awesome-repositories.com
Blog
awesome-repositories.com

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectDespreCum realizăm clasamentulPresăServer MCP
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

67 repository-uri

Awesome GitHub RepositoriesKnowledge Base Retrieval

Systems that store and retrieve enterprise knowledge for context-aware AI responses.

Distinct from Agentic RAG Development: Distinct from agentic RAG development: focuses on the storage and retrieval of enterprise knowledge bases rather than the agentic orchestration logic.

Explore 67 awesome GitHub repositories matching artificial intelligence & ml · Knowledge Base Retrieval. Refine with filters or upvote what's useful.

Awesome Knowledge Base Retrieval GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • microsoft/ai-agents-for-beginnersAvatar microsoft

    microsoft/ai-agents-for-beginners

    67,369Vezi pe GitHub↗

    This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin

    Provides technical guidance on implementing knowledge base retrieval systems to enable context-aware AI responses.

    Jupyter Notebookagentic-aiagentic-frameworkagentic-rag
    Vezi pe GitHub↗67,369
  • stangirard/quivrAvatar StanGirard

    StanGirard/quivr

    39,167Vezi pe GitHub↗

    Quivr is a framework for building retrieval-augmented generation pipelines that connect large language models to custom knowledge bases. It serves as a generative AI integration layer that abstracts the process of transforming diverse document sources into searchable context for AI responses. The project orchestrates the end-to-end flow between document ingestion, vector storage management, and model provider interfaces. It features a vector-store-agnostic retrieval system and a modular API layer that allows for flexible switching between different generative model providers. The system cove

    Enables the storage and retrieval of custom knowledge bases to provide context-aware AI responses.

    Python
    Vezi pe GitHub↗39,167
  • chatchat-space/langchain-chatchatAvatar chatchat-space

    chatchat-space/Langchain-Chatchat

    38,211Vezi pe GitHub↗

    Langchain-Chatchat is a system for building retrieval-augmented generation applications and autonomous AI agents. It integrates a knowledge base management system and an agent framework to enable language models to interact with private documents and execute multi-step tasks through external tools. The platform supports local deployment of language models on private infrastructure to operate without an internet connection. It includes a multimodal AI platform that combines vision models for image analysis with text-to-image generation capabilities. The system provides a web-based conversatio

    Implements a system for storing and retrieving document segments from a local knowledge base to provide context for AI responses.

    Pythonchatbotchatchatchatglm
    Vezi pe GitHub↗38,211
  • danswer-ai/danswerAvatar danswer-ai

    danswer-ai/danswer

    30,552Vezi pe GitHub↗

    Danswer is an LLM application framework and RAG engine that provides a self-hosted interface for connecting large language models to private data. It serves as an enterprise AI chat interface and agent orchestrator, enabling the creation of specialized assistants with custom instructions and knowledge bases. The platform differentiates itself through an observability dashboard for tracking query history and token consumption, as well as a white-labeled interface for customized branding. It includes a multi-step research workflow for producing long-form reports and a sandboxed environment for

    Combines vector and keyword search with reranking to retrieve relevant private document context for LLM prompts.

    Python
    Vezi pe GitHub↗30,552
  • datawhalechina/prompt-engineering-for-developersAvatar datawhalechina

    datawhalechina/prompt-engineering-for-developers

    24,267Vezi pe GitHub↗

    This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

    Implements systems that retrieve enterprise knowledge from vector databases to provide context for AI responses.

    Jupyter Notebook
    Vezi pe GitHub↗24,267
  • garrytan/gbrainAvatar garrytan

    garrytan/gbrain

    23,848Vezi pe GitHub↗

    gbrain is an agent framework and retrieval-augmented generation system that combines a durable task queue, a git-synced vector store, and a knowledge graph engine. It provides a foundation for building AI agents that interact with structured knowledge bases using the Model Context Protocol. The system synchronizes markdown files from a git repository into a database for high-performance semantic retrieval and creates typed edges between data pages by extracting entity references and wikilinks. It uses a database-backed queue to execute persistent background jobs and tool loops, ensuring relia

    Combines vector embeddings and keyword matching to retrieve relevant pages based on semantic and factual connectivity.

    TypeScript
    Vezi pe GitHub↗23,848
  • rohitg00/agentmemoryAvatar rohitg00

    rohitg00/agentmemory

    23,785Vezi pe GitHub↗

    AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel

    Combines keyword matching, vector embeddings, and graph traversal to retrieve the most relevant project context.

