deepset-ai--haystack/haystack/components/retrievers/multi_query_text_retriever.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0

import asyncio
from concurrent.futures import ThreadPoolExecutor
from typing import Any

from haystack import Document, component, default_from_dict, default_to_dict
from haystack.components.retrievers.types import TextRetriever
from haystack.core.serialization import component_to_dict
from haystack.utils.async_utils import _execute_component_async
from haystack.utils.misc import _deduplicate_documents


@component
class MultiQueryTextRetriever:
    """
    A component that retrieves documents using multiple queries in parallel with a text-based retriever.

    This component takes a list of text queries and uses a text-based retriever to find relevant documents for each
    query in parallel, using a thread pool to manage concurrent execution. The results are combined and sorted by
    relevance score.

    You can use this component in combination with QueryExpander component to enhance the retrieval process.

    ### Usage example
    ```python
    from haystack import Document
    from haystack.components.writers import DocumentWriter
    from haystack.document_stores.in_memory import InMemoryDocumentStore
    from haystack.document_stores.types import DuplicatePolicy
    from haystack.components.retrievers import InMemoryBM25Retriever
    from haystack.components.query import QueryExpander
    from haystack.components.retrievers.multi_query_text_retriever import MultiQueryTextRetriever

    documents = [
        Document(content="Renewable energy is energy that is collected from renewable resources."),
        Document(content="Solar energy is a type of green energy that is harnessed from the sun."),
        Document(content="Wind energy is another type of green energy that is generated by wind turbines."),
        Document(content="Hydropower is a form of renewable energy using the flow of water to generate electricity."),
        Document(content="Geothermal energy is heat that comes from the sub-surface of the earth.")
    ]

    document_store = InMemoryDocumentStore()
    doc_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)
    doc_writer.run(documents=documents)

    in_memory_retriever = InMemoryBM25Retriever(document_store=document_store, top_k=1)
    multiquery_retriever = MultiQueryTextRetriever(retriever=in_memory_retriever)
    results = multiquery_retriever.run(queries=["renewable energy?", "Geothermal", "Hydropower"])
    for doc in results["documents"]:
        print(f"Content: {doc.content}, Score: {doc.score}")
    # >>
    # >> Content: Geothermal energy is heat that comes from the sub-surface of the earth., Score: 1.6474448833731097
    # >> Content: Hydropower is a form of renewable energy using the flow of water to generate electricity., Score: 1.615
    # >> Content: Renewable energy is energy that is collected from renewable resources., Score: 1.5255309812344944
    ```
    """  # noqa E501

    def __init__(self, *, retriever: TextRetriever, max_workers: int = 3) -> None:
        """
        Initialize MultiQueryTextRetriever.

        :param retriever: The text-based retriever to use for document retrieval.
        :param max_workers: Maximum number of worker threads for parallel processing. Default is 3.
        """
        self.retriever = retriever
        self.max_workers = max_workers

    def warm_up(self) -> None:
        """
        Warm up the retriever.
        """
        if hasattr(self.retriever, "warm_up"):
            self.retriever.warm_up()

    async def warm_up_async(self) -> None:
        """
        Warm up the retriever on the serving event loop.
        """
        if hasattr(self.retriever, "warm_up_async"):
            await self.retriever.warm_up_async()
        elif hasattr(self.retriever, "warm_up"):
            self.retriever.warm_up()

    def close(self) -> None:
        """
        Release the retriever's resources.
        """
        if hasattr(self.retriever, "close"):
            self.retriever.close()

    async def close_async(self) -> None:
        """
        Release the retriever's async resources.
        """
        if hasattr(self.retriever, "close_async"):
            await self.retriever.close_async()
        elif hasattr(self.retriever, "close"):
            self.retriever.close()

    @component.output_types(documents=list[Document])
    def run(self, queries: list[str], retriever_kwargs: dict[str, Any] | None = None) -> dict[str, list[Document]]:
        """
        Retrieve documents using multiple queries in parallel.

        :param queries: List of text queries to process.
        :param retriever_kwargs: Optional dictionary of arguments to pass to the retriever's run method.
        :returns:
            A dictionary containing:
                `documents`: List of retrieved documents sorted by relevance score.
        """
        docs: list[Document] = []
        retriever_kwargs = retriever_kwargs or {}

        self.warm_up()

        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            queries_results = executor.map(lambda query: self._run_on_thread(query, retriever_kwargs), queries)
            for result in queries_results:
                if not result:
                    continue
                docs.extend(result)

        # de-duplicate and sort
        docs = _deduplicate_documents(docs)
        docs.sort(key=lambda x: x.score or 0.0, reverse=True)
        return {"documents": docs}

    @component.output_types(documents=list[Document])
    async def run_async(
        self, queries: list[str], retriever_kwargs: dict[str, Any] | None = None
    ) -> dict[str, list[Document]]:
        """
        Retrieve documents using multiple queries concurrently.

        Uses the retriever's `run_async` method if available, otherwise falls back to running `run`
        in a thread executor. Queries are processed concurrently using asyncio.gather.

        :param queries: List of text queries to process.
        :param retriever_kwargs: Optional dictionary of arguments to pass to the retriever's run method.
        :returns:
            A dictionary containing:
                `documents`: List of retrieved documents sorted by relevance score.
        """
        retriever_kwargs = retriever_kwargs or {}

        await self.warm_up_async()

        results = await asyncio.gather(*[self._run_one_async(q, retriever_kwargs) for q in queries])
        docs: list[Document] = [doc for result in results if result for doc in result]
        docs = _deduplicate_documents(docs)
        docs.sort(key=lambda x: x.score or 0.0, reverse=True)
        return {"documents": docs}

    def _run_on_thread(self, query: str, retriever_kwargs: dict[str, Any] | None = None) -> list[Document] | None:
        """
        Process a single query on a separate thread.

        :param query: The text query to process.
        :param retriever_kwargs: Optional dictionary of arguments to pass to the retriever's run method.
        :returns:
            List of retrieved documents or None if no results.
        """
        result = self.retriever.run(query=query, **(retriever_kwargs or {}))
        if result and "documents" in result:
            return result["documents"]
        return None

    async def _run_one_async(self, query: str, retriever_kwargs: dict[str, Any]) -> list[Document] | None:
        """
        Process a single query asynchronously.

        :param query: The text query to process.
        :param retriever_kwargs: Arguments to pass to the retriever's run method.
        :returns:
            List of retrieved documents or None if no results.
        """
        result = await _execute_component_async(self.retriever, query=query, **retriever_kwargs)

        if result and "documents" in result:
            return result["documents"]
        return None

    def to_dict(self) -> dict[str, Any]:
        """
        Serializes the component to a dictionary.

        :returns:
            The serialized component as a dictionary.
        """
        return default_to_dict(
            self, retriever=component_to_dict(obj=self.retriever, name="retriever"), max_workers=self.max_workers
        )

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "MultiQueryTextRetriever":
        """
        Deserializes the component from a dictionary.

        :param data: The dictionary to deserialize from.
        :returns:
            The deserialized component.
        """
        return default_from_dict(cls, data)