graphrag 安装以及踩坑关于graph-local-ollama（离线）安装过程以及中途遇到的问题：tiktok

安装过程

克隆graph-local-ollama仓库：

git clone https://github.com/TheAiSingularity/graphrag-local-ollama.git

进入项目目录：
```
cd graphrag-local-ollama/
```
安装依赖：
```
pip install -e .
```
创建graphrag 的一个项目，将输入输出文件夹创建好：
```
mkdir -p ./ragtest/input
cp input/* ./ragtest/input
```

初始化项目：

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

将设置的配置文件拷贝到项目内：
```
cp settings.yaml ./ragtest
```

修改配置文件：


encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: qwen-32b-instruct-fp16:latest
  model_supports_json: true # recommended if this is available for your model.
  # max_tokens: 4000
  # request_timeout: 180.0
  api_base: http://localhost:7889/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: bge-large-zh-v1.5:f16
    api_base: http://localhost:7889/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-times
    # concurrent_requests: 25 # 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: 300
  overlap: 100
  group_by_columns: [id] # by default, we don't allow chunks to cross documents
    
input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\.txt$"

cache:
  type: file # or blob
  base_dir: "cache"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

storage:
  type: file # or blob
  base_dir: "output/${timestamp}/artifacts"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  base_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 task
  prompt: "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 task
  prompt: "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: true
  prompt: "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 task
  prompt: "prompts/community_report.txt"
  max_length: 2000
  max_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: yes
  raw_entities: yes
  top_level_nodes: yes

local_search:
  # text_unit_prop: 0.5
  # community_prop: 0.1
  # conversation_history_max_turns: 5
  # top_k_mapped_entities: 10
  # top_k_relationships: 10
  # max_tokens: 12000

global_search:
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

关于配置文件，修改文件的模型名称和模型地址为本地，也可以修改为远程调用（如阿里云百炼）：

image-20250501151659835转存失败，建议直接上传图片文件

运行索引，创建图（直接运行下面的命令在离线的环境下不能成功，详见踩坑记录）：
```
python -m graphrag.index --root ./ragtest
```

运行结果

在以上命令都执行完之后，运行索引执行成功的结果如下图：

查询语句

python -m graphrag.query --root ./ragtest --method global "What is machine learning?"

运行查询，得到结果：

踩坑记录

关于 tiktoken

tiktoken 在没有编码器的情况下，自动请求tiktoken 的下载对应的文件。然而在离线环境下，这种下载是不可能成功的。所以需要手动下载来解决这个问题。

方法一（未验证）

在 tiktoken_ext/openai_public.py，文件中，如果blobpath 为 https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken，那么文件的hash就是9b5ad71b2ce5302211f9c61530b329a4922fc6a4

def cl100k_base():
    mergeable_ranks = load_tiktoken_bpe(
        "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken",
        expected_hash="223921b76ee99bde995b7ff738513eef100fb51d18c93597a113bcffe865b2a7",
    )

在 tiktoken/load.py 中，可以看到文件cache的存储路径，如果没有在环境变量中设置，默认使用的路径在Linux下是/tmp/data-gym-cache（可以自己验证路径）。那么找一台联网的机器，运行过tiktoken程序的机器，去找到 /tmp/data-gym-cache/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 文件，copy到另一台机器上相同路径下即可。

以上见原文链接：blog.csdn.net/zhilaizhiwa…

方法二（我采用）

在安装了tiktoken 包的路径下，有文件load.py，修改文件中的读取文件的逻辑：

from __future__ import annotations

import base64
import hashlib
import json
import os
import tempfile
import uuid
from typing import Optional

import requests

# 以下注释掉的是原本的read_file() 的逻辑
#def read_file(blobpath: str) -> bytes:
#    if not blobpath.startswith("http://") and not blobpath.startswith("https://"):
#        try:
#            import blobfile
#        except ImportError as e:
#            raise ImportError(
#                "blobfile is not installed. Please install it by running `pip install blobfile`."
#            ) from e
#        with blobfile.BlobFile(blobpath, "rb") as f:
#            return f.read()
    # avoiding blobfile for public files helps avoid auth issues, like MFA prompts
#    resp = requests.get(blobpath)
#    resp.raise_for_status()
#    return resp.content

def read_file(blobpath: str) -> bytes:
    blobpath = "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken"  
    cache_key = hashlib.sha1(blobpath.encode()).hexdigest()
    cache_dir = "/data/inspur/product/lbk-tests"
    cache_path = os.path.join(cache_dir, cache_key)
    with open(cache_path, "rb") as f:
        data = f.read()
    return data

其实上面就是将read_file()的逻辑从远程下载的逻辑改为从本地读取。本质上同方法一的底层逻辑一样，只不过方法一是从环境变量中读取。