Turning Reddit noise into structured product signals is exactly the kind of pipeline problem where RAG architecture matters — naive semantic search on raw posts misses context that entity-aware retrieval catches.
Relevant: GraphRAG demo — hybrid BM25+RRF retrieval on unstructured text, NetworkX entity graph, Claude for extraction, pgvector for embeddings. Built specifically to handle noisy real-world text. GitHub: github.com/ChunkyTortoise/graphrag-demo. Also: DocExtract AI (production, live) — async document pipeline, pgvector + Claude + ARQ worker, 234 tests. docextract-api.onrender.com
Stack match: Python, PostgreSQL, pgvector, RAG, LLM APIs (Anthropic/OpenAI) — every item in your stack is something I ship with. Available within 1 week.
Turning Reddit noise into structured product signals is exactly the kind of pipeline problem where RAG architecture matters — naive semantic search on raw posts misses context that entity-aware retrieval catches.
Relevant: GraphRAG demo — hybrid BM25+RRF retrieval on unstructured text, NetworkX entity graph, Claude for extraction, pgvector for embeddings. Built specifically to handle noisy real-world text. GitHub: github.com/ChunkyTortoise/graphrag-demo. Also: DocExtract AI (production, live) — async document pipeline, pgvector + Claude + ARQ worker, 234 tests. docextract-api.onrender.com
Stack match: Python, PostgreSQL, pgvector, RAG, LLM APIs (Anthropic/OpenAI) — every item in your stack is something I ship with. Available within 1 week.
Cayman | caymanroden@gmail.com | github.com/ChunkyTortoise