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RAGatouille is a retrieval framework and search engine designed for implementing and training late-interaction retrieval models. It serves as a modular retrieval component for generative AI pipelines, focusing on high-performance document ranking to improve search accuracy.
The main features of answerdotai/ragatouille are: Late Interaction Retrieval, Retrieval Model Fine-Tuning, RAG Context Retrieval, Ranking Model Training, Hard Negative Mining, Semantic Word Embeddings, Late Interaction Search Engines, NLP Model Trainers.
Open-source alternatives to answerdotai/ragatouille include: stanford-futuredata/colbert — ColBERT is a neural information retrieval model and dense passage retrieval framework. It functions as a search engine… alibaba/zvec — zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It… crmne/ruby_llm — ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large… brianpetro/obsidian-smart-connections — This project is a knowledge base plugin and RAG context manager that uses a local vector database interface to enable… anthropics/anthropic-cookbook — This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,…
ColBERT is a neural information retrieval model and dense passage retrieval framework. It functions as a search engine that uses contextual embeddings to index text passages and retrieve relevant documents based on semantic meaning rather than keyword matching. The system is distinguished by a late interaction architecture that defers the calculation of query and document similarity until the final step. It employs multi-vector indexing to store separate embeddings for every token in a document, enabling granular matching against query terms. The project covers document indexing, passage ret
zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ
This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce
This project is a knowledge base plugin and RAG context manager that uses a local vector database interface to enable semantic search and relationship mapping. It transforms text into numerical vectors to find semantically related notes and excerpts based on conceptual meaning rather than keyword matches. The system differentiates itself through a semantic graph visualizer that maps notes into clusters to reveal conceptual connections. It also features a context manager capable of bundling local notes and excerpts into reusable packs to provide grounded factual bases for large language model