feat: Semantic product search with LLM evaluation tests #64
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- Implement semantic product search using Weaviate text_vector - Add LLM-based search query extraction from conversation context - Create LLM-as-judge evaluation framework for e2e testing - Add translations for product search responses (en/pl) Product Search: - searchProductIdsInWeaviate for semantic search via nearText - productsNode extracts query from full conversation, not just last message - Returns formatted product list with prices, categories, stock status Evaluation Framework: - evaluator.ts: LLM-as-judge using Bielik model (score 1-5) - conversationRunner.ts: Execute multi-turn conversations - productSearch.e2e.test.ts: 11 scenarios (single-turn, multi-turn, edge cases) - Separate vitest config for e2e tests (npm run test:eval) Generated with [Claude Code](https://claude.ai/code) via [Happy](https://happy.engineering) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Happy <yesreply@happy.engineering>
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Summary
text_vector)Changes
Product Search
searchProductIdsInWeaviatefor semantic search vianearTextontext_vectorproductsNodeextracts search query from entire conversation (not just last message)getAgentTranslationshelper for server-side translationsEvaluation Framework
evaluator.ts: LLM-as-judge using Bielik model (scores 1-5 with criteria)conversationRunner.ts: Execute multi-turn conversations through chatGraphproductSearch.e2e.test.ts: 11 test scenariosnpm run test:eval)Test plan
npm test- unit tests pass (94 tests)npm run test:eval- e2e evaluation tests execute (~16-18/21 pass, some variance due to LLM non-determinism)🤖 Generated with Claude Code