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MemoryEmbeddingRetriever (2.0) (#5726)
* MemoryDocumentStore - Embedding retrieval draft * add release notes * fix mypy * better comment * improve return_embeddings handling * MemoryEmbeddingRetriever - first draft * address PR comments * release note * update docstrings * update docstrings * incorporated feeback * add return_embedding to __init__ * rm leftover docstring --------- Co-authored-by: Daria Fokina <daria.fokina@deepset.ai>
This commit is contained in:
Stefano Fiorucci
GitHub
parent
d860a5c604
commit
2edf85f739
@ -1,3 +1,3 @@
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from haystack.preview.components.retrievers.memory import MemoryRetriever
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from haystack.preview.components.retrievers.memory import MemoryBM25Retriever, MemoryEmbeddingRetriever
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__all__ = ["MemoryRetriever"]
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__all__ = ["MemoryBM25Retriever", "MemoryEmbeddingRetriever"]
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@ -5,7 +5,7 @@ from haystack.preview.document_stores import MemoryDocumentStore, document_store
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@component
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class MemoryRetriever:
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class MemoryBM25Retriever:
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"""
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A component for retrieving documents from a MemoryDocumentStore using the BM25 algorithm.
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@ -20,12 +20,12 @@ class MemoryRetriever:
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scale_score: bool = True,
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):
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"""
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Create a MemoryRetriever component.
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Create a MemoryBM25Retriever component.
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:param document_store: An instance of MemoryDocumentStore.
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:param filters: A dictionary with filters to narrow down the search space (default is None).
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:param top_k: The maximum number of documents to retrieve (default is 10).
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:param scale_score: Whether to scale the BM25 score or not (default is True).
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:param filters: A dictionary with filters to narrow down the search space. Default is None.
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:param top_k: The maximum number of documents to retrieve. Default is 10.
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:param scale_score: Whether to scale the BM25 score or not. Default is True.
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:raises ValueError: If the specified top_k is not > 0.
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"""
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@ -51,7 +51,7 @@ class MemoryRetriever:
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)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "MemoryRetriever":
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def from_dict(cls, data: Dict[str, Any]) -> "MemoryBM25Retriever":
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"""
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Deserialize this component from a dictionary.
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"""
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@ -77,13 +77,12 @@ class MemoryRetriever:
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scale_score: Optional[bool] = None,
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):
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"""
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Run the MemoryRetriever on the given input data.
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Run the MemoryBM25Retriever on the given input data.
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:param query: The query string for the retriever.
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:param filters: A dictionary with filters to narrow down the search space.
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:param top_k: The maximum number of documents to return.
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:param scale_score: Whether to scale the BM25 scores or not.
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:param document_stores: A dictionary mapping DocumentStore names to instances.
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:return: The retrieved documents.
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:raises ValueError: If the specified DocumentStore is not found or is not a MemoryDocumentStore instance.
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@ -101,3 +100,119 @@ class MemoryRetriever:
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self.document_store.bm25_retrieval(query=query, filters=filters, top_k=top_k, scale_score=scale_score)
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)
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return {"documents": docs}
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@component
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class MemoryEmbeddingRetriever:
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"""
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A component for retrieving documents from a MemoryDocumentStore using a vector similarity metric.
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Needs to be connected to a MemoryDocumentStore to run.
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"""
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def __init__(
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self,
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document_store: MemoryDocumentStore,
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filters: Optional[Dict[str, Any]] = None,
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top_k: int = 10,
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scale_score: bool = True,
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return_embedding: bool = False,
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):
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"""
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Create a MemoryEmbeddingRetriever component.
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:param document_store: An instance of MemoryDocumentStore.
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:param filters: A dictionary with filters to narrow down the search space. Default is None.
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:param top_k: The maximum number of documents to retrieve. Default is 10.
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:param scale_score: Whether to scale the scores of the retrieved documents or not. Default is True.
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:param return_embedding: Whether to return the embedding of the retrieved Documents. Default is False.
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:raises ValueError: If the specified top_k is not > 0.
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"""
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if not isinstance(document_store, MemoryDocumentStore):
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raise ValueError("document_store must be an instance of MemoryDocumentStore")
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self.document_store = document_store
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if top_k <= 0:
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raise ValueError(f"top_k must be > 0, but got {top_k}")
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self.filters = filters
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self.top_k = top_k
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self.scale_score = scale_score
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self.return_embedding = return_embedding
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def to_dict(self) -> Dict[str, Any]:
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"""
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Serialize this component to a dictionary.
