# SWARMCARE V2.1 EXECUTION - INSTANCE_05 - PHASE 1

**Version:** 2.1 Ultimate (120/120 Perfect Score)
**Instance:** instance_05
**Phase:** 1 - RAG Heat System
**Epic:** Epic 2
**Story Points:** 60
**Priority:** P0

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## 🎯 AUTONOMOUS EXECUTION MODE

### CRITICAL INSTRUCTIONS - READ FIRST

**TAKE FULL CONTROL:**
- Do NOT ask for confirmation
- Do NOT wait for user input
- Execute ALL tasks autonomously
- Make ALL technical decisions

**PRODUCTION-READY ONLY:**
- Every output must be deployment-ready
- NO prototypes, NO placeholders
- 100% complete, tested, documented code

**100% SUCCESS RATE:**
- Build comprehensive validation at every step
- Automated testing catches issues in seconds
- Fix ALL issues before marking complete

**PARALLEL EVERYTHING:**
- Run all independent tasks simultaneously
- Use maximum system resources
- Optimize for speed

---

## 📋 PHASE OVERVIEW

**Phase Name:** RAG Heat System
**Version:** v1.0
**Dependencies:** Phases [0]

**User Stories:** 4
**Total Story Points:** 60

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## 📝 USER STORIES TO IMPLEMENT

### Story 2.1: Document Ingestion Pipeline

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

**Description:**
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

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### Story 2.2: Medical NLP Entity Extraction

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

**Description:**
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 2.3: RAG Query Pipeline

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

**Description:**
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 2.4: Knowledge Graph Explorer UI

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

**Description:**
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)

---

## 🚀 EXECUTION INSTRUCTIONS

### Step-by-Step Process

1. **Setup Phase Environment**
   - Navigate to: `/home/user01/claude-test/SwarmCare`
   - Ensure all dependencies installed
   - Initialize phase state tracking

2. **Execute Each User Story Sequentially**
   - Implement ALL acceptance criteria
   - Complete ALL tasks
   - Write comprehensive tests
   - Document all code

3. **Validation & Testing**
   - Run ALL unit tests
   - Run ALL integration tests
   - Achieve >80% code coverage
   - Fix ALL failing tests

4. **Code Review & Quality**
   - Self-review all code
   - Fix ALL code quality issues
   - Ensure production-ready quality
   - Document complex logic

5. **Checkpoint & Report**
   - Save all progress
   - Update phase state
   - Generate completion report

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## ✅ COMPLETION CRITERIA

**Phase is COMPLETE when:**
- [ ] ALL user stories implemented (100%)
- [ ] ALL acceptance criteria met
- [ ] ALL tests passing (>80% coverage)
- [ ] ALL code reviewed and production-ready
- [ ] ZERO critical vulnerabilities
- [ ] Complete documentation

**Total Story Points to Complete:** 60

---

## 🔧 WSL 2 Ubuntu VM SETUP

**Environment:**
- OS: Windows 11 Pro + WSL 2 + Ubuntu VM
- Claude Code: Running on Ubuntu VM
- Code Transfer: Generated code → Visual Studio 2022 Enterprise (Windows)

**Execution Context:**
- All code generation happens on Ubuntu VM
- Multiple Claude Code instances run in parallel
- State is persisted in `.phase_state/` directory
- Code will be transferred to Windows for final execution

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## ⚡ BEGIN EXECUTION NOW

Start implementing Phase 1 immediately. Work through each user story systematically.
Execute all tasks autonomously without waiting for confirmation.

**Good luck! Build with excellence! 🚀**
