#go #classification #contextual_search #database #deep_learning #deep_search #graphql #knn_search #machine_learning #neural_search #restful_api #search_engine #search_engines #semantic_search #semantic_search_engine #vector_database #vector_search #vector_search_engine #vectors #weaviate

https://github.com/semi-technologies/weaviate

#rust #search_engine #machine_learning #algorithm #neural_network #high_performance #artificial_intelligence #simd #data_structures #image_search #recommender_system #approximate_nearest_neighbor_search #similarity_search #k_nearest_neighbors #hnsw #vector_search

https://github.com/hora-search/hora

#rust #approximate_nearest_neighbor_search #embeddings_similarity #hnsw #image_search #knn_algorithm #machine_learning #matching #mlops #nearest_neighbor_search #neural_network #neural_search #recommender_system #search #search_engine #search_engines #similarity_search #vector_database #vector_search #vector_search_engine

https://github.com/qdrant/qdrant

#python #cross_modal #data_structures #dataclass #deep_learning #docarray #elasticsearch #graphql #multi_modal #multimodal #nearest_neighbor_search #nested_data #neural_search #protobuf #qdrant #semantic_search #sqlite #unstructured_data #vector_search #weaviate

https://github.com/docarray/docarray

#python #chatgpt #clip #deep_learning #gpt #hacktoberfest #hnsw #information_retrieval #knn #large_language_models #machine_learning #machinelearning #multi_modal #natural_language_processing #search_engine #semantic_search #tensor_search #transformers #vector_search #vision_language #visual_search

https://github.com/marqo-ai/marqo

#python #embeddings #information_retrieval #language_model #large_language_models #llm #machine_learning #nearest_neighbor_search #neural_search #nlp #search #search_engine #semantic_search #sentence_embeddings #similarity_search #transformers #txtai #vector_database #vector_search #vector_search_engine

https://github.com/neuml/txtai

#python #ai #data #data_structures #database #long_term_memory #machine_learning #ml #mlops #mongodb #pytorch #scikit_learn #sklearn #torch #transformers #vector_search

https://github.com/SuperDuperDB/superduperdb

#go #approximate_nearest_neighbor_search #generative_search #grpc #hnsw #hybrid_search #image_search #information_retrieval #mlops #nearest_neighbor_search #neural_search #recommender_system #search_engine #semantic_search #semantic_search_engine #similarity_search #vector_database #vector_search #vector_search_engine #vectors #weaviate

Weaviate is a powerful, open-source vector database that uses machine learning to make your data searchable. It's fast, scalable, and flexible, allowing you to vectorize your data at import or upload your own vectors. Weaviate supports various modules for integrating with popular AI services like OpenAI, Cohere, and Hugging Face. It's designed for production use with features like scaling, replication, and security. You can use Weaviate for tasks beyond search, such as recommendations, summarization, and integration with neural search frameworks. It offers APIs in GraphQL, REST, and gRPC and has client libraries for several programming languages. This makes it easy to build applications like chatbots, recommendation systems, and image search tools quickly and efficiently. Joining the Weaviate community provides access to tutorials, demos, blogs, and forums to help you get started and stay updated.

https://github.com/weaviate/weaviate

#go #anns #cloud_native #distributed #embedding_database #embedding_similarity #embedding_store #faiss #golang #hnsw #image_search #llm #nearest_neighbor_search #tensor_database #vector_database #vector_search #vector_similarity #vector_store

Milvus is an open-source vector database designed for embedding similarity search and AI applications. It makes unstructured data search more accessible and provides a consistent user experience across different deployment environments. Key features include millisecond search on trillion vector datasets, simplified unstructured data management, reliable and always-on operations, high scalability, and hybrid search capabilities. Milvus is cloud-native, supports multiple SDKs, and has a strong community with extensive documentation and support channels like Discord and mailing lists. Using Milvus benefits users by enabling fast and efficient vector searches, simplifying data management, and ensuring reliability and scalability in their applications.

https://github.com/milvus-io/milvus

#python #applicant_tracking_system #ats #hacktoberfest #machine_learning #natural_language_processing #nextjs #python #resume #resume_builder #resume_parser #text_similarity #typescript #vector_search #word_embeddings

Resume Matcher is a free and open-source tool that helps you tailor your resume to a job description. It uses AI to extract important keywords from the job description and matches them with your resume, improving its readability and making it more likely to pass through applicant tracking systems (ATS). Here’s how it benefits you: it analyzes your resume and job descriptions, identifies key terms, and suggests improvements to increase your chances of getting noticed by employers. This tool is easy to install and use, and it's available for free, making it a valuable resource for anyone looking to enhance their job application process.

https://github.com/srbhr/Resume-Matcher

#cplusplus #cache #cpp #database #fibers #in_memory #in_memory_database #key_value #keydb #memcached #message_broker #multi_threading #nosql #redis #valkey #vector_search

Dragonfly is a modern in-memory data store compatible with Redis and Memcached, offering up to 25 times higher throughput and better cache efficiency while using up to 80% fewer resources. It scales well with larger servers, supports many Redis commands, and features a unique, memory-efficient cache and fast snapshotting. Dragonfly provides low latency, high performance, and is easy to configure with familiar Redis options. Its design ensures atomic operations and efficient resource use, making it ideal for fast, cost-effective cloud applications needing real-time data access and high scalability. This means you get faster, more efficient caching and data handling with minimal changes to your existing setup[5][2][4].

https://github.com/dragonflydb/dragonfly

#go #agent #agentic #ai #chatbot #chatbots #embeddings #evaluation #generative_ai #golang #knowledge_base #llm #multi_tenant #multimodel #ollama #openai #question_answering #rag #reranking #semantic_search #vector_search

WeKnora is a powerful tool that helps you understand and find answers in complex documents like PDFs and Word files. It uses advanced AI to read documents, understand what they mean, and answer your questions in a simple way. This tool is useful for businesses and researchers because it can quickly find information from many documents, making it easier to manage knowledge and make decisions. It also supports multiple languages and can be used privately, ensuring your data stays safe.

https://github.com/Tencent/WeKnora

#python #ai #faiss #gpt_oss #langchain #llama_index #llm #localstorage #offline_first #ollama #privacy #python #rag #retrieval_augmented_generation #vector_database #vector_search #vectors

LEANN is a tiny, powerful vector database that lets you turn your laptop into a personal AI assistant capable of searching millions of documents using 97% less storage than traditional systems without losing accuracy. It works by storing a compact graph and computing embeddings only when needed, saving huge space and keeping your data private on your device. You can search your files, emails, browser history, chat logs, live data from platforms like Slack and Twitter, and even codebases—all locally without cloud costs. This means fast, private, and efficient AI-powered search and retrieval on your own laptop.

https://github.com/yichuan-w/LEANN