安装
pip install graphrag
mkdir -p ./ragtest/input
#将文档拷贝至 ./ragtest/input/ 下
python -m graphrag.index --init --root ./ragtest
修改settings.yaml
encoding_model: cl100k_base
skip_workflows: []
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_chat # or azure_openai_chat
model: qwen2-instruct
model_supports_json: true # recommended if this is available for your model.
# max_tokens: 4000
# request_timeout: 180.0
api_base: http://192.168.2.2:9997/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: 5 # 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: ${GRAPHRAG_API_KEY}
type: openai_embedding # or azure_openai_embedding
model: bge-large-zh-v1.5
api_base: http://127.0.0.1:9997/v1/
# api_version: 2024-02-15-preview
# organization: <organization_id>
# deployment_name: <azure_model_deployment_name>
# tokens_per_minute: 150_000 # set a leaky bucket throttle
# requests_per_minute: 10_000 # set a leaky bucket throttle
# max_retries: 10
# max_retry_wait: 10.0
# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
# concurrent_requests: 25 # the number of parallel inflight requests that may be made
# batch_size: 16 # the number of documents to send in a single request
# batch_max_tokens: 8191 # the maximum number of tokens to send in a single request
# target: required # or optional
chunks:
size: 300
overlap: 100
group_by_columns: [id] # by default, we don't allow chunks to cross documents
input:
type: file # or blob
file_type: text # or csv
base_dir: "input"
file_encoding: utf-8
file_pattern: ".*\\.txt$"
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: false
raw_entities: false
top_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: 10
# max_tokens: 12000
global_search:
# max_tokens: 12000
# data_max_tokens: 12000
# map_max_tokens: 1000
# reduce_max_tokens: 2000
# concurrency: 32
LLM模型 ：Qwen2-72B-Instruct
EMBEDDING模型： bge-large-zh-v1.5
本地部署模型使用的Xinference
生成索引 图谱
python -m graphrag.index --root ./ragtest成功界面
全局查询和本地查询
python -m graphrag.query \
--root ./ragtest \
--method global \
"你的问题"
python -m graphrag.query \
--root ./ragtest \
--method local \
"你的问题"gradio 代码
import sys
import shlex
import gradio as gr
import subprocess
def parse_text(text):
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split('`')
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f'<br></code></pre>'
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", "\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = "<br>" + line
text = "".join(lines)
return text
def predict(history):
messages = []
for idx, (user_msg, model_msg) in enumerate(history):
if idx == len(history) - 1 and not model_msg:
messages.append({"role": "user", "content": user_msg})
break
if user_msg:
messages.append({"role": "user", "content": user_msg})
if model_msg:
messages.append({"role": "assistant", "content": model_msg})
messages = messages[len(messages) - 1]["content"]
print("\n\n====conversation====\n", messages)
python_path = sys.executable
# 构建命令
cmd = [
python_path, "-m", "graphrag.query",
"--root", "./ragtest",
"--method", "local",
]
# 安全地添加查询到命令中
cmd.append(shlex.quote(messages))
try:
result = subprocess.run(cmd, capture_output=True, text=True, check=True, encoding='utf-8')
output = result.stdout
if output:
# 提取 "SUCCESS: Local Search Response:" 之后的内容
response = output.split("SUCCESS: Local Search Response:", 1)[-1]
history[-1][1] += response.strip()
yield history
else:
history[-1][1] += "None"
yield history
except subprocess.CalledProcessError as e:
print(e)
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">GraphRAG 测试</h1>""")
chatbot = gr.Chatbot(height=600)
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10, container=False)
with gr.Column(min_width=32, scale=1):
submitBtn = gr.Button("Submit")
def user(query, history):
return "", history + [[parse_text(query), ""]]
submitBtn.click(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
predict, [chatbot], chatbot
)
demo.queue()
demo.launch(server_name="0.0.0.0", server_port=9901, inbrowser=True, share=False)
不知道是不是受限于模型能力 还是自己操作问题，个人感觉效果一般
