TecWorms – Python, AI & Developer Tutorials

Production RAG Masterclass · Day 4 of 7 Some questions are not “find the nearest paragraph.” They are “walk a path across three documents and explain the join.” Flat vector search was never designed for that. Figure 1. Vectors find seeds. Graphs find relationships. Thesis Cosine similarity cannot infer multi-hop entity relationships across separate documents. Pair vectors with an entity–relation graph. Where flat vector RAG breaks Ask: “Which vendors touch the same data pipeline as Team Atlas, and who owns the SLA?” The facts live in an org chart, a vendor contract, and a runbook. No single chunk holds the full path. Similarity returns locally relevant paragraphs — not the chain. Build the graph at ingest Extract triples while you chunk: (Entity) -[RELATION]-> (Entity) (Team Atlas)-[OWNS]->(Billing Pipeline) (Billing Pipeline)-[DEPENDS_ON]->(Vendor Stripe) (Vendor Stripe)-[HAS_SLA]->(99.95%) Store nodes and edges in Neo4j (or similar). Keep chunk t...

Production RAG Masterclass · Day 3 of 7 Most RAG post-mortems blame the index. In practice, the query that hit the index was a vague chat message that never resembled the documents you stored. Figure 1. Fix the query shape before you retune the vector store. Thesis Raw user queries are short, vague, and poorly formatted for semantic matching. Optimize the query — then retrieve. The query–document mismatch User: “why is billing failing?” Doc: “Invoice reconciliation jobs abort when Stripe webhook signature validation returns HTTP 400…” Those strings barely share tokens. Embeddings help. A deliberate bridge helps more. Strip conversational noise first Before expansion, normalize the prompt: Drop politeness filler (“please”, “can you”, “thanks”). Drop chat history that is not about the current ask. Keep entities, IDs, product names, and constraints. def clean_query(q: str) -> str: return llm(f"Rewrite as a concise search query:\n{q}") ...

Production RAG Masterclass · Day 2 of 7 If your RAG system “almost” finds the right answer when users paste an error code, a part number, or an acronym — the problem is rarely the LLM. It is almost always retrieval that smoothed away the exact tokens that mattered. Figure 1. Sparse and dense retrieval merged before a final rerank pass. Thesis Vector distance is great at meaning and weak at literals. Hybrid search closes that gap; a cross-encoder decides what actually belongs in the prompt. Where dense-only retrieval quietly fails Embedding models shine on paraphrases. Production users do not. They paste strings the model was never trained to treat as sacred: ERR_TIMEOUT_5042 PN-88421-B Domain shorthand: SOX, MTTR, OIDC, SKU families Cosine similarity softens those tokens into “something nearby.” BM25 keeps them exact. You need both lists, then a principled way to merge them. Stage 1: run sparse and dense in parallel Dense path — embed the query, ne...

Part 1 of 7 — RAG Systems in Practice Stop Letting Naive Chunking Ruin Your RAG Pipelines When a RAG system answers a query with confidence and gets it totally wrong, the initial reaction is usually to blame the model's temperature or swap vector databases. Most of the time, the real bug happened hours earlier when you ingested the raw PDF. What actually breaks when you use fixed character splitting? Standard tutorials tell you to take your document and run a RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) . It works fine for demo apps, but here is what happens on enterprise data: Tables turn into nonsense: A 10-column financial or spec table gets chopped right in half. Row entries lose their column headers, turning structured data into random numbers. Headers detach from paragraphs: The section heading ### Rate Limits lands in Chunk A, while the actual rate l...