Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).
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Updated
Sep 29, 2024 - Python
Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).
Contextual RAG over webinar videos using Pinecone, Claude and AWS.
RAG-Ingest: A tool for converting PDFs to markdown and indexing them for enhanced Retrieval Augmented Generation (RAG) capabilities.
It is a case study of an intelligent agent for Ocean.
Enhance your RAG with Contextual Retrieval
Production-grade multi-agent RAG system with a self-correcting LangGraph supervisor (Researcher, Synthesizer, Critic), agentic tool calling, hybrid search, and an MCP server. Built with Gemini 2.5 Flash, FastAPI, Next.js 16, Postgres, pgvector, Redis, and Celery.
PDF → Mistral OCR → deterministic AST chunker with Anthropic contextual retrieval for RAG pipelines
基于上下文增强(Contextual Retrieval)的 RAG 检索系统。
Shared knowledge layer for human-agent teams. Agents search, classify, and manage knowledge alongside your team.
ContextualRetriever enhances document retrieval accuracy by leveraging Voyage AI models for embedding & reranking models, and the GEMINI model for context and retrieval generation.
Chatbot based on Contextual RAG with Hybrid Search and Reranking with short conversation history awareness, fully OpenSource.
Production-shaped RAG for PDF corpora: contextual retrieval, hybrid dense+sparse search in Qdrant, RRF fusion, reranking, and streaming answers with clickable PDF citations.
Search local documents to provide private knowledge retrieval for AI agents and teams in under a second.
Rigorous evaluation of contextual retrieval techniques on FinanceBench: comparing 5 embedders × 4 chunking strategies with bootstrapped confidence intervals on FinMTEB and FinanceBench.
Anthropic's Contextual Retrieval (2024) implemented and measured: LLM-situated chunks vs plain chunks, contextual embeddings + contextual BM25, reproducible recall@5 benchmark on an ambiguity-engineered corpus. Free local Ollama.
Seven RAG approaches compared side-by-side as OpenAI-compatible endpoints (vanilla, hybrid, contextual, LightRAG graph, agentic, n8n-adaptive, experimental lazy-graph) with reproducible Ragas + judge-panel evaluation — plus a reference for consuming the Atlas platform as vendored infrastructure via a plugin seam + consumer manifest, no fork.
Production-grade agentic Retrieval-as-a-Service (RaaS) microservice built with LangGraph, FastAPI & LangChain. Features Corrective RAG (CRAG) with Pydantic LLM-as-a-Judge, Contextual Retrieval, Single-Database Parent Payloads, Groundedness Self-Correction, Vision OCR, and real-time SSE streaming.
Production-grade RAG over Chip Huyen's "AI Engineering" book and blog. Every answer cites its exact source passage and is re-checked by a local NLI model — when evidence is missing, the system declines to answer. Hybrid retrieval (BM25 + embeddings + RRF), reranking, contextual retrieval, and a CI gate that re-checks quality on every push.
🎬 EchoVid: AI-driven text/audio to video generation pipeline using Stable Diffusion.
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