#embeddings

6 resources and 3 guides tagged #embeddings on ChangeGamer.

RAG and Retrieval for Agents · Guide
End-to-end practitioner reference for Retrieval-Augmented Generation: pipeline stages, chunking strategies, dense/sparse/hybrid retrieval, reranking, agentic retrieval patterns, quality failure modes, and evaluation — with verified sources for every named technique.
Embeddings and Vector Search for Agents · Reference
How to pick an embedding model, understand distance metrics, choose an ANN index type, and operate a vector store reliably in agent retrieval pipelines.
Reranking for RAG: Cross-Encoders, LLM Rerankers, and Hosted APIs · Guide
Second-stage retrieval step that re-scores bi-encoder candidates with full query-document attention, boosting precision without sacrificing recall; covers cross-encoder, LLM, and late-interaction reranking, hosted APIs, sizing heuristics, and evaluation.
Chunking Strategies for RAG · Guide
Practitioner reference for chunking documents before embedding: fixed-size, recursive, semantic, late chunking, and contextual retrieval — with a strategy comparison table, chunk-size and overlap tradeoffs, code/table/Markdown handling, embedding model context limits, and evaluation methods.
Application-Level Response Caching for AI Agents · Guide
How to implement exact-match and semantic caching in your agent application to eliminate redundant LLM calls, with threshold guidance, invalidation strategies, and a decision matrix for when semantic caching is unsafe.
Hybrid Search for RAG: BM25 + Dense Retrieval and Fusion · Guide
How to combine lexical (BM25/SPLADE) and dense vector retrieval with Reciprocal Rank Fusion for higher first-stage recall in RAG pipelines — with the RRF formula, a sparse-method comparison table, and verified DB support.

Guides

Agentic RAG in Production: The Complete Operator Guide · pillar guide
The operator playbook for agentic RAG in production: ingestion, chunking, hybrid retrieval, reranking, evaluation, freshness and cost.
How to Choose an Embedding Model for RAG (and Version It Like a Schema) · guide
Embedding selection as an operations problem: the criteria that dominate total cost of ownership, the never-mix-spaces invariant, reindex migrations with dual indexes, and where quantization fits once cost shows up.
How to Combine Keyword and Vector Search in RAG · guide
Why hybrid retrieval is the production default rather than an upgrade: complementary failure modes of lexical and dense search, reciprocal rank fusion versus weighted scoring, parameter choices, and how filtering interacts with fusion.