#rag

Notes on choosing between prompting, RAG, and fine-tuning for LLM applications, covering PEFT, RLHF, chunking, RAG vs CAG, vector databases, and Vietnamese embedding models.

Overview of text chunking approaches for RAG (fixed size, recursive, document-based, semantic, agentic), proposition-based retrieval, and the ChunkFormer speech model.

Explains how prompt engineering, RAG, and fine-tuning relate as ways to steer an LLM, with open-source RAG libraries and a comparison of RAG versus cache-augmented generation.

Step-by-step tutorial on building a RAG system on a Neo4j knowledge graph with LangChain, plus best practices and common pitfalls for GraphRAG.

Curated tools for analytics and tabular ML, document processing, and knowledge or data management.

Overview of vector and graph-vector databases, context stores, and RAG frameworks used for semantic retrieval in LLM applications.

An open-source Retrieval-Augmented Generation engine that combines deep document understanding, template-based chunking, grounded citations, and agent capabilities.

A self-hosted context development platform that stores, enriches, and retrieves structured knowledge through a multi-model database, graph-native RAG pipelines, and portable context cores.

XERJ (xerj-org/xerj, Apache-2.0, Rust) is a local-first search engine and MCP server for AI agents. One binary auto-indexes folders of code, docs, logs and PDFs into BM25, vector and hybrid search behind an Elasticsearch-compatible API, with an agent memory endpoint. Its headline claim is fewer output tokens for "reference coding". Release candidate (v1.0.0-rc.97), asks users for a field-report PR.