GraphRAG+Ollama 本地部署，保姆教程，踩坑无数，闭坑大法

mkdir -p ./ragtest/input

python -m graphrag.index --init --root ./ragtest1

quentinz/bge-large-zh-v1.5:latestgemma2:9b

encoding_model: cl100k_baseskip_workflows: []llm:api_key: ollamatype: openai_chat # or azure_openai_chatmodel: gemma2:9b # 你ollama中的本地llm模型，可以换成其他的，只要你安装了就可以model_supports_json: true # recommended if this is available for your model.max_tokens: 2048# request_timeout: 180.0api_base: http://localhost:11434/v1 # 接口注意是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-timesconcurrent_requests: 1 # 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 embeddingsasync_mode: threaded # or asynciollm:api_key: ollamatype: openai_embedding # or azure_openai_embeddingmodel: quentinz/bge-large-zh-v1.5:latest #你ollama中的本地embeding模型，可以换成其他的，只要你安装了就可以api_base: http://localhost:11434/api # 注意是 api# 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-timesconcurrent_requests: 1 # the number of parallel inflight requests that may be made# batch_size: 16 # the number of documents to send in a single request# batch_max_tokens: 8191 # the maximum number of tokens to send in a single request# target: required # or optional

chunks:size: 300overlap: 100group_by_columns: [id] # by default, we don't allow chunks to cross documentsinput:type: file # or blobfile_type: text # or csvbase_dir: "input"file_encoding: utf-8file_pattern: ".*\\.txt$"
cache:type: file # or blobbase_dir: "cache"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>
storage:type: file # or blobbase_dir: "output/${timestamp}/artifacts"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>
reporting:type: file # or console, blobbase_dir: "output/${timestamp}/reports"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>
entity_extraction:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this taskprompt: "prompts/entity_extraction.txt"entity_types: [organization,person,geo,event]max_gleanings: 0
summarize_descriptions:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this taskprompt: "prompts/summarize_descriptions.txt"max_length: 500
claim_extraction:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this task# enabled: trueprompt: "prompts/claim_extraction.txt"description: "Any claims or facts that could be relevant to information discovery."max_gleanings: 0
community_report:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this taskprompt: "prompts/community_report.txt"max_length: 2000max_input_length: 8000
cluster_graph:max_cluster_size: 10
embed_graph:enabled: false # if true, will generate node2vec embeddings for nodes# num_walks: 10# walk_length: 40# window_size: 2# iterations: 3# random_seed: 597832
umap:enabled: false # if true, will generate UMAP embeddings for nodes
snapshots:graphml: falseraw_entities: falsetop_level_nodes: false
local_search:# text_unit_prop: 0.5# community_prop: 0.1# conversation_history_max_turns: 5# top_k_mapped_entities: 10# top_k_relationships: 10max_tokens: 5000
global_search:max_tokens: 5000# data_max_tokens: 12000# map_max_tokens: 1000# reduce_max_tokens: 2000# concurrency: 32

python -m graphrag.index --root ./ragtest1

# Copyright (c) 2024 Microsoft Corporation.# Licensed under the MIT License
"""The EmbeddingsLLM class."""
from typing_extensions import Unpack
from graphrag.llm.base import BaseLLMfrom graphrag.llm.types import (EmbeddingInput,EmbeddingOutput,LLMInput,)
from .openai_configuration import OpenAIConfigurationfrom .types import OpenAIClientTypesimport ollama

class OpenAIEmbeddingsLLM(BaseLLM[EmbeddingInput, EmbeddingOutput]):"""A text-embedding generator LLM."""
_client: OpenAIClientTypes_configuration: OpenAIConfiguration
def __init__(self, client: OpenAIClientTypes, configuration: OpenAIConfiguration):self.client = clientself.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="quentinz/bge-large-zh-v1.5:latest",prompt=inp)embedding_list.append(embedding["embedding"])return embedding_list# embedding = await self.client.embeddings.create(# input=input,# **args,# )# return [d.embedding for d in embedding.data]

embedding = ollama.embeddings(model="quentinz/bge-large-zh-v1.5:latest",prompt=inp)

