Install Microsoft GraphRAG with Ollama Locally

 This video is a step-by-step tutorial to install Microsoft GraphRAG with Ollama models with your own data.

Commands Used:

conda create -n graphollama python=3.11 -y && conda activate graphollama

pip install ollama

ollama pull mistral

ollama pull nomic-embed-text

pip install graphrag

mkdir -p ./ragtest/input

cp fahd.txt ragtest/input

python3 -m graphrag.index --init --root ./ragtest

cd ragtest , vi settings

sudo find / -name openai_embeddings_llm.py

python3 -m graphrag.index --root ./ragtest

python3 -m graphrag.query --root ./ragtest --method global "Who is Fahd Mirza?"

Files Used:

#settings.yaml

encoding_model: cl100k_base

skip_workflows: []

llm:

  api_key: ${GRAPHRAG_API_KEY}

  type: openai_chat # or azure_openai_chat

  model: mistral

  model_supports_json: true # recommended if this is available for your model.

  # max_tokens: 4000

  # request_timeout: 180.0

api_base: http://localhost:11434/v1

  # api_version: 2024-02-15-preview

  # organization: <organization_id>

  # deployment_name: <azure_model_deployment_name>

  # tokens_per_minute: 150_000 # set a leaky bucket throttle

  # requests_per_minute: 10_000 # set a leaky bucket throttle

  # max_retries: 10

  # max_retry_wait: 10.0

  # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times

  # concurrent_requests: 25 # the number of parallel inflight requests that may be made

parallelization:

  stagger: 0.3

  # num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:

  ## parallelization: override the global parallelization settings for embeddings

  async_mode: threaded # or asyncio

  llm:

    api_key: ${GRAPHRAG_API_KEY}

    type: openai_embedding # or azure_openai_embedding

    model: nomic_embed_text

    api_base: http://localhost:11434/api

    # api_version: 2024-02-15-preview

#openai_embeddings_llm.py

from typing_extensions import Unpack

from graphrag.llm.base import BaseLLM

from graphrag.llm.types import (

    EmbeddingInput,

    EmbeddingOutput,

    LLMInput,

)

from .openai_configuration import OpenAIConfiguration

from .types import OpenAIClientTypes

import ollama

class OpenAIEmbeddingsLLM(BaseLLM[EmbeddingInput, EmbeddingOutput]):

    _client: OpenAIClientTypes

    _configuration: OpenAIConfiguration

    def __init__(self, client: OpenAIClientTypes, configuration: OpenAIConfiguration):

        self._client = client

        self._configuration = configuration

    async def _execute_llm(

        self, input: EmbeddingInput, **kwargs: Unpack[LLMInput]

    ) -> EmbeddingOutput | None:

        args = {

            "model": self._configuration.model,

            **(kwargs.get("model_parameters") or {}),

        }

        embedding_list = []

        for inp in input:

            embedding = ollama.embeddings(model="nomic-embed-text", prompt=inp)

            embedding_list.append(embedding["embedding"])

        return embedding_list