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# 定义可使用工具
from llama_index.core.indices.base import BaseIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.agent import ReActAgent
from llama_index.core import get_response_synthesizer
from typing import List, Tuple
def from_index(index: Tuple[str, BaseIndex]) -> Tuple[str, QueryEngineTool]:
"""
Optional response mode:
- refine: LLM call per node
- compact: less LLM call compare to refine
- tree_summarize: tree based summarization
- simple_summarize: summarize all chunks
- context_only: Returns a concatenated string of all text chunks.
- accumulate: Good for when you need to run the same query separately against each text chunk.
- compact accumulate: less LLM call accumulate
"""
index_name, vectorIndex = index
response_synthesizer = get_response_synthesizer(response_mode="compact")
query_engine = vectorIndex.as_query_engine(response_synthesizer=response_synthesizer, similiarity_top_k=3)
tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(
name=index_name,
description=f"Useful for questions related to specific aspects of {index_name}"
),
)
return (index_name, tool)
def from_agent(agent: ReActAgent) -> List[QueryEngineTool]:
tool = QueryEngineTool(
query_engine=agent,
metadata=ToolMetadata(
name="agent",
description="This is a agent specilized in the topic: agent"
),
)
return tool
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