本机Graphrag+ollma跑通

Conda 环境

conda create -n graphrag-local python=3.10 #Python 3.10-3.12
conda activate graphrag-local

1 2	conda create -n graphrag-local python=3.10 #Python 3.10-3.12 conda activate graphrag-local

Ollama配置

curl -fsSL https://ollama.com/install.sh | sh #ollama for linux
pip install ollama

1 2	curl -fsSL https://ollama.com/install.sh \| sh #ollama for linux pip install ollama

通过Ollama下载需要的大语言模型以及嵌入模型，这里用的阿里千问以及nomic。

ollama pull qwen:7b #llm
ollama pull nomic-embed-text #embedding

1 2	ollama pull qwen:7b #llm ollama pull nomic-embed-text #embedding

代码下载以及依赖安装

git clone https://github.com/microsoft/graphrag.git
cd graphrag
pip install -e .

git clone https://github.com/microsoft/graphrag.git

cd graphrag

pip install -e .

环境配置

创建一个目录用于存放输入的文本数据集，例如txt，csv文档。

mkdir -p ./ragtest/input

1	mkdir -p ./ragtest/input

初始化./ragtest目录用于生成默认环境配置文件。

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

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

./ragtest目录下settings.yaml为默认配置文件，配的是Chatgpt模型，由于我们通过Ollama使用本地大模型，所以需要修改配置，可以使用如下内容直接替换settings.yaml中的配置：

encoding_model: cl100k_base
skip_workflows: []
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_chat # or azure_openai_chat
model: qwen2:7b-instruct
model_supports_json: true # recommended if this is available for your model.
# max_tokens: 4000
# request_timeout: 180.0
api_base: http://127.0.0.1:11434/v1
# api_version: 2024-02-15-preview
# organization:
# 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
# temperature: 0 # temperature for sampling
# top_p: 1 # top-p sampling
# n: 1 # Number of completions to generate

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://127.0.0.1:11434/v1
# api_version: 2024-02-15-preview
# organization:
# 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:
# container_name:

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

reporting:
type: file # or console, blob
base_dir: "output/${timestamp}/reports"
# connection_string:
# 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
# llm_temperature: 0 # temperature for sampling
# llm_top_p: 1 # top-p sampling
# llm_n: 1 # Number of completions to generate
# max_tokens: 12000

global_search:
# llm_temperature: 0 # temperature for sampling
# llm_top_p: 1 # top-p sampling
# llm_n: 1 # Number of completions to generate
# max_tokens: 12000
# data_max_tokens: 12000
# map_max_tokens: 1000
# reduce_max_tokens: 2000
# concurrency: 32

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encoding_model: cl100k_base

skip_workflows: []

llm:

api_key: ${GRAPHRAG_API_KEY}

type: openai_chat # or azure_openai_chat

model: qwen2:7b-instruct

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

# max_tokens: 4000

# request_timeout: 180.0

api_base: http://127.0.0.1:11434/v1

# api_version: 2024-02-15-preview

# organization:

# 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

# temperature: 0 # temperature for sampling

# top_p: 1 # top-p sampling

# n: 1 # Number of completions to generate

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://127.0.0.1:11434/v1

# api_version: 2024-02-15-preview

# organization:

# 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:

# container_name:

storage:

type: file # or blob

base_dir: "output/${timestamp}/artifacts"

# connection_string:

# container_name:

reporting:

type: file # or console, blob

base_dir: "output/${timestamp}/reports"

# connection_string:

# 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

# llm_temperature: 0 # temperature for sampling

# llm_top_p: 1 # top-p sampling

# llm_n: 1 # Number of completions to generate

# max_tokens: 12000

global_search:

# llm_temperature: 0 # temperature for sampling

# llm_top_p: 1 # top-p sampling

# llm_n: 1 # Number of completions to generate

# max_tokens: 12000

# data_max_tokens: 12000

# map_max_tokens: 1000

# reduce_max_tokens: 2000

# concurrency: 32

修改内容如下：

diff --git a/settings.yaml b/settings.yaml
index 0a7f255..ffb4fbe 100644
--- a/settings.yaml
+++ b/settings.yaml
@@ -4,11 +4,11 @@ skip_workflows: []
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_chat # or azure_openai_chat
- model: gpt-4-turbo-preview
+ model: qwen2:7b-instruct
model_supports_json: true # recommended if this is available for your model.
# max_tokens: 4000
# request_timeout: 180.0
- # api_base: https://.openai.azure.com
+ api_base: http://127.0.0.1:11434/v1
# api_version: 2024-02-15-preview
# organization:
# deployment_name:
@@ -34,8 +34,8 @@ embeddings:
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_embedding # or azure_openai_embedding
- model: text-embedding-3-small
- # api_base: https://.openai.azure.com
+ model: nomic-embed-text
+ api_base: http://127.0.0.1:11434/v1
# api_version: 2024-02-15-preview
# organization:
# deployment_name:
@@ -52,7 +52,7 @@ embeddings:

chunks:
- size: 1200
+ size: 300
overlap: 100
group_by_columns: [id] # by default, we don't allow chunks to cross documents

