163 repositorios
Generation of vector representations for text to enable semantic search and memory.
Distinguishing note: Focuses on the generation process for embeddings.
Explore 163 awesome GitHub repositories matching artificial intelligence & ml · Vector Embeddings. Refine with filters or upvote what's useful.
LocalAI is a local generative AI platform and inference engine designed to host large language, vision, and audio models on private hardware. It functions as an API compatible gateway that mimics proprietary service endpoints, allowing existing third-party software to integrate with a self-hosted backend. The platform distinguishes itself as a distributed AI model orchestrator, capable of scaling inference across machine clusters using VRAM-aware routing and hardware coordination. It provides a unified interface for diverse open-source backends and supports self-hosted RAG infrastructure thro
Generates numerical vector representations of text and data to enable semantic search and RAG.
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
Uses mathematical distance between query and document embeddings to retrieve the most relevant text segments.
This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to pr
Improve search accuracy by using specialized vector representations that capture the specific meaning and context of documents within a retrieval system.
This project is an AI model API gateway and proxy server designed to provide a unified interface for interacting with diverse artificial intelligence service providers. It functions as a centralized middleware platform that routes, load balances, and translates API requests across multiple models, enabling developers to access text, image, audio, and video generation capabilities through a single, standardized integration. The gateway distinguishes itself through comprehensive administrative and financial controls, including event-driven usage accounting, real-time token consumption tracking,
Transforms input text into numerical vector representations for semantic search and analysis.
This project is a LangChain-based framework for building retrieval-augmented generation systems, autonomous agents, and multimodal chatbots. It functions as an open-source orchestrator that connects local inference engines and online APIs to manage various large language model deployments. The system distinguishes itself by providing specialized interfaces for local knowledge bases, allowing the loading and vectorization of private documents to create context-aware assistants. It also supports multimodal capabilities, enabling the processing of both text and image inputs through vision-capabl
Converts private documents into high-dimensional vector embeddings to enable semantic search and context retrieval.
HanLP is a natural language processing library and deep learning framework specifically optimized for the Chinese language, while also functioning as a multilingual text processor. It serves as a toolkit for performing linguistic analysis, semantic understanding, and script conversion. The project distinguishes itself through a dedicated focus on Chinese linguistic structures, including a specialized script converter for transforming text between Simplified Chinese, Traditional Chinese, and Pinyin. It further supports domain-specific model training to improve the recognition of professional t
Represents text as high-dimensional vectors to calculate mathematical similarity between different pieces of content.
Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr
Converts input text into vector representations using embedding models for downstream tasks.
This project is an educational resource focused on the internal mechanics and design principles of transformer-based neural networks. It provides a structured guide to the fundamental components of generative artificial intelligence, including sequence modeling, semantic embeddings, and the mathematical foundations of large language models. The repository distinguishes itself through a heavy emphasis on visual documentation, utilizing diagrams and step-by-step explanations to clarify how data flows through complex neural architectures. It serves as a technical reference for developers seeking
Explains the mapping of tokens into vector-space semantic embeddings for contextual meaning.
fastText is a library and framework for word embedding generation, text vectorization, and supervised text classification. It provides tools to transform raw text into fixed-length vector representations and to train models that assign category labels to sentences or documents. The system utilizes subword-based vectorization and character n-gram embeddings, allowing it to generate meaningful vectors for words that were not present during training. To manage resource usage, it includes a quantized language model implementation that employs product quantization and dimensionality reduction to d
Transforms full paragraphs or sentences into single fixed-size vector representations.
Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q
Converts text into numerical vector representations using cloud-based models to enable semantic search and document retrieval.
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
Generates vector representations of text using local or cloud providers to enable semantic retrieval.
Lens is a multi-cluster management platform and desktop application for administering Kubernetes environments. It provides a graphical interface for deploying Helm charts, editing YAML manifests, and managing the lifecycle of pods and deployments. The project features an AI-powered cluster assistant that enables users to query cluster state, perform autonomous troubleshooting, and translate natural language requests into system commands. It also supports collaborative team access through shared spaces, utilizing encrypted cluster sharing and role-based access control to manage credentials and
Integrates with Azure AI Foundry using API keys and resource identifiers for LLM-powered help.
This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a robust framework for managing application state by synchronizing data across browsers, mobile devices, and server-side runtimes. By treating local storage as the primary source of truth, it enables applications to remain functional without network connectivity, automatically reconciling changes with remote backends once a connection is restored. The database distinguishes itself through a modular architecture that supports cross-environment synchronization and high-performance d
Transforms text data into numerical vector representations using machine learning models directly within the browser environment.
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
Converts facial images into numerical vector representations to quantify unique features for downstream analysis.
This project is a transformer-based framework for generating dense and sparse vector embeddings of text and multimodal data. It serves as a library for fine-tuning models to perform semantic similarity tasks, retrieval, and reranking. The system is distinguished by its support for diverse architectural patterns, including bi-encoders for fast similarity search and cross-encoders for high-precision reranking. It provides dedicated pipelines for multimodal embeddings, mapping text and images into a shared vector space, and implements knowledge distillation to compress large models into smaller,
Produces sparse vector representations of text to support efficient keyword-aware hybrid retrieval.
This project is a framework for training and deploying transformer-based models that map text, images, audio, and video into dense or sparse vector representations. It functions as a multimodal embedding library and semantic search engine used to retrieve relevant documents by calculating vector similarity between meanings. The framework provides specialized tools for both cross-encoder reranking, which calculates precise similarity scores to refine search results, and vector quantization to compress embedding vectors for reduced memory usage and increased retrieval speed. The project covers
Maps sentences and paragraphs to dense vector spaces for high-performance semantic search and clustering.
This project is a Neovim plugin that integrates large language models directly into the text editor to provide conversational programming assistance. It functions as an artificial intelligence coding assistant, enabling users to generate, refactor, and modify source code through natural language prompts and iterative chat sessions. The extension distinguishes itself by performing in-place code editing, where it applies suggestions directly to the active file buffer using precise diff-based patching. It supports agentic workflows by allowing models to execute shell commands and local scripts,
Generates vector embeddings for semantic search and context-aware retrieval across the codebase.
VideoLingo is an automated video localization suite designed to transcribe, translate, and dub video content. It functions as a translation pipeline that utilizes large language models to convert spoken audio into precise text segments and translate them into multiple languages. The system differentiates itself through a multi-step translation refinement process and a specialized natural language processing utility that segments text into single-line captions meeting broadcast standards. It also integrates synthetic voiceover generation to replace or augment original audio tracks. The projec
Uses NLP to segment transcribed text into readable subtitle lines following broadcast standards.
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
Generates vector representations for text and images to facilitate semantic search and memory.
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
Computes high-performance semantic vector representations of text using optimized and parallelized routines.