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3 commits

Author SHA1 Message Date
michael
7d0d72b974 Add documentation for post-deploy background init fixes 
Documents the two bugs found after initial deploy (double startup hook
execution and blocking event loop) with root cause analysis and fixes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-23 17:52:18 -06:00
michael
fe0d881341 Speed up GraphRAG startup with triple caching and background init 
Server now starts serving vector-only queries in ~1-2 minutes instead of
30-60 minutes. GraphRAG initializes in a background task and its tool is
dynamically added to the agent when ready.

- Cache extracted triples to disk (neo4j_triples.pickle) so Neo4j can be
  repopulated without expensive LLM re-extraction
- Split initialize_global_index() into initialize_vector_index() (fast) and
  initialize_graphrag_components() (background)
- Add graphrag_ready/graphrag_initializing status flags to shared_state
- Launch GraphRAG init as asyncio background task in main.py
- Report GraphRAG status in /status endpoint for frontend awareness
- Add comprehensive migration guide for applying to other projects

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-23 17:33:19 -06:00
michael
236d1ddbd8 Initial commit: Netflix GraphRAG marketing chatbot 
Full-stack application combining LlamaIndex vector search with Neo4j
knowledge graph (GraphRAG) for answering queries about Netflix marketing
materials. Flask/Hypercorn backend with custom ReAct agent, React frontend.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-23 10:28:33 -06:00