awesome-repositories.com
博客
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目关于排名机制媒体报道MCP 服务器
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

53 个仓库

Awesome GitHub RepositoriesVector Storage

Technologies for storing and managing high-dimensional vector data.

Distinguishing note: None available; no candidates provided.

Explore 53 awesome GitHub repositories matching data & databases · Vector Storage. Refine with filters or upvote what's useful.

Awesome Vector Storage GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • pewdiepie-archdaemon/odysseuspewdiepie-archdaemon 的头像

    pewdiepie-archdaemon/odysseus

    72,184在 GitHub 上查看↗

    Odysseus is a self-hosted AI workspace and autonomous agent framework designed for deploying and managing large language models. It serves as a centralized platform for orchestrating agentic tasks, utilizing a model context protocol server to connect AI models to external system utilities, browser automation, and local hardware. The system distinguishes itself through a combination of retrieval-augmented generation and a RAG knowledge base, using vector stores and local embeddings to provide persistent semantic memory. It further integrates AI-driven communication management to triage email i

    Stores and retrieves high-dimensional embeddings to support retrieval-augmented generation.

    Python
    在 GitHub 上查看↗72,184
  • qdrant/qdrantqdrant 的头像

    qdrant/qdrant

    32,372在 GitHub 上查看↗

    Qdrant is a high-performance vector similarity database designed to store, index, and search high-dimensional vectors alongside structured metadata. It functions as a distributed search engine that manages large-scale data clusters, providing low-latency retrieval and complex filtering capabilities. The system is built to serve as a specialized middleware layer, connecting machine learning pipelines and AI agents to persistent storage for intelligent information retrieval and recommendation tasks. The platform distinguishes itself through advanced retrieval techniques, including support for h

    Supports in-memory and memory-mapped storage for efficient vector data handling.

    Rustai-searchai-search-engineembeddings-similarity
    在 GitHub 上查看↗32,372
  • pgvector/pgvectorpgvector 的头像

    pgvector/pgvector

    21,787在 GitHub 上查看↗

    Vector similarity search extension for PostgreSQL.

    Offers storage using specialized data types like half-precision and binary formats to optimize memory usage.

    Cpostgresvector-searchembeddings
    在 GitHub 上查看↗21,787
  • openai/chatgpt-retrieval-pluginopenai 的头像

    openai/chatgpt-retrieval-plugin

    21,192在 GitHub 上查看↗

    This project is a retrieval-augmented generation pipeline designed for building custom ChatGPT plugins that allow language models to query private or professional documents. It implements a full retrieval workflow, from processing and indexing document chunks to retrieving relevant context for natural language queries. The system distinguishes itself through a hybrid retrieval approach that combines dense vector embeddings with sparse keyword matching, further refined by a two-stage semantic re-ranking process. It includes specialized data privacy tools for screening personally identifiable i

    Indexes high-dimensional vectors in cloud-native databases to support fast similarity searches across large datasets.

    Pythonchatgptchatgpt-plugins
    在 GitHub 上查看↗21,192
  • camel-ai/camelcamel-ai 的头像

    camel-ai/camel

    17,253在 GitHub 上查看↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Transforms text and images into dense numerical vector representations for semantic search and similarity analysis.

    Pythonagentai-societiesartificial-intelligence
    在 GitHub 上查看↗17,253
  • tursodatabase/libsqltursodatabase 的头像

    tursodatabase/libsql

    16,887在 GitHub 上查看↗

    LibSQL is a high-performance, distributed SQL database engine that extends SQLite to support remote network access, edge computing, and real-time synchronization. It functions as an embedded database library that integrates directly into application processes while providing the infrastructure to maintain consistency across multiple geographic regions. The platform distinguishes itself by enabling database interaction over standard HTTP protocols, allowing applications to query remote data sources in serverless and edge environments without requiring local filesystem access. It includes nativ

    Stores and queries high-dimensional vector data natively to support machine learning and AI applications.

