All Documents
174 documents available
🤖 Agentic Finance Director — Agent Inventory (Batch 3: AGT-101 → AGT-150)
Catalogues 50 finance AI agents across 5 modules with purpose, triggers, tools, KPIs, and error handling for each.
AIP-C01 Exam Concepts: Ranked by Importance
Ranks AIP-C01 exam concepts by importance using official domain weightings and cross-domain relevance.
SUMMARY
Proposes three on-prem AI architectures, modular, hybrid, and fully local RAG, with hardware specs and vendor lists.
Glossar
Defines retrieval, chunking, embedding, search, reranking, synthesis, and evaluation concepts for RAG experiments.
Metrics
Documents evaluation metrics for RAG systems, covering retrieval and generation quality, plus guidance on building golden datasets.
RAG Deep Dive Part 7: Evaluation and Debugging RAG Systems
Teaches you to measure and debug a RAG pipeline with retrieval metrics, generation metrics, and production monitoring, all implemented from scratch in Python.
Project Memory
Documents a production-ready RAG system for Paris cultural events with hybrid search, LLM generation, and Streamlit UI.
RAG Evaluation
Explains how to measure RAG system quality using four RAGAS metrics, faithfulness, answer relevancy, context precision, and context recall.
Code Span Semantic Chunking Executive Summary
Recommends an AST-driven chunking strategy for code retrieval, preserving function/class boundaries to improve precision and reduce token waste.
Split
Documents 15+ text chunking methods for RAG pipelines, including recursive, semantic, entity-aware, and graph-based splitting.
embedding-first-chunking-second-smarter-rag-retrieval-with-max-min-semantic-chunking
Explains Max-Min Semantic Chunking, an embed-first chunk-second strategy that uses semantic similarity to set chunk boundaries for better RAG retrieval.
Text Splitting with LangChain
Explains LangChain's RecursiveCharacterTextSplitter for dividing documents into chunks that fit within LLM context windows.
Chunker Server
Provides an MCP server with five text chunking strategies and a text analysis tool for recommending the best strategy.
语义块切分 Semantic Chunking
Implements semantic chunking for RAG by splitting text at points where sentence similarity drops below a threshold.
Chunking Fundamentals
Teaches chunking strategies for RAG pipelines, covering fixed-size, recursive, and semantic splitting with LangChain code examples.
Chunking Strategies
Explains the trade-off between chunk size for entity extraction and retrieval precision, with code examples for Neo4j's FixedSizeSplitter.
Semantic Chunking
Defines a quote-aware chunking and indexing strategy for semantic search over mailing-list corpora, with block parsing rules and retrieval flow.
How-to: Customize Chunking Strategies
Shows how to choose and configure three chunking strategies for a RAG pipeline, with CLI and Python examples.
语义块切分 Semantic Chunking
Implements three semantic chunking methods (percentile, standard deviation, IQR) for RAG pipelines, splitting text by sentence similarity.
rag-vector-chunking
Explains embedding-based retrieval, cosine similarity, RAG evaluation metrics, chunking strategies, and retrieval optimization techniques.
Text Chunking - Anton (ChromaDB)
Summarises a technical session on text chunking strategies for RAG, covering heuristic and semantic approaches, evaluation metrics, and practical advice.
Text Chunking - Anton (ChromaDB)
Summarises a talk on text chunking for RAG, covering heuristic vs semantic approaches, evaluation metrics, and practical advice.
ADR-0006: Paragraph-Boundary Chunking
Splits documents into chunks at paragraph boundaries for embedding and retrieval, with configurable token limits and overlap.
With Jina Embeddings v3
Explains late chunking, a technique that embeds full documents before splitting into chunks to preserve context, improving retrieval recall by 3-10%.