    TypeScriptagentmemoryagentsai
    Vezi pe GitHub↗23,785
  • arc53/docsgptAvatar arc53

    arc53/DocsGPT

    17,939Vezi pe GitHub↗

    DocsGPT is a retrieval-augmented generation platform and private knowledge base used to build AI agents that perform grounded search and analysis. It functions as a multi-model AI orchestrator and enterprise agent builder, allowing for the integration of various local and cloud language models to customize reasoning and text generation. The project provides a visual environment for developing automated assistants using conditional logic and third-party API connectivity. It enables the creation of private AI agents capable of performing enterprise search and detailed document analysis using pr

    Uses retrieval-augmented generation to ground AI responses in private enterprise knowledge bases.

    Pythonagent-builderagentsai
    Vezi pe GitHub↗17,939
  • tencent/weknoraAvatar Tencent

    Tencent/WeKnora

    16,974Vezi pe GitHub↗

    WeKnora is a multi-tenant retrieval-augmented generation (RAG) knowledge platform and autonomous AI agent framework. It transforms raw documents into queryable knowledge bases and integrates large language models with vector databases to provide grounded AI responses. The system also functions as a Model Context Protocol (MCP) tool server, exposing knowledge search and agentic capabilities to external AI clients. The platform distinguishes itself through an autonomous agent framework that utilizes iterative reasoning, tool calling, and web search to solve multi-step tasks. It implements a sta

    Implements hybrid search retrievers that combine keyword and vector search to improve document recall and accuracy.

    Goagentagenticai
    Vezi pe GitHub↗16,974
  • mayooear/gpt4-pdf-chatbot-langchainAvatar mayooear

    mayooear/gpt4-pdf-chatbot-langchain

    16,542Vezi pe GitHub↗

    This project is a framework for building custom AI chatbots capable of PDF document analysis. It implements Retrieval Augmented Generation to connect a large language model to private document data. The system utilizes graph-based agent orchestration to control conversation flow and decision logic. It maintains context across interactions through thread-based state management and delivers AI responses to the user interface via real-time streaming. The project covers PDF document ingestion through chunk-based processing and vector-store retrieval. It includes mechanisms for query-based data r

    Implements a retrieval system that grounds AI responses in uploaded document data via a knowledge base.

    TypeScript
    Vezi pe GitHub↗16,542
  • rockchinq/qchatgptAvatar RockChinQ

    RockChinQ/QChatGPT

    16,352Vezi pe GitHub↗

    QChatGPT is an LLM bot orchestration platform and multi-platform chatbot gateway. It serves as a bridge that routes messages between various instant messaging services and AI models using a unified codebase, functioning as an agentic workflow manager to handle complex multi-turn dialogues. The platform distinguishes itself through an extensible plugin framework that allows for the addition of custom logic and event-driven features via standardized protocols. It also includes a web-based bot controller, providing a browser interface to manage bot behavior and monitor real-time performance with

    Integrates bots with RAG frameworks and data stores to provide grounded, evidence-based answers.

    Python
    Vezi pe GitHub↗16,352
  • apache/dorisAvatar apache

    apache/doris

    15,526Vezi pe GitHub↗

    Doris is a distributed SQL data warehouse designed for high-performance analytical workloads and real-time data processing. It functions as a unified platform that integrates traditional relational warehousing with lakehouse query capabilities, allowing users to execute analytical operations directly against external data lakes without requiring data migration. The system distinguishes itself through a shared-nothing, massively parallel processing architecture that utilizes vectorized query execution and columnar storage to maintain sub-second latency. It supports dynamic schema evolution, en

    Facilitates intelligent document retrieval and context-aware responses by storing and querying enterprise knowledge base data.

    Javaagentaibigquery
    Vezi pe GitHub↗15,526
  • gaizhenbiao/chuanhuchatgptAvatar GaiZhenbiao

    GaiZhenbiao/ChuanhuChatGPT

    15,311Vezi pe GitHub↗

    This project is a web-based user interface and multi-model API gateway for interacting with various large language model providers and local inference services. It functions as a retrieval-augmented generation chatbot for private document questioning, a manager for model fine-tuning, and an autonomous agent framework. The system distinguishes itself by integrating an autonomous assistant mode that uses web search and external tools to solve complex, multi-step tasks without manual prompting. It also features an API gateway capable of rotating multiple authentication keys to balance usage and

    Processes uploaded local files into a searchable knowledge base for retrieval-augmented generation.

    Python
    Vezi pe GitHub↗15,311
  • langbot-app/langbotAvatar langbot-app

    langbot-app/LangBot

    15,311Vezi pe GitHub↗

    LangBot is an orchestration platform designed for building, managing, and deploying AI agents. It functions as a comprehensive framework for integrating large language models with custom workflows, enabling developers to connect intelligent agents to various messaging platforms and external tools. The platform distinguishes itself through a modular, plugin-based architecture that allows for the extension of agent capabilities via custom tools and file parsers. It features a secure, sandbox-isolated runtime environment that executes untrusted code and plugin logic within resource-constrained c

    Grounds AI responses in private data through document ingestion, semantic indexing, and vector-based knowledge retrieval.