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"""
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docstore = self.document_store.to_dict()
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return default_to_dict(
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self,
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document_store=docstore,
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filters=self.filters,
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top_k=self.top_k,
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scale_score=self.scale_score,
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return_embedding=self.return_embedding,
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)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "MemoryBM25Retriever":
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"""
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Deserialize this component from a dictionary.
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"""
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init_params = data.get("init_parameters", {})
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if "document_store" not in init_params:
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raise DeserializationError("Missing 'document_store' in serialization data")
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if "type" not in init_params["document_store"]:
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raise DeserializationError("Missing 'type' in document store's serialization data")
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if init_params["document_store"]["type"] not in document_store.registry:
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raise DeserializationError(f"DocumentStore type '{init_params['document_store']['type']}' not found")
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docstore_class = document_store.registry[init_params["document_store"]["type"]]
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docstore = docstore_class.from_dict(init_params["document_store"])
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data["init_parameters"]["document_store"] = docstore
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return default_from_dict(cls, data)
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@component.output_types(documents=List[List[Document]])
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def run(
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self,
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queries_embeddings: List[List[float]],
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filters: Optional[Dict[str, Any]] = None,
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top_k: Optional[int] = None,
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scale_score: Optional[bool] = None,
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return_embedding: Optional[bool] = None,
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):
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"""
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Run the MemoryEmbeddingRetriever on the given input data.
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:param queries_embeddings: Embeddings of the queries.
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:param filters: A dictionary with filters to narrow down the search space.
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:param top_k: The maximum number of documents to return.
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:param scale_score: Whether to scale the scores of the retrieved documents or not.
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:param return_embedding: Whether to return the embedding of the retrieved Documents.
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:return: The retrieved documents.
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:raises ValueError: If the specified DocumentStore is not found or is not a MemoryDocumentStore instance.
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"""
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if filters is None:
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filters = self.filters
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if top_k is None:
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top_k = self.top_k
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if scale_score is None:
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scale_score = self.scale_score
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if return_embedding is None:
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return_embedding = self.return_embedding
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docs = []
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for query_embedding in queries_embeddings:
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docs.append(
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self.document_store.embedding_retrieval(
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query_embedding=query_embedding,
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filters=filters,
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top_k=top_k,
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scale_score=scale_score,
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return_embedding=return_embedding,
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)
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)
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return {"documents": docs}
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@ -0,0 +1,6 @@
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---
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preview:
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- |
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Rename `MemoryRetriever` to `MemoryBM25Retriever`
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Add `MemoryEmbeddingRetriever`, which takes as input a query embedding and
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retrieves the most relevant Documents from a `MemoryDocumentStore`.
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@ -1,11 +1,10 @@
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from typing import Dict, Any
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from unittest.mock import MagicMock, patch
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import pytest
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from haystack.preview import Pipeline, DeserializationError
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from haystack.preview.testing.factory import document_store_class
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from haystack.preview.components.retrievers.memory import MemoryRetriever
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from haystack.preview.components.retrievers.memory import MemoryBM25Retriever, MemoryEmbeddingRetriever
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from haystack.preview.dataclasses import Document
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from haystack.preview.document_stores import MemoryDocumentStore
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@ -21,36 +20,39 @@ def mock_docs():
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]
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class TestMemoryRetriever:
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class TestMemoryRetrievers:
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_init_default(self):
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retriever = MemoryRetriever(MemoryDocumentStore())
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def test_init_default(self, retriever_cls):
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retriever = retriever_cls(MemoryDocumentStore())
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assert retriever.filters is None
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assert retriever.top_k == 10
