# SWARMCARE EXECUTION - Instance instance_01 - Phase 1

## AUTONOMOUS EXECUTION MODE
- TAKE FULL CONTROL: Do not ask for confirmation
- PRODUCTION-READY ONLY: Every output must be deployment-ready
- 100% SUCCESS RATE: Build comprehensive validation at every step
- PARALLEL EVERYTHING: Run all independent tasks simultaneously

## PHASE: RAG Heat System

### Phase Overview
- **Phase ID:** 1
- **Story Points:** 60
- **Priority:** P0
- **Dependencies:** 0

### User Stories to Implement

#### User Story 2.1: Document Ingestion Pipeline
**As a** System
**I want** to ingest medical documents and convert them to embeddings
**So that** I can perform semantic search

**Acceptance Criteria:**
- [ ] Document upload API endpoint (POST /api/v1/documents)
- [ ] PDF/TXT parsing implemented
- [ ] Text chunking strategy (500-1000 tokens per chunk)
- [ ] Embedding generation (OpenAI/Cohere)
- [ ] Vector database storage (Weaviate/Pinecone)
- [ ] Metadata extraction and storage
- [ ] 5000+ medical documents ingested

**Implementation Tasks:**
1. Create FastAPI endpoint for document upload
2. Implement PDF parsing (pypdf, pdfplumber)
3. Implement text chunking (LangChain RecursiveCharacterTextSplitter)
4. Set up Weaviate or Pinecone
5. Implement embedding generation (OpenAI text-embedding-ada-002)
6. Store embeddings in vector DB
7. Extract and store metadata (title, author, date, medical specialty)
8. Create batch ingestion script
9. Test with 100 sample documents
10. Load 5000+ production documents

**Story Points:** 13
**Priority:** P0


#### User Story 2.2: Medical NLP Entity Extraction
**As a** System
**I want** to extract medical entities from text
**So that** I can link them to ontologies

**Acceptance Criteria:**
- [ ] Named Entity Recognition (NER) implemented
- [ ] Medical entity types extracted (diseases, drugs, symptoms, procedures)
- [ ] Entity linking to ontologies (SNOMED-CT, RxNorm, ICD-10)
- [ ] Confidence scores for entity extraction
- [ ] API endpoint for entity extraction

**Implementation Tasks:**
1. Set up spaCy with medical models (scispaCy)
2. Implement NER for medical entities
3. Create ontology linking service
4. Implement entity disambiguation
5. Create entity extraction API endpoint
6. Test with medical documents
7. Tune for accuracy (target >85%)

**Story Points:** 13
**Priority:** P1


#### User Story 2.3: RAG Query Pipeline
**As a** User
**I want** to ask medical questions and get accurate answers
**So that** I can retrieve relevant medical knowledge

**Acceptance Criteria:**
- [ ] Query API endpoint (POST /api/v1/query)
- [ ] Hybrid search (vector + graph)
- [ ] Context retrieval and ranking
- [ ] LLM-based answer generation
- [ ] Source citation in responses
- [ ] Response time <3 seconds (p95)

**Implementation Tasks:**
1. Create query API endpoint
2. Implement vector search (top-k retrieval)
3. Implement graph search (Cypher queries)
4. Merge and rank results
5. Implement prompt engineering for medical domain
6. Generate answers with OpenAI/Claude
7. Add source citations
8. Implement caching (Redis) for common queries
9. Performance testing and optimization

**Story Points:** 21
**Priority:** P0


#### User Story 2.4: Knowledge Graph Explorer UI
**As a** User
**I want** a visual interface to explore the medical knowledge graph
**So that** I can understand relationships between medical concepts

**Acceptance Criteria:**
- [ ] React component for graph visualization
- [ ] D3.js or vis.js integration
- [ ] Interactive node/edge exploration
- [ ] Search and filter functionality
- [ ] Entity details panel
- [ ] Graph query builder (visual Cypher)

**Implementation Tasks:**
1. Set up React project structure
2. Install D3.js or vis.js
3. Create graph visualization component
4. Fetch graph data from Neo4j API
5. Implement interactive exploration (click, drag, zoom)
6. Add search bar for entity lookup
7. Create entity details panel
8. Implement filters (entity type, relationship type)
9. Add visual Cypher query builder (optional)

**Story Points:** 13
**Priority:** P2


### Success Criteria
- All user stories implemented
- All tests passing (>80% coverage)
- Code reviewed and optimized
- Documentation complete
- No critical security vulnerabilities

### Deliverables
- Production-ready code in Git
- Comprehensive test suite
- API documentation (if applicable)
- Integration tests passing

### Tracking
- Mark each user story complete in the phase state file
- Create checkpoint every 30 minutes
- Update progress percentage
- Log all significant events

### Next Steps After Completion
1. Run all tests
2. Generate documentation
3. Create checkpoint
4. Mark phase as COMPLETED
5. Notify integration coordinator

BEGIN EXECUTION NOW.
