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52 dépôts

Awesome GitHub RepositoriesVector Similarity Search

Algorithms and data structures for performing similarity queries on high-dimensional vector embeddings.

Distinguishing note: Focuses on the mathematical matching of embeddings rather than general database storage.

Explore 52 awesome GitHub repositories matching artificial intelligence & ml · Vector Similarity Search. Refine with filters or upvote what's useful.

Awesome Vector Similarity Search GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • elasticsearch/elasticsearchAvatar de elasticsearch

    elasticsearch/elasticsearch

    77,171Voir sur GitHub↗

    Elasticsearch is a distributed search engine and NoSQL document store designed for full-text search and real-time data retrieval. It functions as a RESTful data indexer and vector database, allowing for the storage and management of structured JSON documents across multiple nodes. The system distinguishes itself through its ability to serve as a log analytics platform for monitoring system health and security events. It incorporates vector search implementation using mathematical embeddings to support generative AI and augmented generation applications. The platform covers a broad range of c

    Provides high-speed retrieval of similar documents based on high-dimensional vector embeddings.

    Java
    Voir sur GitHub↗77,171
  • anthropics/anthropic-cookbookAvatar de anthropics

    anthropics/anthropic-cookbook

    45,984Voir sur GitHub↗

    This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce

    Implements mathematical distance functions to measure relatedness between text embeddings.

    Jupyter Notebook
    Voir sur GitHub↗45,984
  • deepinsight/insightfaceAvatar de deepinsight

    deepinsight/insightface

    29,002Voir sur GitHub↗

    InsightFace is a comprehensive deep learning framework designed for face recognition, biometric identity verification, and feature extraction. It provides a specialized engine for one-to-one verification and one-to-many identification tasks, utilizing convolutional neural networks to transform raw image pixels into high-dimensional vector embeddings. The project includes a complete toolkit for detecting, aligning, and processing facial data to ensure consistent identity discrimination. Beyond core recognition, the platform distinguishes itself through an extensive model management and optimiz

    Determines identity by calculating the distance between normalized feature embeddings using cosine similarity or Euclidean metrics in high-dimensional space.

    Pythonage-estimationarcfaceface-alignment
    Voir sur GitHub↗29,002
  • d2l-ai/d2l-enAvatar de d2l-ai

    d2l-ai/d2l-en

    29,001Voir sur GitHub↗

    This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

    Queries semantic similarity between words using cosine similarity in trained embedding spaces.

    Pythonbookcomputer-visiondata-science
    Voir sur GitHub↗29,001
  • lukasmasuch/best-of-ml-pythonAvatar de lukasmasuch

    lukasmasuch/best-of-ml-python

    23,236Voir sur GitHub↗

    This project serves as a comprehensive, community-driven directory of high-quality open-source Python libraries and tools for machine learning, data science, and artificial intelligence. It functions as a centralized resource for developers to discover, evaluate, and track the maintenance status of software packages across the entire machine learning ecosystem. The platform distinguishes itself through automated popularity tracking and data-driven content curation, which programmatically validate and rank projects based on community activity and development velocity. By organizing these tools

    Catalogs libraries for vector similarity search and high-dimensional embedding retrieval.

    automlchatgptdata-analysis
    Voir sur GitHub↗23,236
  • vonng/ddiaAvatar de Vonng

    Vonng/ddia

    22,648Voir sur GitHub↗

    This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

    Performs similarity searches on high-dimensional vector embeddings using specialized indexing structures.

    Pythonbookdatabaseddia
    Voir sur GitHub↗22,648
  • serengil/deepfaceAvatar de serengil

    serengil/deepface

    22,226Voir sur GitHub↗

    Deepface is a comprehensive deep learning library for facial recognition and demographic analysis. It provides a modular pipeline that handles the entire lifecycle of facial processing, including detection, geometric alignment, and the transformation of facial images into high-dimensional numerical vector embeddings for identity verification and similarity comparison. The library distinguishes itself through a model ensemble approach, which combines predictions from multiple pre-trained neural networks to improve classification accuracy and reduce bias. It also integrates advanced security fe

    Performs similarity queries on high-dimensional facial vector embeddings to find matching identities.

