Freelance RAG & Knowledge Graph Programmer: Production pgvector, FalkorDB & Hybrid Retrieval

Naive Retrieval-Augmented Generation (splitting PDFs into fixed 500-token chunks and running cosine similarity) fails in enterprise production when users ask multi-hop relational questions, search exact part numbers, or require strict tenant permission filtering.

As a Senior Freelance RAG & Knowledge Graph Programmer, I engineer hybrid retrieval architectures that combine dense vector search (PostgreSQL `pgvector`, SQLite WASM), lexical BM25 (Elasticsearch), and graph memory (FalkorDB). Hire Aldian Fazrihady on Upwork.

Deep-Dive RAG & Memory Architecture Articles

• The Dual-Memory Architecture: Orchestrating Short-Term Agent Context and Long-Term Knowledge Graphs
• Asynchronous Memory Curation: Decoupling LLM Fact-Extraction with NATS and FalkorDB
• Production RAG Architecture in 2026: Hybrid Search with pgvector, Knowledge Graphs & Reranking
• Offline RAG: Building a High-Performance SQLite Vector Index Inside a Web Worker