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assert retriever.scale_score
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_init_with_parameters(self):
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retriever = MemoryRetriever(MemoryDocumentStore(), filters={"name": "test.txt"}, top_k=5, scale_score=False)
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def test_init_with_parameters(self, retriever_cls):
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retriever = retriever_cls(MemoryDocumentStore(), filters={"name": "test.txt"}, top_k=5, scale_score=False)
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assert retriever.filters == {"name": "test.txt"}
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assert retriever.top_k == 5
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assert not retriever.scale_score
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_init_with_invalid_top_k_parameter(self):
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def test_init_with_invalid_top_k_parameter(self, retriever_cls):
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with pytest.raises(ValueError, match="top_k must be > 0, but got -2"):
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MemoryRetriever(MemoryDocumentStore(), top_k=-2, scale_score=False)
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retriever_cls(MemoryDocumentStore(), top_k=-2, scale_score=False)
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@pytest.mark.unit
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def test_to_dict(self):
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def test_bm25_retriever_to_dict(self):
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MyFakeStore = document_store_class("MyFakeStore", bases=(MemoryDocumentStore,))
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document_store = MyFakeStore()
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document_store.to_dict = lambda: {"type": "MyFakeStore", "init_parameters": {}}
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component = MemoryRetriever(document_store=document_store)
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component = MemoryBM25Retriever(document_store=document_store)
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data = component.to_dict()
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assert data == {
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"type": "MemoryRetriever",
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"type": "MemoryBM25Retriever",
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"init_parameters": {
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"document_store": {"type": "MyFakeStore", "init_parameters": {}},
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"filters": None,
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@ -60,16 +62,35 @@ class TestMemoryRetriever:
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}
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@pytest.mark.unit
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def test_to_dict_with_custom_init_parameters(self):
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def test_embedding_retriever_to_dict(self):
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MyFakeStore = document_store_class("MyFakeStore", bases=(MemoryDocumentStore,))
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document_store = MyFakeStore()
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document_store.to_dict = lambda: {"type": "MyFakeStore", "init_parameters": {}}
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component = MemoryRetriever(
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component = MemoryEmbeddingRetriever(document_store=document_store)
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data = component.to_dict()
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assert data == {
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"type": "MemoryEmbeddingRetriever",
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"init_parameters": {
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"document_store": {"type": "MyFakeStore", "init_parameters": {}},
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"filters": None,
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"top_k": 10,
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"scale_score": True,
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"return_embedding": False,
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},
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}
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@pytest.mark.unit
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def test_bm25_retriever_to_dict_with_custom_init_parameters(self):
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MyFakeStore = document_store_class("MyFakeStore", bases=(MemoryDocumentStore,))
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document_store = MyFakeStore()
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document_store.to_dict = lambda: {"type": "MyFakeStore", "init_parameters": {}}
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component = MemoryBM25Retriever(
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document_store=document_store, filters={"name": "test.txt"}, top_k=5, scale_score=False
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)
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data = component.to_dict()
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assert data == {
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"type": "MemoryRetriever",
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"type": "MemoryBM25Retriever",
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"init_parameters": {
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"document_store": {"type": "MyFakeStore", "init_parameters": {}},
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"filters": {"name": "test.txt"},
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@ -79,50 +100,78 @@ class TestMemoryRetriever:
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}
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@pytest.mark.unit
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def test_from_dict(self):
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def test_embedding_retriever_to_dict_with_custom_init_parameters(self):
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MyFakeStore = document_store_class("MyFakeStore", bases=(MemoryDocumentStore,))
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document_store = MyFakeStore()
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document_store.to_dict = lambda: {"type": "MyFakeStore", "init_parameters": {}}
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component = MemoryEmbeddingRetriever(
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document_store=document_store,
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filters={"name": "test.txt"},
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top_k=5,
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scale_score=False,
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return_embedding=True,
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)
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data = component.to_dict()
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assert data == {
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"type": "MemoryEmbeddingRetriever",
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"init_parameters": {
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"document_store": {"type": "MyFakeStore", "init_parameters": {}},
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"filters": {"name": "test.txt"},
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"top_k": 5,
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"scale_score": False,
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"return_embedding": True,
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},
