Embeddings & retrieval

Embeddings & retrieval

Engineering note · Part 5

Using OpenAI Embeddings to Find Related Evidence in MailTrace

Where text embeddings fit in MailTrace, how source chunks are indexed, and what similarity does not prove.

Embeddings & retrieval

Engineering note · Part 6

Why Embedding Dimensions and Model Versions Matter in Production

How explicit embedding contracts prevent incompatible indexes and make migrations reviewable.

Embeddings & retrieval

Engineering note · Part 7

Hybrid Retrieval in MailTrace: Combining Semantic Search with Keyword Matching

Why evidence search needs both paraphrase matching and exact terms, with a small rank-fusion example.

Embeddings & retrieval

Engineering note · Part 11

Building Evidence-Grounded Q&A with RAG and the Responses API

How scoped retrieval, structured answer proposals and saved artifacts fit together in MailTrace Q&A.

Embeddings & retrieval

Engineering note · Part 12

A Citation Is More Than a Link: Validating AI Answers Against Source Evidence

Validate source scope, freshness and exact quotations before treating an answer as evidence-grounded.

Embeddings & retrieval

Experiment design · Part 23

Reranking Retrieved Evidence Before Asking the LLM

A proposed second-stage relevance check for MailTrace retrieval, with clear limits on cost and authority.

RAG & Search

RAG for Evidence-Heavy Businesses: Source-Linked Retrieval That People Can Trust

How to design RAG and semantic retrieval for evidence, compliance and document-heavy workflows using source boundaries, pgvector and auditability.

Vector Search

pgvector Semantic Search: Production Lessons from Evidence Retrieval

Practical guidance on using PostgreSQL and pgvector for semantic search, with metadata filters, ownership boundaries and retrieval evaluation.

Search Engineering

Hybrid Search: When Vector Similarity Is Not Enough

Why production search often combines semantic retrieval, lexical matching and metadata filters.

RAG & Search

Reranking for RAG: Improving Relevance After Retrieval

How reranking can improve retrieval quality without replacing your vector database.

RAG Fundamentals

RAG Decision Support Explained: Retrieval, FAISS and Grounded Generation

A clear explanation of how decision-support logic fits into RAG, what FAISS does, and why RAG can provide better domain-specific context than a standalone LLM.

GraphRAG & Knowledge Systems

RAG + Knowledge Graphs: How Graph-Aware Retrieval Improves Context Relevance

How combining Retrieval-Augmented Generation with knowledge graphs improves entity linking, relationship-aware retrieval, multi-hop context and grounded answers.

Agentic RAG

Agentic RAG: Multi-Step Reasoning with Retrieval and Generation

How agentic RAG extends standard retrieval-augmented generation with planning, iterative retrieval, tool use, validation and multi-step reasoning.