once and reused by both the HTTP API and the MCP server.

┌────────────┐     HTTP/stream      ┌─────────────────────────────┐
│  React UI  │ ───────────────────▶ │  NestJS API (apps/api)      │
│ (apps/web) │   /api proxy (nginx) │  ┌───────────────────────┐  │
└────────────┘                      │  │ AI agent + tool calling│  │
                                    │  │ RAG retrieval + ingest │  │
┌────────────┐     stdio / MCP      │  │ business use-cases     │  │