Source code for autogen_ext.tools.graphrag._global_search
# mypy: disable-error-code="no-any-unimported,misc"
from pathlib import Path
import pandas as pd
import tiktoken
from autogen_core import CancellationToken
from autogen_core.tools import BaseTool
from graphrag.config.config_file_loader import load_config_from_file
from graphrag.query.indexer_adapters import (
read_indexer_communities,
read_indexer_entities,
read_indexer_reports,
)
from graphrag.query.llm.base import BaseLLM
from graphrag.query.llm.get_client import get_llm
from graphrag.query.structured_search.global_search.community_context import GlobalCommunityContext
from graphrag.query.structured_search.global_search.search import GlobalSearch
from pydantic import BaseModel, Field
from ._config import GlobalContextConfig as ContextConfig
from ._config import GlobalDataConfig as DataConfig
from ._config import MapReduceConfig
_default_context_config = ContextConfig()
_default_mapreduce_config = MapReduceConfig()
[docs]
class GlobalSearchToolArgs(BaseModel):
query: str = Field(..., description="The user query to perform global search on.")
[docs]
class GlobalSearchTool(BaseTool[GlobalSearchToolArgs, GlobalSearchToolReturn]):
"""Enables running GraphRAG global search queries as an AutoGen tool.
This tool allows you to perform semantic search over a corpus of documents using the GraphRAG framework.
The search combines graph-based document relationships with semantic embeddings to find relevant information.
.. note::
This tool requires the :code:`graphrag` extra for the :code:`autogen-ext` package.
To install:
.. code-block:: bash
pip install -U "autogen-agentchat" "autogen-ext[graphrag]"
Before using this tool, you must complete the GraphRAG setup and indexing process:
1. Follow the GraphRAG documentation to initialize your project and settings
2. Configure and tune your prompts for the specific use case
3. Run the indexing process to generate the required data files
4. Ensure you have the settings.yaml file from the setup process
Please refer to the [GraphRAG documentation](https://microsoft.github.io/graphrag/)
for detailed instructions on completing these prerequisite steps.
Example usage with AssistantAgent:
.. code-block:: python
import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.ui import Console
from autogen_ext.tools.graphrag import GlobalSearchTool
from autogen_agentchat.agents import AssistantAgent
async def main():
# Initialize the OpenAI client
openai_client = OpenAIChatCompletionClient(
model="gpt-4o-mini",
api_key="<api-key>",
)
# Set up global search tool
global_tool = GlobalSearchTool.from_settings(settings_path="./settings.yaml")
# Create assistant agent with the global search tool
assistant_agent = AssistantAgent(
name="search_assistant",
tools=[global_tool],
model_client=openai_client,
system_message=(
"You are a tool selector AI assistant using the GraphRAG framework. "
"Your primary task is to determine the appropriate search tool to call based on the user's query. "
"For broader, abstract questions requiring a comprehensive understanding of the dataset, call the 'global_search' function."
),
)
# Run a sample query
query = "What is the overall sentiment of the community reports?"
await Console(assistant_agent.run_stream(task=query))
if __name__ == "__main__":
asyncio.run(main())
"""
def __init__(
self,
token_encoder: tiktoken.Encoding,
llm: BaseLLM,
data_config: DataConfig,
context_config: ContextConfig = _default_context_config,
mapreduce_config: MapReduceConfig = _default_mapreduce_config,
):
super().__init__(
args_type=GlobalSearchToolArgs,
return_type=GlobalSearchToolReturn,
name="global_search_tool",
description="Perform a global search with given parameters using graphrag.",
)
# Use the provided LLM
self._llm = llm
# Load parquet files
community_df: pd.DataFrame = pd.read_parquet(f"{data_config.input_dir}/{data_config.community_table}.parquet") # type: ignore
entity_df: pd.DataFrame = pd.read_parquet(f"{data_config.input_dir}/{data_config.entity_table}.parquet") # type: ignore
report_df: pd.DataFrame = pd.read_parquet( # type: ignore
f"{data_config.input_dir}/{data_config.community_report_table}.parquet"
)
entity_embedding_df: pd.DataFrame = pd.read_parquet( # type: ignore
f"{data_config.input_dir}/{data_config.entity_embedding_table}.parquet"
)
communities = read_indexer_communities(community_df, entity_df, report_df)
reports = read_indexer_reports(report_df, entity_df, data_config.community_level)
entities = read_indexer_entities(entity_df, entity_embedding_df, data_config.community_level)
context_builder = GlobalCommunityContext(
community_reports=reports,
communities=communities,
entities=entities,
token_encoder=token_encoder,
)
context_builder_params = {
"use_community_summary": context_config.use_community_summary,
"shuffle_data": context_config.shuffle_data,
"include_community_rank": context_config.include_community_rank,
"min_community_rank": context_config.min_community_rank,
"community_rank_name": context_config.community_rank_name,
"include_community_weight": context_config.include_community_weight,
"community_weight_name": context_config.community_weight_name,
"normalize_community_weight": context_config.normalize_community_weight,
"max_tokens": context_config.max_data_tokens,
"context_name": "Reports",
}
map_llm_params = {
"max_tokens": mapreduce_config.map_max_tokens,
"temperature": mapreduce_config.map_temperature,
"response_format": {"type": "json_object"},
}
reduce_llm_params = {
"max_tokens": mapreduce_config.reduce_max_tokens,
"temperature": mapreduce_config.reduce_temperature,
}
self._search_engine = GlobalSearch(
llm=self._llm,
context_builder=context_builder,
token_encoder=token_encoder,
max_data_tokens=context_config.max_data_tokens,
map_llm_params=map_llm_params,
reduce_llm_params=reduce_llm_params,
allow_general_knowledge=mapreduce_config.allow_general_knowledge,
json_mode=mapreduce_config.json_mode,
context_builder_params=context_builder_params,
concurrent_coroutines=32,
response_type=mapreduce_config.response_type,
)
[docs]
async def run(self, args: GlobalSearchToolArgs, cancellation_token: CancellationToken) -> GlobalSearchToolReturn:
result = await self._search_engine.asearch(args.query)
assert isinstance(result.response, str), "Expected response to be a string"
return GlobalSearchToolReturn(answer=result.response)
[docs]
@classmethod
def from_settings(cls, settings_path: str | Path) -> "GlobalSearchTool":
"""Create a GlobalSearchTool instance from GraphRAG settings file.
Args:
settings_path: Path to the GraphRAG settings.yaml file
Returns:
An initialized GlobalSearchTool instance
"""
# Load GraphRAG config
config = load_config_from_file(settings_path)
# Initialize token encoder
token_encoder = tiktoken.get_encoding(config.encoding_model)
# Initialize LLM using graphrag's get_client
llm = get_llm(config)
# Create data config from storage paths
data_config = DataConfig(
input_dir=str(Path(config.storage.base_dir)),
)
return cls(
token_encoder=token_encoder,
llm=llm,
data_config=data_config,
context_config=_default_context_config,
mapreduce_config=_default_mapreduce_config,
)