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Tagged: vector-search
A MongoDB-Backed Virtual Filesystem for LangChain Deep Agents
A MongoDB-backed virtual filesystem for LangChain Deep Agents: bytes in object storage, search in Atlas with hybrid grep, rank fusion, chunking and sync.
8 min readFrom Prototype to Production RAG: Hybrid Search, Agents and Evaluation
A reference architecture for production RAG in .NET: chunking, hybrid and graph retrieval, re-ranking, query planning, typed output and ongoing evaluation.
8 min readMetadata Filtering for Databricks RAG Using ai_extract and ai_prep_search
Combine ai_parse_document, ai_extract v2.1 and ai_prep_search so each chunk carries typed metadata, letting Vector Search filter before ranking by similarity.
7 min readA Practical RAG Architecture on Firebase and Vertex AI with Firestore Vectors
RAG on Firebase with Vertex AI embeddings and Firestore vector search: batching, metadata filters, tenant isolation, grounded prompts, caching and metrics.
10 min readWhy RAG Still Gives Wrong Answers: Hybrid Search, Reranking and Recall@K
Clean chunks don't guarantee good answers. See where vector retrieval fails and how fusion, a cross-encoder reranker, HyDE and Recall@K measurement fix it.
6 min readHybrid Image Search at Scale: SigLIP, SPLADE, YOLOv8 and Gemini Captions
Design of a million-image search system that pairs dense and sparse embeddings with object detection, entity recognition, safe search and LLM-written descriptions.
6 min readHow RAG Gives an LLM Your Private Data: Chunks, Vectors, Prompts
Understand retrieval-augmented generation from ingestion to query, with a small Node.js example, three common failure modes and a RAG versus fine-tuning table.
8 min readA Local RAG Pipeline with Qdrant, fastembed and Gemini in Python
A small RAG pipeline in Python: chunk local docs, embed them offline with fastembed, keep vectors in Qdrant, and answer questions with Gemini from the CLI.
6 min readTen Quiet Retrieval Bugs in Production RAG, and How Each One Gets Fixed
From chunk size and overlap to hybrid search, RRF, reranking, embedding choice and CI evals: ten retrieval mistakes behind wrong RAG answers and their fixes.
16 min read
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