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Amirali/ocr - #2
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- Uses langgraph.prebuilt.create_react_agent (handles state + routing) - Tavily search via langchain-tavily (TavilySearch) - Qwen qwen3.7-plus model via ChatOpenAI (OpenAI-compatible) - Parses structured JSON output into AgentEvents - Tested on TK health insurance letter — correct classification and risk Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
OCR (5.8s) + ReAct Agent (69.7s) = 75.5s total Traces visible at https://eu.smith.langchain.com → project 'klar-hackathon' Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
- Use langchain.agents.create_agent (replaces deprecated create_react_agent) - 3-tier deadline extraction: letter → calculate → search as last resort - Max 2 searches per letter with focused search guidelines - Tavily max_results reduced to 3 Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
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