EvidenceBridge Multimodal MCP

An evidence-first research workbench for policy, legal, teaching, and public-information materials. It is designed as a verifiable engineering artifact for the HKU AI Engineer / Research Assistant II role (537095) and the PolyU Research Assistant role (260401018), not as a generic chatbot.

The default mode needs no model key. A deterministic agent searches uploaded sources through a real MCP stdio server, verifies the selected quotation, routes uncertain or high-risk requests to human review, streams the answer to the UI, and records an audit trail. OpenAI-compatible and Azure OpenAI adapters are included for credentialed environments.

Truthful status: Azure adapter implemented but not verified against a live Azure deployment. No Azure, production deployment, real-user, real-LLM, or completed model-training result is claimed.

What works

• Next.js 15 / React / TypeScript workbench with Vercel AI SDK streaming, source upload, tool trace, evidence cards, locators, and audit page.

• Flask / Gunicorn service for UTF-8 text, Markdown, and PDF extraction; bounded chunking; lexical retrieval; exact/fuzzy citation verification; version diff; review routing; health checks; structured errors; and privacy-conscious logs.

• Official @modelcontextprotocol/sdk stdio server with executable search_documents, get_evidence, verify_citation, compare_document_versions, and request_human_review tools and Zod input schemas.

• PostgreSQL schema managed by Prisma ORM, checked-in migration and seed, plus runtime Prisma conversation persistence and Flask access to the same schema.

• Image and audio adapter boundaries that accept an explicit transcript fixture. OCR/transcription is never fabricated when an engine is unavailable.

• A 45-question gold dataset comparing direct answer, basic RAG, and RAG with citation verification. Raw per-question outputs and run metadata are saved.

• Docker Compose PostgreSQL, Gunicorn config, Apache reverse-proxy example, CI, unit/integration tests, and an optional—not completed—LoRA router experiment.

Related MCP server: pramana-mcp

Architecture

flowchart LR
  U[Researcher] --> W[Next.js workbench]
  W --> A[Vercel AI SDK agent / deterministic runner]
  A <-->|MCP over stdio| M[TypeScript MCP server]
  M -->|bounded HTTP + token| F[Flask evidence service]
  F --> P[(PostgreSQL)]
  W -->|Prisma ORM| P
  F --> X[Parser + chunker + verifier]
  A --> H[Human-review boundary]
  X --> E[Evidence + locators]
  M --> T[Tool-call and audit records]

See ARCHITECTURE.md for trust boundaries and sequence details.

Quick start

Prerequisites: Node.js 22+, pnpm 11+, Python 3.12+, and Docker with Compose.

cp .env.example .env
./scripts/bootstrap.sh
./scripts/dev.sh

Open http://localhost:3000, choose Load synthetic demo set, and ask “When do applications close?” The deterministic mock mode is the default and uses no external API.

Manual equivalent:

docker compose up -d postgres
pnpm install --frozen-lockfile
python3 -m venv .venv
.venv/bin/pip install -r apps/flask-service/requirements-dev.txt
pnpm db:generate && pnpm db:migrate && pnpm db:seed
pnpm --filter @evidencebridge/mcp-server build
pnpm dev

For a database-free UI demonstration, omit DATABASE_URL; the Flask service uses an explicit in-memory repository. That mode is ephemeral and is not evidence of PostgreSQL integration. Docker-backed CI runs the PostgreSQL integration test.

Model providers

Set MODEL_PROVIDER to one of:

• mock: deterministic offline workflow. It is reproducible but is not a real LLM result.

• openai-compatible: uses OPENAI_API_KEY, OPENAI_BASE_URL, and MODEL_NAME through the Vercel AI SDK.

• azure: uses AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, AZURE_OPENAI_DEPLOYMENT, and AZURE_OPENAI_API_VERSION.

Credentialed modes use Vercel AI SDK structured generation only after server-enforced MCP retrieval and risk preflight. The selected quotation is then verified again through MCP before the AI data stream is opened; an out-of-set chunk, citation mismatch, or high-risk request creates a review and returns a refusal. Documents are explicitly treated as untrusted data. Never commit .env.

Verification and evaluation

pnpm lint
pnpm test
pnpm build
pnpm evaluate
.venv/bin/python peft/smoke_test.py

The saved v2 deterministic run 20260908T090054Z used the same fixed 45-question regression set as the original v1 diagnosis. After metadata-aware version filtering, latest-version tie-breaking, content-coverage ranking, and a small disclosed normalization map, the verified workflow reached 1.000 on strict citation accuracy, evidence-retrieval recall, tool-call success, and human-review recall, with a 0.000 unsupported/mismatch rate. These are in-sample deterministic regression results after inspecting the same dataset, not held-out, real-LLM, or production-performance results. The original v1 run is retained for comparison. See EVALUATION.md and the raw records under evaluation/runs/.

Project map

apps/web             Next.js UI, provider adapters, agent route, audit view
apps/flask-service   Parsing, chunking, retrieval, verification, persistence
apps/mcp-server      Official MCP SDK server and tool schemas
packages/db          Prisma schema, migration, seed, runtime client
evaluation           45 gold questions, runner, saved raw/summary results
examples/materials   Clearly labelled synthetic source documents
peft                 Optional LoRA router script and offline smoke check
deploy/apache        Reverse-proxy example

Evidence boundaries

• The six sample materials are synthetic and MIT-licensed with this repository.

• PDF text extraction is local; scanned PDF OCR is not bundled.

• Image/audio are adapter-complete and fixture-tested, but require an explicit transcript unless an optional engine is installed.

• PostgreSQL Docker execution was unavailable in the authoring environment, but GitHub Actions verified PostgreSQL 16 startup, the checked-in Prisma migration, and all 13 Python tests including the PostgreSQL integration path. Azure live calls remain unverified because no cloud credentials/resources were available.

• The PEFT code is an optional reproducible experiment scaffold. Only its dataset/metric smoke test is run by default; no trained adapter or claimed PEFT scores are included.

Read LIMITATIONS.md before using this system for decisions and SECURITY.md before any public deployment.