Replies: 7 comments
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🤖 Hey there, @XinyueZ! Long time no chat. Diving into the depths of LlamaIndex again, I see? Let's unravel these mysteries together. Based on the context provided, your understanding is mostly correct. Both the Sub Question Query Engine and the Multi-Step Query Engine in LlamaIndex are designed to handle complex queries that may require multiple steps or data sources to fully answer. The Sub Question Query Engine works by breaking down the original query into sub-questions, each of which is directed to a relevant data source. The intermediate answers from these sub-questions are used to provide context and contribute to the overall answer. Each sub-question is designed to extract a specific piece of information from the data source it is directed to. The responses to these sub-questions are then combined to form a comprehensive answer to the original query. The Multi-Step Query Engine, on the other hand, operates over an existing base query engine, along with a multi-step query transform. It processes queries in multiple steps, using a query combiner to combine queries and a response synthesizer to synthesize the final response from the nodes and source nodes obtained from the multi-step query process. It also supports early stopping, which stops the multi-step query process when a certain condition is met. As for the purpose of Retrieval on indices, it is indeed to provide the necessary Context. The retrieval process is responsible for fetching the relevant documents or data from the index based on the query. These retrieved documents or data are then used as the Context for the query. You can refer to the following code snippets from the LlamaIndex repository for more details:
I hope this helps clarify your understanding. If you have any more questions, feel free to ask. Sources
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Based on the statement, are the intermediate answers provided for the transformed questions considered as the context? Alternatively, are those intermediate answers included in the final synthesized answers along with the answer to the original query? According to the https://docs.llamaindex.ai/en/stable/examples/query_transformations/SimpleIndexDemo-multistep.html#multi-step-query-engine |
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🤖 Yes, your understanding is correct. The Multi-Step Query Engine in LlamaIndex does generate multiple new queries, combines them with the original one, and doesn't provide intermediate answers for the transformed questions. This process is handled by the Here is the relevant part of the code: while not should_stop:
if self._num_steps is not None and cur_steps >= self._num_steps:
should_stop = True
break
elif should_stop:
break
updated_query_bundle = self._combine_queries(query_bundle, prev_reasoning)
stop_dict = {"query_bundle": updated_query_bundle}
if self._stop_fn(stop_dict):
should_stop = True
break
cur_response = self._query_engine.query(updated_query_bundle)
cur_qa_text = (
f"\nQuestion: {updated_query_bundle.query_str}\n"
f"Answer: {cur_response!s}"
)
text_chunks.append(cur_qa_text)
for source_node in cur_response.source_nodes:
source_nodes.append(source_node)
final_response_metadata["sub_qa"].append(
(updated_query_bundle.query_str, cur_response)
)
prev_reasoning += (
f"- {updated_query_bundle.query_str}\n" f"- {cur_response!s}\n"
)
cur_steps += 1
nodes = [
NodeWithScore(node=TextNode(text=text_chunk)) for text_chunk in text_chunks
]
return nodes, source_nodes, final_response_metadata You can find this code in the llama_index/query_engine/multistep_query_engine.py file in the LlamaIndex repository. The Here is the def _combine_queries(
self, query_bundle: QueryBundle, prev_reasoning: str
) -> QueryBundle:
"""Combine queries."""
transform_metadata = {
"prev_reasoning": prev_reasoning,
"index_summary": self._index_summary,
}
return self._query_transform(query_bundle, metadata=transform_metadata) This method is also located in the llama_index/query_engine/multistep_query_engine.py file. Sources
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So let me summarize before closing the whole, if the |
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🤖 You're correct in your understanding of the The On the other hand, the I hope this clarifies the difference between the Sources
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@dosu-bot |
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🤖 The The class MultiStepQueryEngine(BaseQueryEngine):
def __init__(
self,
query_engine: BaseQueryEngine,
query_transform: StepDecomposeQueryTransform,
response_synthesizer: Optional[BaseSynthesizer] = None,
num_steps: Optional[int] = 3,
early_stopping: bool = True,
index_summary: str = "None",
stop_fn: Optional[Callable[[Dict], bool]] = None,
) -> None:
... The def _query_multistep(
self, query_bundle: QueryBundle
) -> Tuple[List[NodeWithScore], List[NodeWithScore], Dict[str, Any]]:
... The final_response = self._response_synthesizer.synthesize(
query=query_bundle,
nodes=nodes,
additional_source_nodes=source_nodes,
) final_response = await self._response_synthesizer.asynthesize(
query=query_bundle,
nodes=nodes,
additional_source_nodes=source_nodes,
) Unfortunately, I couldn't find specific information about the Sources
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Hey masters,
Multi-Step Query Engine VS Sub Question Query Engine
Can you explain the difference between them or much deeper?
So, from what I understand:
Therefore, are the intermediate answers used as the Context for the original query, or are the intermediate answers and the original query's answer combined or merged?
Please correct me if I am mistaken, but the purpose of Retrieval on indices is to provide the necessary Context. Currently, I am following these lessons:
https://youtu.be/77qELPbNgxA?si=xmQ_aGDmyd207PEH
https://youtu.be/gqhlqdawHT4?si=HMvKmDvbqejttsBI
Based on my understanding, the intermediate answers are meant to contribute to the overall Context, and the original query will be executed based on the overall Context.
Pls help me.
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