banhmi

Evidence-only RAG corpus + MCP server for Southeast-Asian banking & financial regulation and cross-cutting technology law — one codebase, one corpus per country.

Docs · Architecture · Plan

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banhmi crawls official government sources, extracts legal documents into a citable RAG corpus, and serves evidence over MCP — exact citations, validity, amendment relations, provenance, and coverage gaps. It does not answer questions: your agent/model connects, retrieves evidence, and decides the answer.

Use it over MCP

Remote MCP (Streamable HTTP), public, HTTPS, no signup or API key:

Jurisdiction	Endpoint	Ask in	Official sources
Vietnam	https://banhmi.danny.vn/mcp	Vietnamese	VBPL · Cong Bao · vanban.chinhphu · SBV
Malaysia	https://laksa.danny.vn/mcp	English	AGC Laws of Malaysia · Bank Negara Malaysia · Securities Commission
Indonesia	https://rendang.danny.vn/mcp	Indonesian	BPK JDIH · Bank Indonesia · OJK
Singapore	https://kaya.danny.vn/mcp	English	SSO · MAS · PDPC · CSA
Thailand	https://tomyum.danny.vn/mcp	Thai	OCS · BOT · SEC · ETDA
Cambodia	https://amok.danny.vn/mcp	English	NBC · ODC · CDC

Add as a custom connector (pick an endpoint above):

1. Claude (Pro/Max/Team/Enterprise) → Settings → Connectors → Add custom connector → paste URL, no auth.
2. ChatGPT (Plus/Pro/Team/Edu) → Developer mode → Settings → Apps & Connectors → Add → paste URL, no auth.
3. Grok → Settings → Connectors → Add MCP server → paste URL.
4. Gemini CLI → add under mcpServers in ~/.gemini/settings.json (httpUrl = endpoint).

Ask e.g. "What are the technology risk management requirements for banks?" — you get ranked provisions with citation, validity badge, and official source link.

Tools: search · document · corpus_status · quality_gaps · guide.

What it does

• Scope-filtered discovery of banking & financial regulation and cross-cutting technology law (cybersecurity, data protection, AI, cloud, e-transactions, payments, digital banking).
• Verbatim authoritative text — extracted from official government sources, never paraphrased.
• High-fidelity extraction — go-fitz (MuPDF) for DOCX/HTML/born-digital PDF; Google Vision OCR (batched, cached) for scanned PDFs.
• Evidence, not answers — exact native citations (VN Dieu/Khoan, MY Section/Subsection, ID Pasal/ayat), validity badges, confirmed relations, provenance, and gaps.
• Change tracking — amendments, repeals, subsidiary legislation, validity over time.

Sources are pluggable — see docs/design/SOURCES.md and docs/design/jurisdictions/.

Architecture

flowchart TB
  subgraph LOCAL["Write path: cmd/pipeline (local, CPU)"]
    DISC["Discover"] --> FETCH["Fetch official files"]
    FETCH --> EXT["Extract (go-fitz / Vision OCR)"]
    EXT --> NORM["Normalize (structure,<br/>validity, relations)"]
    NORM --> IDX["Index (chunk + embed)"]
  end

  KAGGLE["Kaggle T4 GPU<br/>Qwen3-Embedding ONNX FP16<br/>bulk embed offload"]
  IDX <-->|"dataset I/O"| KAGGLE

  subgraph RDS["AWS RDS PostgreSQL 17 + pgvector (Singapore)"]
    DB[("one database per country<br/>banhmi | laksa | rendang<br/>kaya | tomyum | amok")]
  end

  IDX -->|"write corpus over TLS"| DB

  subgraph ECS["AWS ECS on EC2 Graviton ARM64 (same VPC)"]
    MCP["ONE MCP server, all countries<br/>in-process Qwen3 embedder<br/>hybrid dense + BM25 (RRF)"]
  end

  DB --> MCP
  CF["CloudFront<br/>*.danny.vn<br/>(6 distributions, ACM TLS)"] --> MCP
  USERS["Your agents<br/>Claude | ChatGPT<br/>Gemini | Grok"] -->|"remote MCP<br/>(Streamable HTTP)"| CF

Loading

Medallion pipeline (Bronze → Silver → Gold):

1. Discover → Fetch (Bronze) — scope-filtered crawl; download raw files.
2. Extract → Normalize (Silver) — go-fitz/MuPDF (scanned PDFs via Vision OCR, batched and cached); parse provision tree, validity, relations.
3. Index (Gold) — chunk by article + Qwen3-Embedding (ONNX FP16, 1024 dims) into pgvector. Hybrid retrieval: dense vectors + BM25 sparse vectors (sparsevec), RRF-fused with a query router, current-law pre-filter.
4. Serve — CloudFront → one ECS EC2 Graviton process (in-process Qwen3 ONNX query embedder), same VPC as RDS. Each domain's GET / serves a guide page; /mcp serves agents.

See docs/ARCHITECTURE.md.

Status

Retrieval quality — golden-set eval per jurisdiction (hybrid retrieval, top-8, measured 2026-07-19 on the deployed corpora; harness in docs/design/RAG.md):

Jurisdiction	Chunks	Golden cases	Recall@8	MRR@8	Current-law precision	Abstention
🥖 Vietnam	52,546	80	92.7%	69.7%	100%	100%
🍜 Malaysia	11,286	72	94.3%	79.6%	100%	100%
🍛 Indonesia	160,142	110	79.8%	62.4%	100%	100%
🍞 Singapore	27,951	76	93.5%	79.9%	100%	97.9%
🍲 Thailand	29,736	58	89.5%	72.0%	100%	96.6%
🐟 Cambodia	2,609	42	93.1%	74.4%	100%	100%

Recall@8 = expected citations found in the top-8 evidence; current-law precision = returned primary hits that are in-force law; abstention = out-of-scope questions correctly refused.

See PLAN.md for the roadmap.

Self-host

Everything runs in podman — full guide in docs/DEVELOPMENT.md.

cp config/config.example.yaml config/config.yaml
export BANHMI_DATABASE_PASSWORD=banhmi
make dev-up        # Postgres+pgvector
make migrate       # apply schema
go run ./cmd/seed  # load config vocabularies

Build the corpus and serve MCP per docs/DEVELOPMENT.md. A fresh clone reaches ingesting + serving with no API keys. Deploy via docs/DEPLOYMENT.md.

Documentation

• Architecture — design, data model, folder layout, interfaces
• Local development — dev stack, migrations, build/run/test
• Deployment — generic 3-part deploy (worker, database, MCP)
• Publishing — listing the MCP servers in agent directories
• Plan — roadmap, phases, open decisions
• Sources · Pipeline · Schema · Extraction · RAG · Workflow Eval
• Jurisdictions — country registry · playbook · Malaysia · Indonesia / Thailand / Singapore
• Documentation index

License

Apache 2.0.