    Pythonage-predictionarcfacedeep-learning
    Voir sur GitHub↗22,226
  • redis/go-redisAvatar de redis

    redis/go-redis

    22,159Voir sur GitHub↗

    This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

    Performs vector similarity searches to match records based on semantic meaning rather than exact keywords.

    Gogogolangredis
    Voir sur GitHub↗22,159
  • camel-ai/camelAvatar de camel-ai

    camel-ai/camel

    17,253Voir sur GitHub↗

    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

    Implements vector similarity search for semantic retrieval within agent memory.

    Pythonagentai-societiesartificial-intelligence
    Voir sur GitHub↗17,253
  • piskvorky/gensimAvatar de piskvorky

    piskvorky/gensim

    16,361Voir sur GitHub↗

    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

    Index high-dimensional vector representations to enable rapid retrieval of similar items from large datasets using approximate nearest neighbor techniques.

    Pythondata-miningdata-sciencedocument-similarity
    Voir sur GitHub↗16,361
  • xai-org/x-algorithmAvatar de xai-org

    xai-org/x-algorithm

    15,579Voir sur GitHub↗

    X-algorithm is a modular recommendation engine framework designed to orchestrate personalized content feeds. It functions as a machine learning ranking system that manages the end-to-end lifecycle of content delivery, from initial candidate retrieval to final display ordering. The system distinguishes itself through a multi-stage pipeline that integrates vector-based similarity search with transformer-based engagement prediction. By mapping user history and content features into high-dimensional embeddings, it performs rapid approximate nearest neighbor searches to identify relevant items. Th

    Uses high-dimensional embeddings to perform rapid approximate nearest neighbor searches for identifying relevant content candidates.

    Rust
    Voir sur GitHub↗15,579
  • scylladb/scylladbAvatar de scylladb

    scylladb/scylladb

    15,355Voir sur GitHub↗

    ScyllaDB is a distributed NoSQL database engine designed for high-throughput data storage and low-latency performance at scale. It functions as a shard-aware platform that manages large-scale datasets across distributed clusters, providing a foundation for real-time applications that require consistent availability and operational stability. The system distinguishes itself through a shared-nothing architecture that distributes data across independent CPU cores to eliminate lock contention. It incorporates a user-space networking stack and an asynchronous event-driven engine to maximize hardwa

    Implements algorithms and data structures for performing similarity queries on high-dimensional vector embeddings.

    C++c-plus-pluscassandracpp
    Voir sur GitHub↗15,355
  • asyncfuncai/deepwiki-openAvatar de AsyncFuncAI

    AsyncFuncAI/deepwiki-open

    14,362Voir sur GitHub↗

    This platform is an automated documentation and codebase analysis system designed to generate structured wikis, technical guides, and interactive diagrams from source code repositories. It functions as a retrieval-augmented generation framework that connects codebases to language models, enabling context-aware answers, deep research, and automated documentation updates through semantic vector search. The system distinguishes itself through a self-hosted, containerized architecture that supports both cloud-based and local AI model execution. It provides sophisticated model orchestration, allow

    Executes complex mathematical similarity computations faster by applying vectorized operations and just-in-time compilation.

    Pythonaigeminigithub
    Voir sur GitHub↗14,362
  • vibrantlabsai/ragasAvatar de vibrantlabsai

    vibrantlabsai/ragas

    12,659Voir sur GitHub↗

    Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

    Quantifies the alignment between generated responses and reference answers using cosine similarity of vector embeddings.

    Pythonevaluationllmllmops
    Voir sur GitHub↗12,659
  • codexu/note-genAvatar de codexu

    codexu/note-gen

    12,173Voir sur GitHub↗

    Note-gen is an artificial intelligence-assisted note-taking application and knowledge management tool designed for local-first data ownership. It functions as a workspace that leverages language models to organize, summarize, and synthesize personal notes into structured documents while maintaining offline accessibility. The platform distinguishes itself through a multimodal workflow orchestrator that chains sequences of tasks to process text, images, and external data. By integrating vision-language models, it extracts information from visual inputs like screenshots and documents, converting

    Performs similarity searches on numerical embeddings to match the underlying intent of user queries.

