RAGatouille

RAGatouille एक रिट्रीवल फ्रेमवर्क और सर्च इंजन है जिसे लेट-इंटरैक्शन रिट्रीवल मॉडल्स को लागू करने और ट्रेन करने के लिए डिज़ाइन किया गया है। यह जनरेटिव AI पाइपलाइन्स के लिए एक मॉड्यूलर रिट्रीवल कंपोनेंट के रूप में कार्य करता है, जो सर्च एक्यूरेसी में सुधार के लिए उच्च-प्रदर्शन डॉक्यूमेंट रैंकिंग पर केंद्रित है।

यह प्रोजेक्ट पेयर्स और ट्रिपलेट्स का उपयोग करके रिट्रीवल मॉडल्स को ट्रेन और फाइन-ट्यून करने के लिए एक टूलकिट प्रदान करता है, जिसमें डोमेन एडेप्टेशन के लिए स्वचालित हार्ड-नेगेटिव माइनिंग की सुविधा है। यह एक लेट-इंटरैक्शन तंत्र को लागू करता है जो कंप्रेस्ड एम्बेडिंग्स का उपयोग करके रिट्रीवल स्पीड को प्रिसिजन के साथ संतुलित करता है।

यह सिस्टम डॉक्यूमेंट इंडेक्सिंग और रिट्रीवल ऑपरेशन्स को कवर करता है, जो उपलब्ध सिस्टम मेमोरी से अधिक डेटासेट्स को संभालने के लिए डिस्क-आधारित वेक्टर स्टोरेज का उपयोग करता है। यह दानेदार (granular) सिमेंटिक जानकारी को संरक्षित करने के लिए टोकन-लेवल एम्बेडिंग्स को मैप करके रिट्रीवल ऑगमेंटेड जनरेशन वर्कफ़्लो के निर्माण का समर्थन करता है।

Features

Late Interaction Retrieval - Implements a late-interaction mechanism that stores multiple vectors per document for granular semantic matching.
Retrieval Model Fine-Tuning - Provides workflows for optimizing late-interaction retrieval models using query-passage pairs to improve search precision.
RAG Context Retrieval - Retrieves relevant document segments from a knowledge base to provide grounded context for language model responses.
Ranking Model Training - Trains retrieval models to order documents by relevance using a ranking objective based on positive and negative pairs.
Hard Negative Mining - Automatically identifies challenging dissimilar examples during training to refine embedding boundaries and improve search precision.
Semantic Word Embeddings - Maps every word in a document to a semantic vector to preserve granular information for late-interaction scoring.
Late Interaction Search Engines - Builds search systems that utilize multi-vector matching to balance retrieval speed and ranking precision.
NLP Model Trainers - Ships a toolkit for fine-tuning retrieval models using pairs, triplets, and automatic hard-negative mining.
Vector Embedding Indexing - Tokenizes and embeds documents into compressed bags of embeddings stored on disk for fast retrieval.
Vector Storage - Provides specialized storage engines that persist high-dimensional vectors to disk to handle datasets exceeding system memory.
Vector Indexing Pipelines - Processes and embeds large text collections into compressed formats for efficient similarity search.
Indexed Document Retrieval - Retrieves the most relevant documents from a vector index based on late-interaction scoring.
Vector Index Compression - Implements quantized vector storage to reduce memory footprint and latency during retrieval.

स्टार हिस्ट्री

AnswerDotAIRAGatouille

Name: answerdotai/ragatouille
Author: AnswerDotAI

View on GitHub

3,937 स्टार्स271 फोर्क्सPythonApache-2.04 व्यूज़

RAGatouille

Features

Late Interaction Retrieval - Implements a late-interaction mechanism that stores multiple vectors per document for granular semantic matching.
Retrieval Model Fine-Tuning - Provides workflows for optimizing late-interaction retrieval models using query-passage pairs to improve search precision.
RAG Context Retrieval - Retrieves relevant document segments from a knowledge base to provide grounded context for language model responses.
Ranking Model Training - Trains retrieval models to order documents by relevance using a ranking objective based on positive and negative pairs.
Hard Negative Mining - Automatically identifies challenging dissimilar examples during training to refine embedding boundaries and improve search precision.
Semantic Word Embeddings - Maps every word in a document to a semantic vector to preserve granular information for late-interaction scoring.
Late Interaction Search Engines - Builds search systems that utilize multi-vector matching to balance retrieval speed and ranking precision.
NLP Model Trainers - Ships a toolkit for fine-tuning retrieval models using pairs, triplets, and automatic hard-negative mining.
Vector Embedding Indexing - Tokenizes and embeds documents into compressed bags of embeddings stored on disk for fast retrieval.
Vector Storage - Provides specialized storage engines that persist high-dimensional vectors to disk to handle datasets exceeding system memory.
Vector Indexing Pipelines - Processes and embeds large text collections into compressed formats for efficient similarity search.
Indexed Document Retrieval - Retrieves the most relevant documents from a vector index based on late-interaction scoring.
Vector Index Compression - Implements quantized vector storage to reduce memory footprint and latency during retrieval.

RAGatouille के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो RAGatouille के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

stanford-futuredata/colbert
stanford-futuredata/ColBERT
3,779GitHub पर देखें
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
Python
GitHub पर देखें3,779
alibaba/zvec
alibaba/zvec
5,198GitHub पर देखें
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
C++ann-searchembedded-databaserag
GitHub पर देखें5,198
anthropics/anthropic-cookbook
anthropics/anthropic-cookbook
45,984GitHub पर देखें
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
Jupyter Notebook
GitHub पर देखें45,984

अक्सर पूछे जाने वाले प्रश्न

answerdotai/ragatouille क्या करता है?

answerdotai/ragatouille की मुख्य विशेषताएं क्या हैं?

answerdotai/ragatouille की मुख्य विशेषताएं हैं: 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।

answerdotai/ragatouille के कुछ ओपन-सोर्स विकल्प क्या हैं?

answerdotai/ragatouille के ओपन-सोर्स विकल्पों में शामिल हैं: 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,…

स्टार हिस्ट्री

अक्सर पूछे जाने वाले प्रश्न

answerdotai/ragatouille क्या करता है?

answerdotai/ragatouille की मुख्य विशेषताएं क्या हैं?

answerdotai/ragatouille के कुछ ओपन-सोर्स विकल्प क्या हैं?

RAGatouille

Features

स्टार हिस्ट्री

RAGatouille

Features

RAGatouille के ओपन-सोर्स विकल्प

stanford-futuredata/ColBERT

alibaba/zvec

anthropics/anthropic-cookbook

अक्सर पूछे जाने वाले प्रश्न

स्टार हिस्ट्री

अक्सर पूछे जाने वाले प्रश्न

RAGatouille के ओपन-सोर्स विकल्प

stanford-futuredata/ColBERT

alibaba/zvec

anthropics/anthropic-cookbook

brianpetro/obsidian-smart-connections