The original title is: "Building a RAG System from Scratch with pgvector and Gemini — Implementation"

Short summary

Step-by-step RAG implementation using pgvector and Google Gemini with complete Python code examples. Covers database setup, HNSW index tuning (dev/production/accuracy parameter reference), asymmetric embedding task types (RETRIEVAL_DOCUMENT vs RETRIEVAL_QUERY), and vector similarity search. Includes architectural reasoning like 768-dimension embeddings optimized for pgvector's HNSW limits.

• •Complete RAG pipeline implementation with pgvector + Gemini, ready to run locally
• •HNSW index parameter tuning guide for different accuracy/performance trade-offs
• •Asymmetric embedding strategies (task_type) to improve retrieval accuracy

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