[Question]: How to use a QueryEngineTool with a QueryPipeline DAG?

brycecf commented 5 months ago

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Question

I am using a QueryPipeline built with the low-level DAG API. I want to use QueryEngineTools to implement multi-document queries, but the constructor seems to require a query engine, which is not used in the QueryPipeline. Is there still a way to use a QueryEngineTool with a QueryPipeine? If not, is there an alternative?

dosubot[bot] commented 5 months ago

Hey there, @brycecf! Nice to see you diving into another interesting challenge. How's everything going?

Yes, it is possible to use a QueryEngineTool with a QueryPipeline built with the low-level DAG API for implementing multi-document queries. However, it requires that the QueryPipeline or its equivalent functionality be encapsulated within a class that implements the BaseQueryEngine interface. The constructor of QueryEngineTool specifically requires an instance of BaseQueryEngine or a subclass thereof. This means that as long as your QueryPipeline or the mechanism you use for multi-document queries can be adapted to follow the BaseQueryEngine interface, you can use it with QueryEngineTool.

The BaseQueryEngine interface is expected to provide at least a query method (and possibly an aquery method for asynchronous operations, as suggested by the presence of an acall method in QueryEngineTool). Therefore, to integrate a QueryPipeline with QueryEngineTool, you would need to create a wrapper class around your QueryPipeline that implements the BaseQueryEngine interface. This wrapper would translate calls to the query (and aquery, if applicable) methods into operations on your QueryPipeline.

Here's a simplified example of how such a wrapper might look:

from llama_index.core.base.base_query_engine import BaseQueryEngine

class QueryPipelineWrapper(BaseQueryEngine):
    def __init__(self, query_pipeline):
        self.query_pipeline = query_pipeline

    def query(self, query_str):
        # Implement the logic to use the query_pipeline for querying
        # This is a placeholder for the actual implementation
        response = self.query_pipeline.execute(query_str)
        return response

    # Optionally implement aquery if asynchronous operations are supported

This wrapper class (QueryPipelineWrapper) takes an instance of your QueryPipeline and makes it compatible with the QueryEngineTool by adhering to the BaseQueryEngine interface. You can then instantiate a QueryEngineTool with an instance of this wrapper:

query_pipeline = YourQueryPipelineImplementation(...)
query_pipeline_wrapper = QueryPipelineWrapper(query_pipeline)
query_engine_tool = QueryEngineTool(query_engine=query_pipeline_wrapper, metadata=your_metadata)

This approach allows you to use the QueryEngineTool with a QueryPipeline built with the low-level DAG API for implementing multi-document queries, by ensuring compatibility through the BaseQueryEngine interface.

You can find more details about the QueryEngineTool and BaseQueryEngine in the LlamaIndex codebase at the following links:

Please let me know if you need further clarification or have any other questions.

Sources

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logan-markewich commented 5 months ago

I think you can wrap it in a custom query engine (Also linking in my beta docs, because that's what I had open) https://docs.llamaindex.ai/en/logan-docs_3.0/examples/query_engine/custom_query_engine/

The API is quite new though, it could probably use a better way to do this. The main issue is a query engine has one input (the query), while the query pipeline can have arbitrary inputs

prabham17 commented 2 months ago

Hi @brycecf , I am facing same issue. I have built query pipeline, Now I can't use that in other library where It supports only query engine. Hopw to create custom query engine, if I have built query pipeline already

run-llama / llama_index