1 points | by sumoaps 2 hours ago
1 comments
Author here. This is a minimal (~1k LOC) Python implementation showing how to make retrieval strategy a learnable decision in RAG/agent systems.
Key ideas: - Router extracts query features (digit_ratio, rare_ratio, etc.) - Chooses between keyword, vector, or hybrid retrieval - Feedback loop updates weights based on evaluation scores - All runs logged to SQLite (or Databricks Lakebase)
The repo runs on CPU with no external API calls. Happy to answer questions about the design or production considerations.
Author here. This is a minimal (~1k LOC) Python implementation showing how to make retrieval strategy a learnable decision in RAG/agent systems.
Key ideas: - Router extracts query features (digit_ratio, rare_ratio, etc.) - Chooses between keyword, vector, or hybrid retrieval - Feedback loop updates weights based on evaluation scores - All runs logged to SQLite (or Databricks Lakebase)
The repo runs on CPU with no external API calls. Happy to answer questions about the design or production considerations.