Graphrag-Local-Ollama

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graphrag-local-ollama is an open-source AI Agents & Automation tool that adapts Microsoft's GraphRAG to support local models using Ollama. It enables cost-effective LLM and embedding extraction with models like Llama3, Mistral, Gemma2, and Phi3.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is graphrag-local-ollama?

GraphRAG Local Ollama is an open-source adaptation of Microsoft's GraphRAG, designed to leverage local models via Ollama for LLM and embedding extraction. This tool eliminates the dependency on costly OpenAPI models, offering a cost-effective solution for knowledge graph implementations. It supports a variety of local models such as Llama3, Mistral, Gemma2, and Phi3, and integrates with Ollama for both language models and embedding models like nomic-embed-text. The setup process is straightforward, involving conda environment creation, Ollama installation, repository cloning, and specific `pip install` commands. Users can easily configure models and run indexing and querying operations, with options to visualize generated graphs using tools like Gephi or a provided Python script.

Best used for

Ideal for developers and data scientists who need to implement Retrieval-Augmented Generation (RAG) with local models, build knowledge graphs from private text corpora, and reduce costs associated with external API calls. Especially valuable for those seeking to experiment with various LLMs and embedding models locally using Ollama.

Common actions

implement RAG

extract embeddings

build knowledge graphs

run local LLMs

process documents locally

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Capabilities

Key features

Local model support
Cost-effective inference
Ollama integration
LLM & embedding extraction
Knowledge graph generation
Graph visualization

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What local models can be used with graphrag-local-ollama?

graphrag-local-ollama supports various local LLM models like Llama3, Mistral, Gemma2, and Phi3, as well as embedding models such as mxbai-embed-large and nomic-embed-text, all available through Ollama. Users can pull any compatible model from the Ollama library for local use.

How does graphrag-local-ollama reduce costs compared to other RAG solutions?

By leveraging local models via Ollama, graphrag-local-ollama eliminates the need for expensive API calls to external language model providers like OpenAI. This allows users to perform LLM and embedding extraction on their own hardware, significantly reducing operational costs for RAG implementations.

Can I visualize the knowledge graphs generated by graphrag-local-ollama?

Yes, graphrag-local-ollama allows you to save generated graphs in GraphML format by enabling the 'graphml' option in the settings.yaml file. These GraphML files can then be visualized using external tools like Gephi or through a provided Python script in the repository.

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Research & Education › Knowledge Management Coding & Development › Open Source & Models

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