MF-RAG: Mutual Fund Document Search for Claude

An MCP server that lets Claude Desktop and Claude Code answer questions about Indian mutual funds from official documents: SEBI scheme documents, AMC factsheets, and AMFI investor education pages.

Why LLM-Free?

Traditional RAG pipelines use a local LLM to generate answers:

Query → Embed → Search → Context → Local LLM → Answer
                                       ↓
                               Slow, inaccurate, jailbreak-prone

This project skips the local LLM entirely:

Query → Embed → Hybrid search → RRF → BGE rerank → Return chunks → Claude synthesizes
                                                                    ↓
                                                            Accurate and safe

Comparison

The local LLM (qwen2.5:7b) failed to extract expense ratios from tables that were right in its context. Claude handles this correctly.

Related MCP server: Hybrid RAG Project MCP Server

Features

• Hybrid search: dense (bge-m3) and keyword (BM25) retrieval, fused with reciprocal rank fusion and reranked by BAAI/bge-reranker-v2-m3

• Layout-aware PDF parsing: a Python Docling service turns factsheets and scheme documents into clean chunks, including tables, charts, and multi-fund pages

• Document types: SID, KIM, factsheets, and HTML investor-education pages

• Filters: by fund name, AMC, scheme type, and document type

• Citations: every result carries its source document, page numbers, and URL

• Chunk review before publishing: nothing reaches search until it passes automated checks and, where flagged, a human review

• Per-AMC access scopes: each credential can only read the documents its scope allows

Quick Start

Local development runs entirely in Docker Compose.

cp .env.example .env
# Fill in the local credential, AMC scope, and review login placeholders.
docker compose --env-file .env -f docker/docker-compose.dev.yml up -d --build --wait

Open http://127.0.0.1:3000/review. The local development guide covers migrations, tests, ingestion, review publication, evaluation, and reset.

To run the Docling parse service as well:

docker compose --env-file .env -f docker/docker-compose.dev.yml --profile docling up -d docling

Authentication

Every MCP client sends a bearer credential and exactly one X-AMC-Scope header. Requests without a scope are rejected; there is no unscoped fallback.

Example Queries

• "What mutual fund schemes do you have access to?"

• "Compare the expense ratios of HDFC and ICICI equity funds"

• "What are the risk factors for debt mutual funds?"

• "Which funds have the lowest expense ratios?"

• "What is the investment objective of Axis Bluechip Fund?"

Architecture

Ingestion

Source connectors ──▶ Fetch ──▶ Parse ──────────────▶ Chunk + inspect ──▶ Review ──▶ Publish
(SEBI, AMC sites,              (Docling service,       (automated checks)  (UI)      (embed into
 AMFI pages)                    Python, HTTP)                                         pgvector)

The pipeline (apps/pipeline) runs as a scheduler plus workers that claim stages from PostgreSQL. PDFs go to the Docling service over a versioned HTTP contract; it returns structured chunks with page positions. Flagged chunks are held for review, and only published chunks are searchable.

Retrieval

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Claude Desktop │     │   MCP Server     │     │   PostgreSQL    │
│  or Claude Code │────▶│   (Bun + TS)     │────▶│   + pgvector    │
└─────────────────┘     └────────┬─────────┘     └─────────────────┘
                                 │
                         ┌───────┴────────┐
                         ▼                ▼
                ┌─────────────────┐ ┌────────────────────┐
                │     Ollama      │ │ Hugging Face TEI   │
                │    (bge-m3)     │ │ bge-reranker-v2-m3 │
                │  Embedding Only │ │   Cross-encoder    │
                └─────────────────┘ └────────────────────┘

MCP Tools

Project Structure

sebi-rag/
├── apps/
│   ├── mcp-server/        # MCP server (main entry point)
│   ├── pipeline/          # Document pipeline: discover, fetch, parse, inspect, publish
│   ├── ingestion/         # Chunking and HTML ingestion
│   ├── scraper/           # SEBI, AMFI, and AMC source scrapers
│   ├── api/               # Review API and UI (chunks drawn over the source PDF page)
│   └── cli/               # Command-line interface
├── packages/
│   ├── core/              # Shared logic: embedding, chunk inspection, rate limiting
│   ├── db/                # Drizzle ORM schema and migrations
│   ├── vector/            # pgvector client and hybrid search
│   └── types/             # Shared TypeScript types
├── python/
│   ├── docling_service/   # Docling PDF parse service (Flask + gunicorn, pytest)
│   └── table_extractor/   # Standalone table extraction helper
├── config/access-scopes/  # Per-AMC source allowlists
├── data/                  # HTML sources and sample PDFs
├── tests/                 # Unit, integration, retrieval, and parser fixtures
├── scripts/               # Utility and evaluation scripts
└── docker/                # Compose files and the Docling image

Development

Use the commands in the local development guide. The one-shot runner service runs repository commands inside the same image and network as PostgreSQL, Redis, embeddings, reranking, MCP, and review.

• TypeScript tests: bun test

• Docling service tests: pytest in python/docling_service

Pre-embedding chunk review

PDF and HTML ingestion stop after automated inspection by default. Candidate chunks and their findings are stored outside the searchable set, so a malformed chunk cannot reach retrieval before review.

The review screen shows every flagged chunk for a document on one page, with the neighbouring chunks, the source page with the chunk outlined on it, the inferred scope, the rule that flagged it, and an audit-logged reviewer decision. Use Edit all candidate chunks to correct extracted text before approval. Each save needs a reason, records the full before/after revision, and re-inspects the whole document, because an edit can shift neighbouring chunk boundaries. Retrieval reads only published rows; staged and retired versions are excluded.

Data Sources

• SEBI: Scheme Information Documents (SID) and Key Information Memorandums (KIM)

• AMC websites: monthly factsheets and scheme pages

• AMFI: investor centre and knowledge centre pages

Resource Requirements

The self-hosted models remove per-query API charges but need memory and CPU:

• At least 8GB RAM to serve bge-m3 and the reranker on CPU

• The Docling service is limited to 6GB RAM and 4 CPUs in the dev compose file

• Any PostgreSQL with pgvector works for the database

Environment Variables

See .env.example for the full list.

Security

The LLM-free design is safer by construction:

1. No LLM to jailbreak: adversarial prompts have nothing to manipulate

2. No content echoing: attack content never appears in output

3. SQL injection safe: Drizzle ORM uses parameterized queries

4. Claude is the safety layer: Claude's guardrails handle interpretation

See the jailbreak tests for details.

License

MIT — see LICENSE.