healthy-diet-ai-agent

Healthy Diet AI Agent

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Healthy Diet AI Agent is a Bun + TypeScript backend for nutrition and healthy diet support. It features chat, food image analysis, RAG document knowledge retrieval, knowledge graph, and MOHW (Ministry of Health and Welfare) data synchronization.

This repository now supports two deployment modes:

• Standalone mode: uses SQLite, runs independently via Docker, HTTP API, or terminal CLI
• Integration mode: uses Supabase, maintaining the ability to integrate with the existing health-diet-api ecosystem

Tech Stack

• Runtime: Bun
• Language: TypeScript
• HTTP Server: Express
• Agent Framework: LangChain, LangGraph, DeepAgents
• Storage: SQLite or Supabase
• AI Integration: OpenAI-compatible API routing, with optional Google Gemini routing
• Deployment: Docker Compose

Core Features

• Nutrition chat assistant for dietary advice, meal planning, and nutrition Q&A
• Food image analysis workflow supporting meal understanding and nutrition-guided interactions
• RAG document search and document management to organize and query nutrition knowledge documents
• Version-Aware & Policy-Aware RAG evaluation engine for resolving multi-version dietary guideline temporal conflicts
• PDF processing tool (src/rag_clean/pdf_to_clean_markdown.py) for converting official guideline PDFs into structured Markdown with prose tables
• Knowledge graph extraction and search to establish structured health and diet knowledge relations
• MOHW data sync pipeline for importing public clarification and reference content
• Flexible deployment modes supporting SQLite, Supabase, HTTP API, and CLI

Planned Features

• Personalized dietary suggestions based on user profiles, preferences, and historical records
• Enhanced multimodal meal analysis for richer food context understanding and grounded responses
• Expanded admin and ingestion tooling for knowledge curation, review, and operations
• Advanced multi-step agent workflows to improve retrieval, reasoning, and task automation

Project Background

This project was originally built to complement the following two projects:

• PU-Hub/healthy-diet as the API-side project
• archie0732/healthy-diet-web as the frontend Web project

As this repository started receiving more attention and views, the project's direction was adjusted. While keeping the ability to integrate with the original stack, we are gradually refactoring this repository into a standalone, independently deployable AI agent service.

Highlights

• Switchable storage backend: sqlite or supabase
• Policy-Aware & Version-Aware RAG framework with parameterizable retrieval rules
• Automated PDF-to-Markdown conversion tool preserving table prose descriptions
• Directly deployable independently, without relying on health-diet-api
• Provides both HTTP API and terminal CLI
• Docker default is standalone SQLite mode
• Supports local knowledge base and uploaded document ingestion
• Retains Supabase integration, suitable for reconnecting to the original project

Project Structure

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Deployment Modes

1. Standalone SQLite Mode

Suitable for:

• Self-hosting locally
• Running directly with Docker
• Using terminal prompts
• Avoiding setting up Supabase upfront

Characteristics:

• SUPABASE_URL and SUPABASE_SERVICE_KEY are not required
• SQLite schema is automatically bootstrapped on startup
• Database path is configured via SQLITE_DB_PATH

2. Supabase Integration Mode

Suitable for:

• Having an existing Supabase schema
• Keeping the integration path with the original system
• Using this agent as a service inside the existing system

Characteristics:

• Preserves existing API routes
• Chat history, user profiles, and document metadata are stored in Supabase

Installation

Prerequisites

• Bun 1.2+
• Node-compatible environment for Bun
• Optional: Docker / Docker Compose
• Optional: Supabase project for integration mode
• Model endpoint compatible with the current agent configuration

Install

bun install

Create env file

cp .env.example .env

Environment Variables

Core runtime variables:

• PORT
• AI_API_URL
• STORAGE_BACKEND=sqlite|supabase
• SQLITE_DB_PATH
• CLI_USER_ID
• CLI_THREAD_ID

Supabase variables for integration mode:

• SUPABASE_URL
• SUPABASE_SERVICE_KEY

Google routing variables:

• GEMINI_AI_API
• GEMINI_API_KEY
• GOOGLE_CHAT_MODEL
• GOOGLE_BASE_URL

Project-level agent behavior now lives in:

• agent_config.json

Background sync variables:

• MOHW_NEWS_SYNC_ENABLED
• MOHW_NEWS_SYNC_INTERVAL_MINUTES
• MOHW_NEWS_SYNC_RUN_ON_START

Config precedence:

• agent_config.json provides the repository default behavior
• Environment variables override those defaults for a specific deployment
• MOHW_NEWS_SYNC_ENABLED overrides agent_config.json features.mohw_enabled when explicitly set

Standalone Local Usage

Recommended .env configuration:

PORT=8001
AI_API_URL=http://127.0.0.1:8080/v1/
STORAGE_BACKEND=sqlite
SQLITE_DB_PATH=./data/healthy-diet-agent.db
CLI_USER_ID=local-user
CLI_THREAD_ID=local-thread

Start the HTTP server:

bun run start

Default URLs:

• http://localhost:8001
• Chat endpoint: POST /api/chat
• Health check: GET /ping

Terminal CLI Usage

Send a prompt directly in the terminal:

bun run cli -- --message "Analyze my lunch"

You can also specify user, thread, and model source:

bun run cli -- --message "Give me a low sugar dinner idea" --user-id demo-user --thread-id demo-thread --model-source auto

Optional: Manual SQLite Initialization

In general, manual schema setup is not required because the app automatically bootstraps the SQLite schema upon startup.

