{
  "phase_id": 1,
  "name": "RAG Heat System",
  "epic": "Epic 2",
  "story_points": 60,
  "priority": "P0",
  "dependencies": [
    0
  ],
  "version": "v1.0",
  "user_stories": [
    {
      "story_id": "2.1",
      "title": "Document Ingestion Pipeline",
      "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"
      ],
      "tasks": [
        "Create FastAPI endpoint for document upload",
        "Implement PDF parsing (pypdf, pdfplumber)",
        "Implement text chunking (LangChain RecursiveCharacterTextSplitter)",
        "Set up Weaviate or Pinecone",
        "Implement embedding generation (OpenAI text-embedding-ada-002)",
        "Store embeddings in vector DB",
        "Extract and store metadata (title, author, date, medical specialty)",
        "Create batch ingestion script",
        "Test with 100 sample documents",
        "Load 5000+ production documents"
      ],
      "story_points": 13,
      "priority": "P0"
    },
    {
      "story_id": "2.2",
      "title": "Medical NLP Entity Extraction",
      "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"
      ],
      "tasks": [
        "Set up spaCy with medical models (scispaCy)",
        "Implement NER for medical entities",
        "Create ontology linking service",
        "Implement entity disambiguation",
        "Create entity extraction API endpoint",
        "Test with medical documents",
        "Tune for accuracy (target >85%)"
      ],
      "story_points": 13,
      "priority": "P1"
    },
    {
      "story_id": "2.3",
      "title": "RAG Query Pipeline",
      "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)"
      ],
      "tasks": [
        "Create query API endpoint",
        "Implement vector search (top-k retrieval)",
        "Implement graph search (Cypher queries)",
        "Merge and rank results",
        "Implement prompt engineering for medical domain",
        "Generate answers with OpenAI/Claude",
        "Add source citations",
        "Implement caching (Redis) for common queries",
        "Performance testing and optimization"
      ],
      "story_points": 21,
      "priority": "P0"
    },
    {
      "story_id": "2.4",
      "title": "Knowledge Graph Explorer UI",
      "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)"
      ],
      "tasks": [
        "Set up React project structure",
        "Install D3.js or vis.js",
        "Create graph visualization component",
        "Fetch graph data from Neo4j API",
        "Implement interactive exploration (click, drag, zoom)",
        "Add search bar for entity lookup",
        "Create entity details panel",
        "Implement filters (entity type, relationship type)",
        "Add visual Cypher query builder (optional)"
      ],
      "story_points": 13,
      "priority": "P2"
    }
  ]
}