    Pythonagentcozedeepseek
    Vezi pe GitHub↗15,311
  • netease-youdao/qanythingAvatar netease-youdao

    netease-youdao/QAnything

    14,020Vezi pe GitHub↗

    QAnything is a retrieval-augmented generation application framework and self-hosted AI interface. It functions as a system that combines a vector database knowledge base, a document parsing service, and a hybrid search engine to generate answers based on private user data. The project features a modular pipeline architecture that allows users to independently replace components such as parsers, embedding models, and reranking engines. It supports local-first model deployment and offline operation to ensure data privacy, and includes a two-stage retrieval pipeline that merges dense vector embe

    Combines keyword and vector search with metadata filtering and reranking for accurate document retrieval.

    Python
    Vezi pe GitHub↗14,020
  • basedhardware/omiAvatar BasedHardware

    BasedHardware/omi

    12,869Vezi pe GitHub↗

    Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

    Provides a system for retrieving information from the total historical record of encountered data for context-aware responses.

    Dartaiappbci
    Vezi pe GitHub↗12,869
  • aws/aws-cdkAvatar aws

    aws/aws-cdk

    12,817Vezi pe GitHub↗

    The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

    Stores and retrieves enterprise knowledge for context-aware AI responses.

    TypeScriptawscloud-infrastructurehacktoberfest
    Vezi pe GitHub↗12,817
  • nashsu/llm_wikiAvatar nashsu

    nashsu/llm_wiki

    12,563Vezi pe GitHub↗

    This project is an LLM knowledge base builder and personal knowledge management tool. It is a desktop application designed to transform diverse documents into a persistent, interlinked wiki through LLM analysis and incremental ingestion. The system distinguishes itself with a knowledge graph visualizer that uses community detection algorithms to map relationships between concepts and identify topical clusters. It features a hybrid retrieval system that combines keyword matching, vector embeddings, and graph relevance to locate information. The platform covers a wide range of capabilities inc

    Provides a conversational interface that retrieves answers from processed knowledge with integrated inline source citations.

    TypeScript
    Vezi pe GitHub↗12,563
  • idootop/mi-gptAvatar idootop

    idootop/mi-gpt

    12,458Vezi pe GitHub↗

    mi-gpt is a voice assistant bridge and agent orchestrator that connects smart speakers to large language models. It functions as an integration layer that routes audio requests from hardware speakers to AI providers and converts generated text back into speech via a customizable synthesis system. The project features a retrieval-augmented generation knowledge base that uses embeddings and external documents to provide context-aware responses. It includes a persona definition system for configuring behavioral rules, system prompts, and roleplay characteristics, alongside a plugin architecture

    Uses embeddings and external documents to provide responses based on wikis and historical data.

    TypeScript
    Vezi pe GitHub↗12,458
  • boto/boto3Avatar boto

    boto/boto3

    9,834Vezi pe GitHub↗

    Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and contain

    Retrieves relevant information from external data sources to provide AI models with grounded context.

    Pythonawsaws-sdkcloud
    Vezi pe GitHub↗9,834
Înapoi123…4Înainte
  1. Home
  2. Artificial Intelligence & ML
  3. Agentic RAG Development
  4. Knowledge Base Retrieval

Explorează sub-etichetele

  • Cited Query ResponsesQueries indexed knowledge and returns answers with inline source citations. **Distinct from Knowledge Base Retrieval:** Distinct from Knowledge Base Retrieval: focuses on returning structured answers with source citations rather than just retrieving relevant chunks.
  • Hybrid Search Retrievers5 sub-tag-uriRetrieval methods that combine keyword and vector search with metadata filtering and reranking for accurate responses. **Distinct from Knowledge Base Retrieval:** Distinct from Knowledge Base Retrieval: focuses on hybrid search techniques (keyword + vector) with reranking, not general knowledge base storage and retrieval.
  • Local Document IndexingProcessing of uploaded local files into searchable vector stores for RAG pipelines. **Distinct from Knowledge Base Retrieval:** Distinct from Knowledge Base Retrieval: focuses on the ingestion and indexing process of local files rather than the retrieval mechanism.
  • MCP Knowledge Base GroundingConnects agents to external data sources via the Model Context Protocol for retrieval and citation. **Distinct from Knowledge Base Retrieval:** Distinct from Knowledge Base Retrieval: uses the Model Context Protocol standard for connecting to external data sources.