    Cdatabaseembedded-databaserust
    在 GitHub 上查看↗16,887
  • semi-technologies/weaviatesemi-technologies 的头像

    semi-technologies/weaviate

    16,337在 GitHub 上查看↗

    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

    Saves high-dimensional vectors alongside structured data objects to enable both semantic and filtered retrieval.

    Go
    在 GitHub 上查看↗16,337
  • piskvorky/gensimpiskvorky 的头像

    piskvorky/gensim

    16,361在 GitHub 上查看↗

    Gensim is a natural language processing toolkit designed for large-scale text analysis and the training of semantic vector embeddings. It provides a framework for identifying latent thematic structures within document collections and calculating semantic similarity between text segments using unsupervised statistical algorithms. The project is distinguished by its ability to handle datasets that exceed available system memory through incremental corpus streaming, which processes documents one at a time from disk. It utilizes sparse vector representations and dictionary-based token mapping to

    Converts text into sparse numerical representations based on word frequency counts for semantic analysis.

    Pythondata-miningdata-sciencedocument-similarity
    在 GitHub 上查看↗16,361
  • morvanzhou/tutorialsMorvanZhou 的头像

    MorvanZhou/tutorials

    12,952在 GitHub 上查看↗

    This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

    Implements word-to-vector conversions using CBOW and Skip-Gram to capture semantic meanings in text.

    Pythonmachine-learningmultiprocessingneural-network
    在 GitHub 上查看↗12,952
  • basedhardware/omiBasedHardware 的头像

    BasedHardware/omi

    12,869在 GitHub 上查看↗

    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

    Utilizes a vector database to store high-dimensional embeddings of conversations for efficient semantic retrieval.

    Dartaiappbci
    在 GitHub 上查看↗12,869
  • langchain4j/langchain4jlangchain4j 的头像

    langchain4j/langchain4j

    12,346在 GitHub 上查看↗

    LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model pr

    Provides the capability to save and retrieve high-dimensional vector representations for efficient similarity searches.

    Javaanthropicchatgptchroma
    在 GitHub 上查看↗12,346
  • insforge/insforgeInsForge 的头像

    InsForge/InsForge

    11,794在 GitHub 上查看↗

    InsForge is a backend-as-a-service platform that provides an integrated suite of tools for managing relational databases, identity provision, object storage, and serverless compute. It functions as an open-source identity provider and a PostgreSQL database manager featuring integrated vector storage and row-level security. The platform serves as an LLM orchestration gateway, offering a unified endpoint to route requests across various AI providers through an OpenAI-compatible interface. It enables AI-driven application generation and connects AI agents to backend resources using a standardize

    Provides integrated storage and management for high-dimensional vector data to enable semantic search.

    TypeScriptaiai-agentscoding
    在 GitHub 上查看↗11,794
  • g-truc/glmg-truc 的头像

    g-truc/glm

    10,710在 GitHub 上查看↗

    This project is a header-only C++ library designed for graphics mathematics, providing a comprehensive suite of vector, matrix, and quaternion types. It is built using template metaprogramming to generate mathematical primitives at compile time, eliminating the need for precompiled binary libraries and allowing for direct integration into existing build systems. The library is distinguished by its strict adherence to the OpenGL Shading Language specification, ensuring that mathematical results remain consistent across both CPU and GPU code. It provides specialized utilities for managing float

    Provides one-dimensional vector structures to facilitate mathematical operations.

    C++cppcpp-libraryglm
    在 GitHub 上查看↗10,710
  • liaokongvfx/langchain-chinese-getting-started-guideliaokongVFX 的头像

    liaokongVFX/LangChain-Chinese-Getting-Started-Guide

    9,039在 GitHub 上查看↗

    This project is a collection of tutorials and guides for building large language model applications using the LangChain framework, written in Chinese. It serves as a learning resource for developing software that integrates language models with memory and chain-based logic. The resource provides specific walkthroughs for implementing retrieval augmented generation systems using vector stores and document loaders. It includes guides on creating autonomous agents that dynamically select and execute external tools, as well as tutorials for translating plain text queries into executable database

    Converts text into numerical embeddings and stores them in databases for similarity lookups.

