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