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│ ✻ Welcome to Claude Code!                                   │
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│   /help for help, /status for your current setup            │
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│   cwd: /home/rjm/projects/IP_assist_lite/src/orchestration  │
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  ⎿  Read ../../USER_GUIDE.md (442 lines)
  ⎿  Read ../../PROJECT_STATUS.md (386 lines)
  ⎿  Read ../../launch.sh (56 lines)
  ⎿  Read ../ui/gradio_app.py (373 lines)
  ⎿  Read ../api/fastapi_app.py (396 lines)
  ⎿  Todo list read (7 items)

✻ Thinking…

  Looking at the conversation summary, the user's last request was:

  "I want to think about it for a bit. In the meantime can you update the PROJECT_STATUS.md with 
  what we have accomplished so far and what we still need to do so I can pick back up later and 
  create full documentation and user guide."

  Based on the files I've read:
  1. PROJECT_STATUS.md - Shows the system is complete except for LLM integration
  2. USER_GUIDE.md - Comprehensive user documentation 
  3. launch.sh - Launcher script for the system
  4. gradio_app.py - UI interface
  5. fastapi_app.py - API endpoints

  The documentation task has been completed. Both PROJECT_STATUS.md and USER_GUIDE.md have been 
  created with comprehensive information about:
  - What's been accomplished (data pipeline, retrieval system, orchestration, API, UI)
  - Current status (functionally complete except LLM integration)
  - Next steps (LLM integration options)
  - Full user guide with installation, usage, API docs, troubleshooting

  The user explicitly said they want to think about the LLM choice and will "pick back up later". 
  The documentation was created so they could do exactly that.

  Since the documentation task is complete and the user wants time to think about LLM integration, I
   should confirm that the documentation has been created and summarize what's ready for them when 
  they return.

● I've completed the comprehensive documentation as requested. Both PROJECT_STATUS.md and
  USER_GUIDE.md have been created with full details of what we accomplished and what remains.

  ✅ What's Complete and Ready

  The system is functionally complete except for LLM integration:

  Core Components (100% Working):

  - Data Pipeline: 460 documents processed, 15,852 optimized chunks
  - MedCPT Embeddings: 768-dimensional vectors generated and indexed
  - Hybrid Retrieval: Semantic + BM25 + exact match with hierarchy-aware ranking
  - LangGraph Orchestration: Query routing, emergency detection, safety guards
  - FastAPI Backend: All endpoints operational at http://localhost:8000/docs
  - Gradio UI: Three-tab interface at http://localhost:7860
  - Infrastructure: Docker, Makefile, launch script all configured

  Key Achievements:

  - Recall@5 > 0.8 on test queries ✅
  - Emergency detection 100% accurate ✅
  - CPT exact match working ✅
  - Query latency < 500ms ✅
  - Hierarchy-aware ranking (A1-A4, H1-H4) operational ✅

  🚀 To Start the System Now

  # Everything is ready to run
  ./launch.sh

  # Access points:
  # - Gradio UI: http://localhost:7860
  # - API Docs: http://localhost:8000/docs

  The system currently returns structured retrieval results with metadata, citations, and safety
   flags - it just needs an LLM to synthesize these into natural language responses.

  📝 LLM Integration (When You're Ready)

  The documentation includes three integration options with code snippets:
  1. Local (Ollama) - Best for privacy
  2. API-based - OpenAI/Anthropic
  3. Medical-specific - HuggingFace models

  The integration point is clearly marked in langgraph_agent.py's _synthesize_response() method.

  All documentation is complete and the system is ready for you to pick up whenever you decide
  on the LLM strategy!