---
title: AI Integration
created: 2026-04-08
updated: 2026-04-08
status: speculative
tags: [concept, ai, rag, langgraph, embeddings]
sources: [ai-service/app/main.py, ai-service/requirements.txt, docker-compose.yml]
---

# AI Integration

## Current State

The AI service is a **Python FastAPI stub** with placeholder endpoints. No actual AI processing is wired up yet.

### Implemented (stub only)

| Endpoint | Method | Status |
|---|---|---|
| `GET /health` | Health check | Working |
| `POST /api/chat` | Chat with context | Stub — returns placeholder text |
| `POST /api/summarize` | Generate summaries | Stub — returns placeholder text |
| `POST /api/search` | Semantic search | Stub — returns empty results |

### Request/Response Models (Pydantic)

```
ChatRequest:     message, project_id?, conversation_history[]
ChatResponse:    reply, project_id?
SummarizeRequest: content, project_id?, summary_type?
SummarizeResponse: summary, project_id?
SearchRequest:   query, project_id?, limit?
SearchResponse:  results[{id, content, score, metadata}], query
```

## Planned Architecture

```
Laravel App ↔ HTTP ↔ Python AI-Service (FastAPI)
                        ├── LangGraph Orchestrator
                        │     ├── Router / Classifier
                        │     └── Agent graph (state machine)
                        ├── Anthropic Claude (LLM)
                        ├── pgvector (embeddings / similarity search)
                        └── Tools:
                              ├── DB query (project data, commitments, phases)
                              ├── Document retrieval (semantic search)
                              └── Embedding generation
```

## RAG Pipeline (planned)

### Sources
- Project descriptions and phase notes
- Documents (uploaded files, meeting notes)
- Lessons learned
- Decisions and their rationale
- Knowledge articles

### Embedding Strategy
- **Storage**: pgvector extension on PostgreSQL 16
- **Models**: Document and KennisArtikel already have `embedding` vector columns
- **Update triggers**: On document create/update, on project phase change
- **Chunking**: Per document type and size

### Agent Skills (from CLAUDE.md)
| Agent | Autonomy | Purpose |
|---|---|---|
| Project Assistant | Low | Answer questions about specific projects |
| Knowledge Assistant | Low | Search and surface knowledge articles |
| Document Assistant | Medium | Summarize, compare, extract from documents |
| System Tasks | High | Background indexing, embedding updates |

## Content Governance Rules

1. AI-generated content always labeled ("AI-suggestie", "Concept")
2. Human confirmation required before AI content gains system status
3. All AI interactions logged (request, response, tools used, sources cited)
4. Source attribution mandatory in AI responses
5. Confidence indicators when certainty is low