    在 GitHub 上查看↗9,039
  • apachecn/interviewapachecn 的头像

    apachecn/Interview

    8,944在 GitHub 上查看↗

    This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie

    Details the transformation of raw text into numerical vectors using TF-IDF and Word2vec.

    Jupyter Notebookinterviewkaggleleetcode
    在 GitHub 上查看↗8,944
  • clips/patternclips 的头像

    clips/pattern

    8,852在 GitHub 上查看↗

    Pattern is a Python web mining library that functions as an HTML web scraper, a natural language processing toolkit, and a network analysis tool. It provides a mathematical framework for categorizing datasets through a vector space model library. The project enables the extraction of structured data from web services and the creation of searchable web content indexes. It processes unstructured text using sentiment analysis, part-of-speech tagging, and n-gram searching. The library covers machine learning classification through the training of models using perceptron algorithms and support ve

    Implements a mathematical framework for categorizing datasets using high-dimensional vector space representations.

    Python
    在 GitHub 上查看↗8,852
  • catboost/catboostcatboost 的头像

    catboost/catboost

    8,808在 GitHub 上查看↗

    CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu

    Allows integrating trained models into external database environments to perform real-time predictions on stored data.

    C++big-datacatboostcategorical-features
    在 GitHub 上查看↗8,808
  • ljpzzz/machinelearningljpzzz 的头像

    ljpzzz/machinelearning

    8,706在 GitHub 上查看↗

    This project is a machine learning implementation library featuring a collection of code examples that implement supervised, unsupervised, and reinforcement learning algorithms from scratch. It provides a comprehensive set of toolkits for core machine learning components, including a natural language processing toolkit, a reinforcement learning framework, and suites for data dimensionality reduction and pattern mining. The library includes specialized implementations for reinforcement learning, such as Q-Learning, Deep Q-Networks, and Actor-Critic agents. The natural language processing capab

    Provides tools for converting raw text into numerical vector representations using TF-IDF and hashing.

    Jupyter Notebookalgorithmsmachinelearningreinforcementlearning
    在 GitHub 上查看↗8,706
  • kreuzberg-dev/kreuzbergkreuzberg-dev 的头像

    kreuzberg-dev/kreuzberg

    8,527在 GitHub 上查看↗

    Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo

    Converts text into numerical embedding vectors using a configured ONNX model.

    Rustdocument-intelligenceelixirffi
    在 GitHub 上查看↗8,527
  • zilliztech/gptcachezilliztech 的头像

    zilliztech/GPTCache

    8,068在 GitHub 上查看↗

    GPTCache is a semantic caching layer and response optimizer for large language models. It functions as pluggable middleware for orchestration frameworks, utilizing vector database caching to store and retrieve model responses based on the semantic similarity of prompts rather than exact text matches. The system uses embeddings to determine cache hits by comparing the distance between new queries and stored vectors. It employs a hybrid storage model that persists original prompts in relational databases while maintaining high-dimensional embeddings in vector stores. The project covers a broad

    Persists model responses and high-dimensional embeddings using specialized vector storage systems.

    Python
    在 GitHub 上查看↗8,068
上一个123下一个
  1. Home
  2. Data & Databases
  3. Vector Storage

探索子标签

  • Single-Component VectorsOne-dimensional vector structures for scalar storage. **Distinct from Vector Storage:** Distinct from Vector Storage: focuses on the single-component type definition rather than high-dimensional storage systems.
  • Specialized Storage Formats1 个子标签Support for compact data types like half-precision and binary formats for vector storage. **Distinct from Vector Storage:** Distinct from Vector Storage: focuses on the specific data representation formats rather than general storage management.
  • Text Vectorizers3 个子标签Tools for converting raw text into numerical vector representations based on frequency counts. **Distinct from Vector Storage:** Distinct from Vector Storage: focuses on the transformation process rather than the storage of vectors.
  • Vector Index MaintenanceReorganizes vector indexes and removes deleted entries to reclaim memory and maintain search accuracy. **Distinct from Vector Storage:** Distinct from Vector Storage: focuses on the maintenance and reorganization of indexes after modifications, not the storage engine itself.