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circlemind-ai avatar

circlemind-ai/fast-graphrag

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Fast Graphrag

Fast-GraphRAG is a system for generating and querying knowledge graphs from domain data. It uses a GraphRAG retrieval workflow to traverse structured data and isolate precise evidence for answering complex questions.

The project utilizes an agent-driven retrieval framework to coordinate the querying of knowledge graphs and the synthesis of final answers. It supports incremental data synchronization, allowing structured knowledge bases to be updated in real time as source information evolves.

The system integrates with API-compatible language models and embedding providers to power its data pipeline. It includes capabilities for retrieval accuracy benchmarking and state recovery through periodic data checkpoints.

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Features

  • Knowledge Graph Retrieval Systems - Uses knowledge graph traversal and ranking algorithms to isolate precise evidence for high-accuracy answers.
  • Agentic Retrieval Frameworks - Implements a promptable agent framework to coordinate knowledge graph querying and answer synthesis.
  • GraphRAG Frameworks - Implements a GraphRAG retrieval workflow to extract high-precision evidence from connected data.
  • Incremental Update Configurations - Refreshes knowledge base structures in real time to ensure retrieved information remains current.
  • Incremental Data Synchronization - Synchronizes structured knowledge bases in real time as source information evolves.
  • Incremental Indexing Engines - Supports dynamic updates to structured knowledge bases in real time as source information evolves.
  • Knowledge Graph Builders - Processes domain data into structured knowledge graphs to enable a navigable view of complex information.
  • Knowledge Graph Querying - Provides interfaces for retrieving accurate and dependable answers by searching over indexed knowledge graphs.
  • Search & Information Retrieval - Traverses connected information using ranking algorithms to identify relevant details and improve accuracy.
  • Retrieval Accuracy Metrics - Calculates retrieval success rates by comparing retrieved evidence against ground truth data.
  • LLM API Integrations - Integrates various API-compatible language models and embedding providers into the data retrieval system.
  • Model API Integrations - Provides a standardized communication layer to interface with various language models and embedding providers.
  • Retrieval Benchmarks - Benchmarks the effectiveness of the data pipeline by measuring how often queries find all required evidence.
  • Retrieval Accuracy Measurement - Measures the percentage of queries that successfully find all required evidence to validate the workflow.
  • GraphRAG Frameworks - Adaptive retrieval system for varied use cases and data.
  • Knowledge Retrieval - Adaptive RAG that adjusts to specific data and queries.
  • RAG Applications - Graph-based RAG framework for structured retrieval.
  • Retrieval Augmented Generation - Adaptive graph-based retrieval for specific use cases.
3,811 stars·247 forks·Python·MIT·23 views

Star history

Star history chart for circlemind-ai/fast-graphragStar history chart for circlemind-ai/fast-graphrag

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does circlemind-ai/fast-graphrag do?

Fast-GraphRAG is a system for generating and querying knowledge graphs from domain data. It uses a GraphRAG retrieval workflow to traverse structured data and isolate precise evidence for answering complex questions.

What are the main features of circlemind-ai/fast-graphrag?

The main features of circlemind-ai/fast-graphrag are: Knowledge Graph Retrieval Systems, Agentic Retrieval Frameworks, GraphRAG Frameworks, Incremental Update Configurations, Incremental Data Synchronization, Incremental Indexing Engines, Knowledge Graph Builders, Knowledge Graph Querying.

Which projects share features with circlemind-ai/fast-graphrag?

Projects with overlapping indexed features include: gusye1234/nano-graphrag — nano-graphrag is a retrieval system that uses knowledge graphs to provide structured context for large language model… hkuds/lightrag — LightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It… getzep/graphiti — Graphiti is a backend framework and memory server designed to provide artificial intelligence agents with persistent,… openspg/kag — KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework… explodinggradients/ragas — Ragas is an evaluation framework and performance benchmark designed to quantify the quality of retrieval augmented… infiniflow/ragflow — This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying…

Projects sharing features with Fast Graphrag

These projects share indexed features with Fast Graphrag. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • gusye1234/nano-graphraggusye1234 avatar

    gusye1234/nano-graphrag

    3,896View on GitHub↗

    nano-graphrag is a retrieval system that uses knowledge graphs to provide structured context for large language model responses. It functions as a knowledge graph indexer that transforms unstructured text into a network of entities and relationships, as well as a hybrid graph retrieval system. The project differentiates itself by combining local neighborhood searches with global community summaries to answer complex natural language questions. It includes a knowledge graph visualizer that generates HTML representations of entities and their relationships to map indexed knowledge. The framewo

    Python
    View on GitHub↗3,896
  • hkuds/lightragHKUDS avatar

    HKUDS/LightRAG

    36,651View on GitHub↗

    LightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It structures unstructured text into knowledge graphs, enabling multi-hop reasoning and complex query synthesis across large document collections. By integrating dense vector embeddings with structured knowledge graphs, the system facilitates both similarity-based and relationship-aware information retrieval. The framework distinguishes itself through a dual-level retrieval strategy that combines low-level keyword matching with high-level semantic graph traversal to capture both specific

    Pythongenaigptgpt-4
    View on GitHub↗36,651
  • getzep/graphitigetzep avatar

    getzep/graphiti

    22,936View on GitHub↗

    Graphiti is a backend framework and memory server designed to provide artificial intelligence agents with persistent, time-aware knowledge graph storage. It functions as a memory layer that enables agents to maintain context across long-term interactions by recording and evolving structured data over time. The system distinguishes itself through a specialized temporal graph database that tracks how entities and relationships change using validity windows. By combining semantic vector similarity, keyword matching, and graph topology traversal, the engine performs hybrid retrieval to locate rel

    Pythonagentsgraphllms
    View on GitHub↗22,936
  • openspg/kagOpenSPG avatar

    OpenSPG/KAG

    8,548View on GitHub↗

    KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework that integrates large language models with graph retrieval and numerical calculation to resolve natural language queries. The system creates unified knowledge representations by aligning unstructured data and expert rules through semantic mapping. It maintains mutual indexing between graph structures and original text blocks to ensure that reasoning processes remain linked to verifiable source data. The project provides capabilities for semantic information integration, grap

    Pythonknowledge-graphlarge-language-modellogical-reasoning
    View on GitHub↗8,548
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