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5 个仓库

Awesome GitHub RepositoriesSemantic Retrieval Engines

Systems for fetching relevant data fragments using vector or keyword matching.

Distinguishing note: Focuses on the retrieval mechanism for knowledge bases.

Explore 5 awesome GitHub repositories matching data & databases · Semantic Retrieval Engines. Refine with filters or upvote what's useful.

Awesome Semantic Retrieval Engines GitHub Repositories

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  • labring/fastgptlabring 的头像

    labring/FastGPT

    27,132在 GitHub 上查看↗

    FastGPT is a comprehensive platform for building, deploying, and managing context-aware artificial intelligence applications. It provides a unified environment that integrates custom data sources with language models, utilizing a retrieval-augmented generation engine to ground responses in accurate, domain-specific information. The system is designed for enterprise-scale use, featuring multi-tenant architecture, administrative controls, and secure authentication protocols including OAuth 2.0 and custom single sign-on integration. The platform distinguishes itself through a visual, node-based

    The platform provides knowledge base content retrieval by fetching relevant document fragments using semantic matching or keyword algorithms to provide necessary context.

    TypeScriptagentclaudedeepseek
    在 GitHub 上查看↗27,132
  • cloudwego/einocloudwego 的头像

    cloudwego/eino

    9,675在 GitHub 上查看↗

    Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

    Indexes textual documents and retrieves relevant content using vector similarity matching.

    Goaiai-applicationai-framework
    在 GitHub 上查看↗9,675
  • brexhq/prompt-engineeringbrexhq 的头像

    brexhq/prompt-engineering

    9,538在 GitHub 上查看↗

    This project is a comprehensive guide and framework for large language model prompt engineering. It provides a collection of techniques and patterns for optimizing model responses through structured system prompts, context management, and a variety of implementation patterns. The project focuses on several specialized domains, including the creation of autonomous agents through reasoning loops and the implementation of retrieval augmented generation to inject semantic context into prompts. It also provides methods for enforcing structured outputs in serialization formats like JSON or YAML for

    Utilizes semantic retrieval engines to fetch relevant data fragments for injection into prompt context.

    在 GitHub 上查看↗9,538
  • getzep/zepgetzep 的头像

    getzep/zep

    4,076在 GitHub 上查看↗

    Zep is a long-term memory layer and persistent storage system for large language model applications. It functions as a memory service and vector database orchestrator that manages chat history, user preferences, and context retrieval to reduce hallucinations in AI agents. The system maintains a temporal knowledge graph that stores interaction data as dated facts to track how user preferences and environments evolve over time. It combines these knowledge graphs with a store for persisting unstructured message data at the user and session levels. The platform provides capabilities for AI conte

    Uses mathematical embeddings to find relevant context by calculating the distance between queries and stored documents.

    Pythonaiknowledge-graphslanguage-model
    在 GitHub 上查看↗4,076
  • superlinked/superlinkedsuperlinked 的头像

    superlinked/superlinked

    40在 GitHub 上查看↗

    Superlinked is a development framework designed for building semantic search and retrieval pipelines. It functions as a machine learning data pipeline and semantic retrieval engine, providing the tools necessary to unify data schema definition, embedding generation, and vector database integration within a single application. The framework distinguishes itself by acting as a vector database orchestrator that manages the lifecycle of machine learning models alongside complex search logic. It enables developers to construct structured data models that map raw content and metadata into unified r

    Executes high-performance semantic searches with granular scoring and explainability for retrieved results.

    Jupyter Notebook
    在 GitHub 上查看↗40
  1. Home
  2. Data & Databases
  3. Semantic Retrieval Engines

探索子标签

  • Semantic Rule RetrievalUses vector similarity search to retrieve organizational coding standards and engineering conventions relevant to a code snippet. **Distinct from Semantic Retrieval Engines:** Specific to retrieving engineering rules/standards rather than general data fragments or AI tools.
  • Tool Semantic RetrievalUsing vector similarity search to select the most relevant tools from a large library for an agent. **Distinct from Semantic Retrieval Engines:** Applies semantic retrieval specifically to tool selection for LLMs, rather than general knowledge base retrieval.
  • Tool-Specific Semantic RetrievalRetrieving the most relevant tools from a library using vector similarity to manage prompt context. **Distinct from Semantic Retrieval Engines:** Specifically focuses on retrieving available tools for an agent rather than general knowledge base fragments.