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README.md

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+# GraphRAG Local Ollama
+
+This repository is an adaptation of Microsoft's [GraphRAG](https://github.com/microsoft/graphrag), modified to support local models downloaded using Ollama, thus eliminating the dependency on costly OpenAPI models for inference.
+
+## Installation and Setup
+
+Follow these steps to set up and use this repository with local models for LLM and embeddings:
+
+1. **Create and activate a new conda environment:**
+    ```bash
+    conda create -n graphrag-ollama-local python=3.10
+    conda activate graphrag-ollama-local
+    ```
+
+2. **Install Ollama:**
+    - Visit [Ollama's website](https://ollama.com/) for installation instructions.
+    - After installation, run:
+    ```bash
+    pip install ollama
+    ```
+
+3. **Download the required models using Ollama:**
+    ```bash
+    ollama pull mistral
+    ollama pull nomic-embed-text
+    ```
+
+4. **Clone the repository:**
+    ```bash
+    git clone https://github.com/TheAiSingularity/graphrag-local-ollama.git
+    ```
+
+5. **Install the necessary packages:**
+    ```bash
+    pip install -e .
+    ```
+
+6. **Navigate to the repository directory:**
+    ```bash
+    cd graphrag-local-ollama/
+    ```
+
+7. **Create the required input directory:**
+    ```bash
+    mkdir -p ./ragtest/input
+    ```
+
+8. **Initialize the indexing:**
+    ```bash
+    python3 -m graphrag.index --init --root ./ragtest
+    ```
+
+9. **Copy input files:**
+    ```bash
+    cp inputs/* ./ragtest/input
+    ```
+
+10. **Move the settings file:**
+    ```bash
+    mv settings.yaml ./ragtest
+    ```
+
+11. **Run the indexing:**
+    ```bash
+    python3 -m graphrag.index --root ./ragtest
+    ```
+
+12. **Run a query:**
+    ```bash
+    python3 -m graphrag.query --root ./ragtest --method global "explain machine learning"
+    ```
+
+## Citations
+
+- Original GraphRAG repository by Microsoft: [GraphRAG](https://github.com/microsoft/graphrag)
+- Ollama: [Ollama](https://ollama.com/)
+
+---
+
+By following the above steps, you can set up and use local models with GraphRAG, making the process more cost-effective and efficient.