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Tagged: ChromaDB
Auditing RAG Retrieval for Stale Chunks, Drift and Noisy Context
A Python audit for RAG retrieval: golden queries, cosine relevance gates, Kolmogorov-Smirnov drift checks and context efficiency, plus how to read the report.
12 min readMulti-Agent K-Drama Recommender: BERTopic, RAG and Reciprocal Rank Fusion
A taste-aware K-drama recommender: BERTopic trope tags, ChromaDB RAG, an interview agent, query expansion and Reciprocal Rank Fusion ahead of an LLM re-rank.
10 min readFrom Naive to Production RAG on SEC 10-K Filings: Chunking, Hybrid Search
Why a textbook RAG pipeline failed on 300-page 10-Ks, and the fixes: section-aware chunks, hybrid retrieval, cross-encoder reranking, validation and metadata citations.
8 min readLayer by Layer: Designing a Real RAG Retrieval Pipeline for AI Agents
Plan a RAG pipeline with SourceDocument and DocumentChunk contracts, chunking, embeddings and ChromaDB, all behind a RetrievalService that keeps the agent stable.
10 min readA Docs Assistant in Two Python Files: ChromaDB, LangChain and Claude
Turn a 187-page dbt standards PDF into a searchable assistant with an ingest script, local Hugging Face embeddings, ChromaDB and Claude answers grounded in the docs.
7 min readPrivate Multimodal RAG on an M1 Mac: Selective Chart Reading With Ollama
Private multimodal RAG on an M1 Mac with Ollama: PyMuPDF for text and tables, a small Qwen vision model only for charts, number checks and ChromaDB retrieval.
10 min readA Fleet Repair Chatbot as a Plain-Language RAG Example With Chroma and Ollama
RAG explained with a fleet repair chatbot: car repair reports arrive over WhatsApp, and Chroma, MiniLM embeddings and Llama 3.2 on Ollama do the first pass.
7 min readSemantic Retrieval with ChromaDB and Embeddings for a Multi-Agent Platform
Moving an enterprise multi-agent platform to real semantic retrieval: word-aware chunking, cosine distance in ChromaDB, test layers and the next RetrievalAgent step.
10 min readWhat Broke in a Corrective RAG Pipeline for YouTube Q&A With LangGraph
Chunking, embedding limits, LangGraph state, async pitfalls and transcription speed: the mistakes found while building a YouTube Q&A bot and how each was fixed.
7 min read
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