@@ -87,7 +87,7 @@ entity_extraction:
## async_mode: override the global async_mode settings for this task
prompt: "prompts/entity_extraction.txt"
entity_types: [organization,person,geo,event]
- max_gleanings: 1
+ max_gleanings: 0

summarize_descriptions:
## llm: override the global llm settings for this task
@@ -103,9 +103,9 @@ claim_extraction:
# enabled: true
prompt: "prompts/claim_extraction.txt"
description: "Any claims or facts that could be relevant to information discovery."
- max_gleanings: 1
+ max_gleanings: 0

-community_reports:
+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
@@ -128,9 +128,9 @@ umap:
enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
- graphml: false
- raw_entities: false
- top_level_nodes: false
+ graphml: yes
+ raw_entities: yes
+ top_level_nodes: yes

local_search:
# text_unit_prop: 0.5

diff --git a/settings.yaml b/settings.yaml

index 0a7f255..ffb4fbe 100644

--- a/settings.yaml

+++ b/settings.yaml

@@ -4,11 +4,11 @@ skip_workflows: []

llm:

api_key: ${GRAPHRAG_API_KEY}

type: openai_chat # or azure_openai_chat

- model: gpt-4-turbo-preview

+ model: qwen2:7b-instruct

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

# max_tokens: 4000

# request_timeout: 180.0

- # api_base: https://.openai.azure.com

+ api_base: http://127.0.0.1:11434/v1

# api_version: 2024-02-15-preview

# organization:

# deployment_name:

@@ -34,8 +34,8 @@ embeddings:

llm:

api_key: ${GRAPHRAG_API_KEY}

type: openai_embedding # or azure_openai_embedding

- model: text-embedding-3-small

- # api_base: https://.openai.azure.com

+ model: nomic-embed-text

+ api_base: http://127.0.0.1:11434/v1

# api_version: 2024-02-15-preview

# organization:

# deployment_name:

@@ -52,7 +52,7 @@ embeddings:

chunks:

- size: 1200

+ size: 300

overlap: 100

group_by_columns: [id] # by default, we don't allow chunks to cross documents

@@ -87,7 +87,7 @@ entity_extraction:

## async_mode: override the global async_mode settings for this task

prompt: "prompts/entity_extraction.txt"

entity_types: [organization,person,geo,event]

- max_gleanings: 1

+ max_gleanings: 0

summarize_descriptions:

## llm: override the global llm settings for this task

@@ -103,9 +103,9 @@ claim_extraction:

# enabled: true

prompt: "prompts/claim_extraction.txt"

description: "Any claims or facts that could be relevant to information discovery."

- max_gleanings: 1

+ max_gleanings: 0

-community_reports:

+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

@@ -128,9 +128,9 @@ umap:

enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:

- graphml: false

- raw_entities: false

- top_level_nodes: false

+ graphml: yes

+ raw_entities: yes

+ top_level_nodes: yes

local_search:

# text_unit_prop: 0.5

配置文件参数说明参考：https://microsoft.github.io/graphrag/posts/config/json_yaml/

提示词微调

在当前工作目录ragtest下，用于大模型提取实体以及关系等的提示词默认存放在prompts子目录下，可通过settings.yaml修改提示词目录。对于特定领域，默认提示词模板表现不佳。这里我们可以通过官方提供的方法进行提示词微调，替换默认提示词模板，从而提高在特定领域上的表现。

自动模板

可以通过配置domain，language等参数，生成符合我们要求的提示词模板，如下是针对某电影拍摄书籍的一个自动模板提示词微调示例。这样就会在prompt目录下生成新的提示词模板，更符合拍摄领域。

python -m graphrag.prompt_tune --config CONFIG --root ./ragtest --domain "a book about how to shoot a film" --language Chinese --chunk-size 300 --output prompt

1	python -m graphrag.prompt_tune --config CONFIG --root ./ragtest --domain "a book about how to shoot a film" --language Chinese --chunk-size 300 --output prompt

详细参数配置可以参考：https://microsoft.github.io/graphrag/posts/prompt_tuning/auto_prompt_tuning/

手动提示词微调

按照规范自己写一个提示词模板，参考：https://microsoft.github.io/graphrag/posts/prompt_tuning/manual_prompt_tuning/

执行索引

这一步会通过大模型提取实体，关系等，构建知识图谱，耗时较久。

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

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

搜索

索引阶段提取的结构被用来提供材料，作为LLM的context来回答问题。查询模式包括本地和全局的搜索：

本地搜索：通过图谱中实体关联信息和原始文档相关文本块来推理关于特定实体的问题
全局搜索：通过社区的总结来推理关于语料库整体问题的答案

本地搜索

python -m graphrag.query --root ./ragtest --method local "test test"

1	python -m graphrag.query --root ./ragtest --method local "test test"

全局搜索

python -m graphrag.query --root ./ragtest --method global "test test"

1	python -m graphrag.query --root ./ragtest --method global "test test"

参考

[1] https://microsoft.github.io/graphrag/posts/query/overview/

本作品采用知识共享署名-非商业性使用-禁止演绎 4.0 国际许可协议进行许可

本机Graphrag+ollma跑通

Conda 环境

Ollama配置

代码下载以及依赖安装

环境配置

提示词微调

自动模板

手动提示词微调

执行索引

搜索

本地搜索

全局搜索

参考

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