3 repositorios
Awesome GitHub RepositoriesVector Search Frameworks
Specialized tools for low-latency retrieval of vector data in AI and RAG applications.
Explore 3 awesome GitHub repositories matching data & databases · Vector Search Frameworks. Refine with filters or upvote what's useful.
Awesome Vector Search Frameworks GitHub Repositories
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- pathwaycom/llm-app
pathwaycom/llm-app
59,341Ver en GitHubThis project is a data processing engine and AI application platform designed for building production-grade machine learning workflows. It provides a unified programming model that handles both historical batch data and live stream ingestion, enabling the development of real-time ETL pipelines and scalable data transformation workflows. The framework distinguishes itself through differential dataflow execution, which propagates only changes through a pipeline rather than recomputing entire datasets. It supports distributed state management across worker nodes and utilizes incremental stream p
Supports low-latency retrieval of evolving knowledge bases for retrieval-augmented generation applications.
Jupyter Notebookchatbothugging-facellm - piskvorky/gensim
piskvorky/gensim
16,361Ver en GitHubGensim 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
Provides a framework for training and managing high-dimensional semantic vector representations using optimized machine learning routines.
Pythondata-miningdata-sciencedocument-similarity - superlinked/superlinked
superlinked/superlinked
Superlinked is a development framework designed for building semantic search and retrieval pipelines. It functions as a machine learning data pipeline and semantic retrieval engine, providing the tools necessary to unify data schema definition, embedding generation, and vector database integration within a single application. The framework distinguishes itself by acting as a vector database orchestrator that manages the lifecycle of machine learning models alongside complex search logic. It enables developers to construct structured data models that map raw content and metadata into unified r
Provides a unified development framework for building semantic search and retrieval pipelines.
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