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6 रिपॉजिटरी

Awesome GitHub RepositoriesSearch-Enhanced Generation

Language model response generation augmented with real-time external search data.

Distinct from Language Model Response Generators: Specifically targets the combination of RAG-like search and conversational response generation, distinct from general response generators.

Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Search-Enhanced Generation. Refine with filters or upvote what's useful.

Awesome Search-Enhanced Generation GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • stangirard/quiverStanGirard का अवतार

    StanGirard/quiver

    39,167GitHub पर देखें↗

    Quiver is a framework for integrating retrieval augmented generation into applications. It provides a generative AI integration layer that connects large language models with vector stores to produce context-aware responses based on custom data. The project features a knowledge base pipeline that parses diverse file types into searchable embeddings and a vector database orchestrator to manage data across different storage implementations. It utilizes a provider-agnostic model interface, allowing users to switch between various external AI providers or local models through a single unified sys

    Enhances response accuracy by combining semantic vector search with real-time internet search functionality.

    Python
    GitHub पर देखें↗39,167
  • dzhng/deep-researchdzhng का अवतार

    dzhng/deep-research

    19,136GitHub पर देखें↗

    This project is an AI research tool designed for autonomous web information gathering and automated topic research. It utilizes agent orchestration to combine search engines and web scraping, enabling the system to discover detailed information and build a comprehensive understanding of complex subjects without manual step-by-step guidance. The tool employs an iterative research execution model that recursively generates targeted search queries and refines directions based on previous results. It includes a feedback loop that compares current findings against initial objectives to identify kn

    Uses LLMs to dynamically generate precise search queries based on research objectives and gathered findings.

    TypeScriptagentaigpt
    GitHub पर देखें↗19,136
  • semi-technologies/weaviatesemi-technologies का अवतार

    semi-technologies/weaviate

    16,337GitHub पर देखें↗

    Weaviate is a cloud-native vector database and distributed vector store designed to save high-dimensional vectors alongside structured data. It functions as a hybrid search engine that combines vector similarity, keyword matching, and structured metadata filtering within a single query. The system is optimized for retrieval-augmented generation, integrating vector search with generative AI and reranking to power question-and-answer workflows. It distinguishes itself through the ability to merge semantic search with traditional keyword queries and structured metadata filters to improve result

    Integrates retrieval-augmented generation and reranking to power generative question-and-answer workflows directly within the database.

    Go
    GitHub पर देखें↗16,337
  • pipecat-ai/pipecatpipecat-ai का अवतार

    pipecat-ai/pipecat

    12,846GitHub पर देखें↗

    Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

    Integrates language models with internet search to provide up-to-date information in real-time conversations.

    Pythonaichatbot-frameworkchatbots
    GitHub पर देखें↗12,846
  • zai-org/glm-4.5zai-org का अवतार

    zai-org/GLM-4.5

    4,210GitHub पर देखें↗

    GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools an

    Augments language model generation with real-time web search data to provide up-to-date and verifiable answers.

    Pythonagentglmllm
    GitHub पर देखें↗4,210
  • presenton/presentonpresenton का अवतार

    presenton/presenton

    4,042GitHub पर देखें↗

    Presenton is an AI-powered presentation engine and API designed to transform natural language prompts, uploaded documents, and structured data into professional slide decks. It functions as a generation service that leverages large language models to automate the creation of outlines, slide content, and visual assets. The system is distinguished by its support for both cloud-based and self-hosted infrastructure, allowing for the integration of local language models and image generators to ensure data privacy. It implements a Model Context Protocol server, enabling external AI agents to trigge

    Augments slide content generation with real-time external search data to ensure accuracy and currency.

    TypeScriptai-agentai-presentationapi
    GitHub पर देखें↗4,042
  1. Home
  2. Artificial Intelligence & ML
  3. Language Model Response Generators
  4. Search-Enhanced Generation

सब-टैग एक्सप्लोर करें

  • Targeted Query GenerationUsing language models to derive specific search terms from goals and existing data. **Distinct from Search-Enhanced Generation:** Generates web search terms for exploration, whereas search-enhanced generation focuses on producing the final response.