HKUDS/LightRAG

Features

• Knowledge Graph Retrieval Systems - Building search systems that map relationships between entities to provide context-aware answers from complex and interconnected document collections.
• Retrieval Augmented Generation Pipelines - Provides a modular workflow for connecting custom data sources to language models for context-aware responses.
• Graph Reasoning Systems - Structuring unstructured text into networks of entities to enable multi-hop analysis and synthesis of information across multiple documents.
• Knowledge Indexing - Structures unstructured text into entity-relationship networks to enable multi-hop reasoning across document collections.
• Hybrid Storage Engines - Integrates dense vector embeddings with structured knowledge graphs to facilitate both similarity-based and relationship-aware information retrieval.
• Hybrid Vector-Graph Databases - Integrates dense vector embeddings with structured knowledge graphs to facilitate similarity-based and relationship-aware information retrieval.
• Graph-Based Retrieval Frameworks - A structured data retrieval architecture that organizes information into knowledge graphs to improve context accuracy during language model generation.
• Multimodal Data Extractors - Parses text and visual data from diverse document formats to build searchable knowledge bases.
• Pipeline Orchestrators - Uses modular function chains to coordinate document parsing, knowledge extraction, and language model generation into repeatable automated workflows.
• Reasoning Engines - Synthesizes information across multiple documents to provide accurate responses to nuanced and multi-step user inquiries.
• Retrieval Strategies - Implements dual-level retrieval combining keyword matching and semantic graph traversal for context-aware information access.
• Knowledge Graph Retrieval - Building search systems that map relationships between entities to provide context-aware answers from large and interconnected document collections.
• Multimodal Encoders - Processes text and visual data through specialized encoders to unify disparate information sources into a single searchable representation.
• Orchestration Frameworks - Designing modular workflows that integrate document indexing and language model generation to solve domain-specific information retrieval challenges.
• GraphRAG Frameworks - Fast and simple retrieval-augmented generation implementation.
• Retrieval Augmented Generation - Simple and fast framework for retrieval-augmented generation.
• Databases and RAG - Simple and fast RAG framework.
• RAG Frameworks - Lightweight graph-based RAG implementation for efficient knowledge updates.
• Pipeline Orchestration Frameworks - Designing repeatable and automated workflows that connect custom data sources to language models for domain-specific information retrieval tasks.
• Multimodal Information Extractors - A processing engine that parses both text and visual data from diverse document formats to build comprehensive searchable knowledge bases.
• Incremental Indexing Engines - Updating large-scale information indexes dynamically as new data arrives without the need to perform a full system re-indexing.
• Incremental Updates - Modifies underlying knowledge structures dynamically as new data arrives without requiring full re-indexing.
• Multimodal Document Processing - Extract information from both text and images within diverse document types to improve the context and accuracy of answers generated by automated information retrieval systems.
• Multimodal Integration Tools - Combining text and visual information into a unified knowledge base to improve the accuracy of automated question answering systems.

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