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ChimeraDB – Vector search, graph queries and SQL

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ChimeraDB – Vector search, graph queries and SQL

I built ChimeraDB to stop juggling three separate databases when building LLM apps. It combines vector embeddings, property graphs, and SQL analytics in a single DuckDB file. from chimeradb import KnowledgeGraph kg = KnowledgeGraph("my.db") # Semantic search - find by meaning results = kg.search("who works on language models?") # Graph traversal - follow relationships employees = kg.traverse("acme", direction="incoming") # SQL analytics - aggregate data stats = kg.query("SELECT company, COUNT(*) FROM nodes...") Why it's useful: - RAG needs semantic search + relationship context - AI agents need graph traversal + analytical queries - Combine all three in a single SQL query Zero infrastructure: One file, runs anywhere, 10-100x faster than SQLite for analytics. Built on DuckDB + duckpgq + vss extensions. MIT licensed. pip install chimeradb GitHub: https://github.com/codimusmaximus/chimeradb

Visit productView on Hacker NewsNov 25, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, para · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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