# Copyright (c) 2024 Microsoft Corporation.# Licensed under the MIT License
"""OpenAI Embedding model implementation."""
import asynciofrom collections.abc import Callablefrom typing import Any
import numpy as npimport tiktokenfrom tenacity import (AsyncRetrying,RetryError,Retrying,retry_if_exception_type,stop_after_attempt,wait_exponential_jitter,)
from graphrag.query.llm.base import BaseTextEmbeddingfrom graphrag.query.llm.oai.base import OpenAILLMImplfrom graphrag.query.llm.oai.typing import (OPENAI_RETRY_ERROR_TYPES,OpenaiApiType,)from graphrag.query.llm.text_utils import chunk_textfrom graphrag.query.progress import StatusReporter
from langchain_community.embeddings import OllamaEmbeddings


class OpenAIEmbedding(BaseTextEmbedding, OpenAILLMImpl):"""Wrapper for OpenAI Embedding models."""
def __init__(self,api_key: str | None = None,azure_ad_token_provider: Callable | None = None,model: str = "text-embedding-3-small",deployment_name: str | None = None,api_base: str | None = None,api_version: str | None = None,api_type: OpenaiApiType = OpenaiApiType.OpenAI,organization: str | None = None,encoding_name: str = "cl100k_base",max_tokens: int = 8191,max_retries: int = 10,request_timeout: float = 180.0,retry_error_types: tuple[type[BaseException]] = OPENAI_RETRY_ERROR_TYPES,# type: ignorereporter: StatusReporter | None = None,):OpenAILLMImpl.__init__(self=self,api_key=api_key,azure_ad_token_provider=azure_ad_token_provider,deployment_name=deployment_name,api_base=api_base,api_version=api_version,api_type=api_type,# type: ignoreorganization=organization,max_retries=max_retries,request_timeout=request_timeout,reporter=reporter,)
self.model = modelself.encoding_name = encoding_nameself.max_tokens = max_tokensself.token_encoder = tiktoken.get_encoding(self.encoding_name)self.retry_error_types = retry_error_types
def embed(self, text: str, **kwargs: Any) -> list[float]:"""Embed text using OpenAI Embedding's sync function.
For text longer than max_tokens, chunk texts into max_tokens, embed each chunk, then combine using weighted average.Please refer to: https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb"""token_chunks = chunk_text(text=text, token_encoder=self.token_encoder, max_tokens=self.max_tokens)chunk_embeddings = []chunk_lens = []for chunk in token_chunks:try:embedding, chunk_len = self._embed_with_retry(chunk, **kwargs)chunk_embeddings.append(embedding)chunk_lens.append(chunk_len)# TODO: catch a more specific exceptionexcept Exception as e:# noqa BLE001self._reporter.error(message="Error embedding chunk",details={self.__class__.__name__: str(e)},)
continuechunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens)chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings)return chunk_embeddings.tolist()
async def aembed(self, text: str, **kwargs: Any) -> list[float]:"""Embed text using OpenAI Embedding's async function.
For text longer than max_tokens, chunk texts into max_tokens, embed each chunk, then combine using weighted average."""token_chunks = chunk_text(text=text, token_encoder=self.token_encoder, max_tokens=self.max_tokens)chunk_embeddings = []chunk_lens = []embedding_results = await asyncio.gather(*[self._aembed_with_retry(chunk, **kwargs) for chunk in token_chunks])embedding_results = [result for result in embedding_results if result[0]]chunk_embeddings = [result[0] for result in embedding_results]chunk_lens = [result[1] for result in embedding_results]chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens)# type: ignorechunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings)return chunk_embeddings.tolist()
def _embed_with_retry(self, text: str | tuple, **kwargs: Any) -> tuple[list[float], int]:try:retryer = Retrying(stop=stop_after_attempt(self.max_retries),wait=wait_exponential_jitter(max=10),reraise=True,retry=retry_if_exception_type(self.retry_error_types),)for attempt in retryer:with attempt:embedding = (OllamaEmbeddings(model=self.model,).embed_query(text)or [])return (embedding, len(text))except RetryError as e:self._reporter.error(message="Error at embed_with_retry()",details={self.__class__.__name__: str(e)},)return ([], 0)else:# TODO: why not just throw in this case?return ([], 0)
async def _aembed_with_retry(self, text: str | tuple, **kwargs: Any) -> tuple[list[float], int]:try:retryer = AsyncRetrying(stop=stop_after_attempt(self.max_retries),wait=wait_exponential_jitter(max=10),reraise=True,retry=retry_if_exception_type(self.retry_error_types),)async for attempt in retryer:with attempt:embedding = (await OllamaEmbeddings(model=self.model,).embed_query(text) or [] )return (embedding, len(text))except RetryError as e:self._reporter.error(message="Error at embed_with_retry()",details={self.__class__.__name__: str(e)},)return ([], 0)else:# TODO: why not just throw in this case?return ([], 0)

python -m graphrag.query --root ./ragtest1 --method local "人卫社的网址"

python -m graphrag.query --root ./ragtest1 --method global "人卫社的网址"