4 repositorios
Awesome GitHub RepositoriesEmbedding Optimizations
Techniques for applying and tuning embedding models to enhance the accuracy and relevance of semantic search results.
Distinct from Semantic Search: Focuses specifically on the optimization of embedding models for search relevance, rather than the general semantic search methodology.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Embedding Optimizations. Refine with filters or upvote what's useful.
Awesome Embedding Optimizations GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- google-gemini/cookbook
google-gemini/cookbook
17,418Ver en GitHubThe Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides designed for building applications with Google Gemini models. It serves as a central resource for developers to integrate multimodal generative artificial intelligence into their software, providing the necessary frameworks to manage model interactions, stateful workflows, and structured data extraction. The repository distinguishes itself by offering specialized toolkits for autonomous agent orchestration, enabling the construction of agents that can execute code, browse the web
Adjusts embedding dimensions to balance storage efficiency and computational performance.
Jupyter Notebookgeminigemini-api - opensearch-project/opensearch
opensearch-project/OpenSearch
13,196Ver en GitHubOpenSearch is a distributed search and analytics engine designed for indexing, searching, and analyzing massive volumes of structured and unstructured data in real time. It functions as a comprehensive platform that integrates enterprise-grade search capabilities, a vector database for high-dimensional similarity lookups, and a unified observability suite for monitoring logs, metrics, and traces across complex distributed environments. The platform distinguishes itself through its support for agentic workflow automation, allowing users to orchestrate multi-agent tasks and integrate foundation
Improves search relevance by applying asymmetric embedding models to natural language queries.
Javaanalyticsapache2foss - sentient-agi/opendeepsearch
sentient-agi/OpenDeepSearch
3,829Ver en GitHubOpenDeepSearch is an autonomous research platform and search orchestration engine that connects large language models with live web data. It functions as an agentic information retrieval tool designed to automate the gathering, structuring, and synthesis of web information to resolve complex queries. The system differentiates itself through iterative multi-hop querying, which allows the agent to execute sequences of dependent searches where previous results refine subsequent requests. It features a semantic reranking pipeline that uses embedding models to prioritize the most relevant content
Tunes and applies embedding models to ensure retrieved web content is prioritized by semantic relevance.
Python - 1517005260/graph-rag-agent
1517005260/graph-rag-agent
2,240Ver en GitHubThis project is a comprehensive framework for constructing, managing, and evaluating knowledge graphs through multi-agent reasoning and deep search capabilities. It provides an end-to-end pipeline that ingests multi-format documents, extracts entities and relationships based on configurable schemas, and maintains structured knowledge bases to support evidence-based retrieval. The system distinguishes itself through its multi-agent orchestration, which decomposes complex queries into parallel research steps and synthesizes long-form reports. It leverages advanced graph-based techniques, includ
Adjusts vector representations by training on domain-specific collections to improve semantic search relevance.
Pythonagentic-ragchain-of-explorationdeepresearch