    TypeScriptagentchatbotknowledge-base
    Voir sur GitHub↗12,173
  • sashabaranov/go-openaiAvatar de sashabaranov

    sashabaranov/go-openai

    10,694Voir sur GitHub↗

    This project is a Go library that provides a programmatic interface for interacting with generative AI services. It serves as a comprehensive software development kit for integrating large language models into applications, enabling developers to perform tasks such as text and chat completion, image generation, and audio transcription. The library distinguishes itself through a unified infrastructure designed for robust network communication and service management. It features structured request mapping and error normalization to ensure type-safe interactions and simplified debugging. Further

    Computes dot products between embedding vectors to quantify semantic relationships.

    Gochatgptchatgpt-apidall-e
    Voir sur GitHub↗10,694
  • autogluon/autogluonAvatar de autogluon

    autogluon/autogluon

    9,997Voir sur GitHub↗

    AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

    Determines semantic similarity between images by projecting them into high-dimensional vector embeddings.

    Pythonautogluonautomated-machine-learningautoml
    Voir sur GitHub↗9,997
  • tporadowski/redisAvatar de tporadowski

    tporadowski/redis

    9,987Voir sur GitHub↗

    Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

    Retrieves vector similarity results in real-time using an in-memory architecture to minimize response times.

    Credisredis-for-windowsredis-msi-installer
    Voir sur GitHub↗9,987
  • facebookresearch/imagebindAvatar de facebookresearch

    facebookresearch/ImageBind

    9,036Voir sur GitHub↗

    ImageBind is a multi-modal embedding model and joint representation learner that maps images, text, audio, and other modalities into a single shared vector space. It functions as a cross-modal retrieval framework designed to bind multiple sensory inputs into one cohesive mathematical embedding. The system uses a contrastive learning architecture to align disparate data types by maximizing the similarity between related samples. This allows the model to perform zero-shot multimodal classification and execute cross-modal data retrieval, such as locating visual content via natural language descr

    Transforms diverse sensory inputs into fixed-dimension vectors to enable efficient cosine similarity comparisons.

    Python
    Voir sur GitHub↗9,036
  • spring-projects/spring-aiAvatar de spring-projects

    spring-projects/spring-ai

    9,001Voir sur GitHub↗

    Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework

    Accepts text queries and returns semantically similar documents ranked by vector similarity.

    Javaartificial-intelligencejavaspring-ai
    Voir sur GitHub↗9,001
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Explorer les sous-tags

  • Embedding Generation PipelinesWorkflows for transforming raw text into high-dimensional vectors for storage in vector databases. **Distinct from Vector Similarity Search:** Focuses on the preprocessing/generation of embeddings rather than the search/matching algorithms.
  • Fixed-Dimension Projections1 sous-tagTransforms diverse input features into uniform length vectors for mathematical comparison. **Distinct from Vector Similarity Search:** Focuses on the dimensionality transformation for similarity search rather than the search algorithms themselves
  • Hybrid Search MethodsCombining full-text BM25 ranking with vector similarity search and relational filters in a single SQL query. **Distinct from Vector Similarity Search:** Distinct from Vector Similarity Search: adds full-text BM25 ranking and relational filters alongside vector search, not just vector similarity.
  • Low-Latency Vector RetrievalHigh-speed retrieval of vector similarity results using in-memory architectures. **Distinct from Vector Similarity Search:** Focuses on the latency and architectural performance of the retrieval, not just the mathematical similarity algorithm.
  • Similarity Query Engines1 sous-tagComputational interfaces for ranking the semantic closeness of documents against indexed collections. **Distinct from Vector Similarity Search:** Distinct from Vector Similarity Search: focuses on the query and ranking interface rather than the underlying search algorithm.