If you want to manually create the database or pre-seed local testing data, you can use:

• Schema: docs/sqlite/schema.sql
• Sample Seed: docs/sqlite/seed.sample.sql

If you have sqlite3 installed in your environment:

sqlite3 ./data/healthy-diet-agent.db < docs/sqlite/schema.sql
sqlite3 ./data/healthy-diet-agent.db < docs/sqlite/seed.sample.sql

seed.sample.sql is only a local development example. You can modify the user, chatroom, and dialogue data inside it before importing.

Docker Deployment

The default Docker setup runs in standalone SQLite mode.

docker compose up --build

Default behavior:

• STORAGE_BACKEND=sqlite
• SQLITE_DB_PATH=/app/data/healthy-diet-agent.db
• Persists SQLite data via ./data:/app/data

Common mounts:

• ./data
• ./knowledge_base
• ./users_images

For automated production deployment using GitHub Actions, GHCR, and a self-hosted runner, see:

• docs/deployment-self-hosted-ghcr.md

Integration with Supabase or Existing Projects

To connect to an existing system, set:

STORAGE_BACKEND=supabase
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-role-key

Notes:

• Existing API routes are preserved
• Actual storage writes are routed through the shared storage layer
• Suitable for integration with the existing health-diet-api or other Supabase-based architectures

Forking and Customizing for Other Advisors

For common customization of agent roles and retrieval behaviors, you can do so without modifying the core runtime code.

Recommended customization steps:

1. Edit agent_config.json
2. Replace knowledge_base/AGENT.md
3. Replace or remove knowledge_base/NUTRITION_RULES.md
4. Enable or disable mohw_news in agent_config.json
5. Add your own custom knowledge files

agent_config.json currently controls:

• Agent prompt file locations
• Default response styles
• Enabled RAG sources
• RAG search parameters
• Default enablement of MOHW sync features

Config precedence:

• agent_config.json provides the repository default behavior
• Environment variables override those defaults for a specific deployment
• MOHW_NEWS_SYNC_ENABLED overrides agent_config.json features.mohw_enabled when explicitly set

API Overview

Chat

• POST /api/chat
• POST /api/approve
• POST /api/generate_title
• GET /ping

RAG and Knowledge

• GET /api/rag/search
• POST /api/rag/search
• GET /api/rag/documents
• POST /api/rag/documents
• GET /api/rag/documents/:document_id
• DELETE /api/rag/documents/:document_id
• POST /api/rag/documents/:document_id/reindex
• GET /api/rag/documents/:document_id/file
• GET /api/rag/documents/:document_id/preview
• GET /api/rag/sources/:document_id/file
• GET /api/rag/sources/:document_id/preview

Knowledge Ingestion

• POST /api/admin/knowledge/upload
• POST /api/admin/knowledge/ingest/:id
• GET /api/admin/knowledge/jobs/:jobId

Knowledge Graph

• POST /api/graph/extract-all
• GET /api/graph/status
• POST /api/graph/documents/:document_id/extract
• GET /api/graph/documents/:document_id
• POST /api/graph/search
• GET /api/graph/nodes
• GET /api/graph/nodes/:node_id
• GET /api/graph/relations/:relation_id/evidence

MOHW Sync

• POST /api/news/sync
• GET /api/news
• GET /api/news/:id
• GET /api/news-files

Local Data and Knowledge Paths

• SQLite file: data/healthy-diet-agent.db or SQLITE_DB_PATH
• User uploaded images: users_images/
• Uploaded source files: knowledge_base/uploads/
• Parsed markdown: knowledge_base/ingested_markdown/
• Nutrition rules: knowledge_base/NUTRITION_RULES.md
• MOHW data: knowledge_base/mohw_clarifications/

Testing

Run focused tests:

bun test src/server/httpRuntime.test.ts src/storage/runtime.test.ts src/server/serverHandlers.test.ts src/server/dbTools.test.ts src/server/ragDocuments.test.ts src/cli.test.ts

Run all tests:

bun test

Notes

• SQLite mode is the recommended default for self-hosting.
• Supabase mode remains supported for integration scenarios.
• Standalone mode does not require health-diet-api.
• Regardless of the mode, the app still expects a working model endpoint through AI_API_URL or the configured Google route.

Security and Failure Notes

• The RAG document management API now requires X-Admin-User-Id and X-Admin-Role (admin or nutritionist) headers.
• A bare Authorization header is no longer considered sufficient for administrator privileges.
• If /api/chat fails after creating the initial chat history row, the placeholder reply will be updated from __PENDING__ to a [FAILED] ... marker.

Related Docs

• Chinese README: README_zh.md
• Japanese README: README_jp.md
• Technical Docs Directory: technical_docs/
• Change Log: technical_docs/CHANGELOG.md
• Daily Planning Log: technical_docs/DAILY_PLANNING_LOG.md
• RAG Analysis Document (ZH): technical_docs/RAG_AGENT_ANALYSIS_ZH.md

Version-Aware RAG R2.19

The Development-only R2.19 experiment adds four checksum-verified WHO source documents and an offline MiniLM q8 candidate retriever. On outcome-exposed R2.16 data, the frozen BM25-MiniLM reciprocal-rank fusion reached required Recall@20 of 0.9615. This is diagnostic model selection only; a new lineage-disjoint, owner-approved confirmation is still required. See experiments/version_aware_rag/V5_R2_19_NEURAL_HYBRID_DIAGNOSTIC_RESULT.md.

Version-Aware RAG R2.20

R2.20 executed one new 32-record, lineage-disjoint Development confirmation for the R2.19-selected BM25-MiniLM RRF candidate generator and the R2.16 Top-6-anchored pair reranker. Candidate Recall@20 reached 0.9808, and all three strict improvements passed, but current-only Recall@3 decreased from 1.0000 to 0.8333. The gate therefore failed and is locked at o