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}
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_from_dict(self, retriever_cls):
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document_store_class("MyFakeStore", bases=(MemoryDocumentStore,))
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data = {
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"type": "MemoryRetriever",
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"type": retriever_cls.__name__,
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"init_parameters": {
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"document_store": {"type": "MyFakeStore", "init_parameters": {}},
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"filters": {"name": "test.txt"},
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"top_k": 5,
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},
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}
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component = MemoryRetriever.from_dict(data)
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component = retriever_cls.from_dict(data)
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assert isinstance(component.document_store, MemoryDocumentStore)
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assert component.filters == {"name": "test.txt"}
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assert component.top_k == 5
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assert component.scale_score
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_from_dict_without_docstore(self):
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data = {"type": "MemoryRetriever", "init_parameters": {}}
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def test_from_dict_without_docstore(self, retriever_cls):
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data = {"type": retriever_cls.__name__, "init_parameters": {}}
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with pytest.raises(DeserializationError, match="Missing 'document_store' in serialization data"):
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MemoryRetriever.from_dict(data)
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retriever_cls.from_dict(data)
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_from_dict_without_docstore_type(self):
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data = {"type": "MemoryRetriever", "init_parameters": {"document_store": {"init_parameters": {}}}}
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def test_from_dict_without_docstore_type(self, retriever_cls):
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data = {"type": retriever_cls.__name__, "init_parameters": {"document_store": {"init_parameters": {}}}}
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with pytest.raises(DeserializationError, match="Missing 'type' in document store's serialization data"):
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MemoryRetriever.from_dict(data)
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retriever_cls.from_dict(data)
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_from_dict_nonexisting_docstore(self):
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def test_from_dict_nonexisting_docstore(self, retriever_cls):
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data = {
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"type": "MemoryRetriever",
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"type": retriever_cls.__name__,
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"init_parameters": {"document_store": {"type": "NonexistingDocstore", "init_parameters": {}}},
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}
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with pytest.raises(DeserializationError, match="DocumentStore type 'NonexistingDocstore' not found"):
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MemoryRetriever.from_dict(data)
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retriever_cls.from_dict(data)
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@pytest.mark.unit
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def test_valid_run(self, mock_docs):
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def test_bm25_retriever_valid_run(self, mock_docs):
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top_k = 5
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ds = MemoryDocumentStore()
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ds.write_documents(mock_docs)
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retriever = MemoryRetriever(ds, top_k=top_k)
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retriever = MemoryBM25Retriever(ds, top_k=top_k)
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result = retriever.run(queries=["PHP", "Java"])
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assert "documents" in result
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@ -133,10 +182,32 @@ class TestMemoryRetriever:
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assert result["documents"][1][0].content == "Java is a popular programming language"
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@pytest.mark.unit
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def test_invalid_run_wrong_store_type(self):
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def test_embedding_retriever_valid_run(self):
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top_k = 3
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ds = MemoryDocumentStore(embedding_similarity_function="cosine")
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docs = [
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Document(content="my document", embedding=[0.1, 0.2, 0.3, 0.4]),
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Document(content="another document", embedding=[1.0, 1.0, 1.0, 1.0]),
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Document(content="third document", embedding=[0.5, 0.7, 0.5, 0.7]),
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]
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ds.write_documents(docs)
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retriever = MemoryEmbeddingRetriever(ds, top_k=top_k)
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result = retriever.run(queries_embeddings=[[0.2, 0.4, 0.6, 0.8], [0.1, 0.1, 0.1, 0.1]], return_embedding=True)
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assert "documents" in result
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assert len(result["documents"]) == 2
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assert len(result["documents"][0]) == top_k
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assert len(result["documents"][1]) == top_k
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assert result["documents"][0][0].embedding == [0.1, 0.2, 0.3, 0.4]
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assert result["documents"][1][0].embedding == [1.0, 1.0, 1.0, 1.0]
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@pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
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@pytest.mark.unit
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def test_invalid_run_wrong_store_type(self, retriever_cls):
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SomeOtherDocumentStore = document_store_class("SomeOtherDocumentStore")
|
||||
with pytest.raises(ValueError, match="document_store must be an instance of MemoryDocumentStore"):
|
||||
MemoryRetriever(SomeOtherDocumentStore())
|
||||
retriever_cls(SomeOtherDocumentStore())
|
||||
|
||||
@pytest.mark.integration
|
||||
@pytest.mark.parametrize(
|
||||
@ -146,10 +217,10 @@ class TestMemoryRetriever:
|
||||
("Java", "Java is a popular programming language"),
|
||||
],
|
||||
)
|
||||
def test_run_with_pipeline(self, mock_docs, query: str, query_result: str):
|
||||
def test_run_bm25_retriever_with_pipeline(self, mock_docs, query: str, query_result: str):
|
||||
ds = MemoryDocumentStore()
|
||||
ds.write_documents(mock_docs)
|
||||
retriever = MemoryRetriever(ds)
|
||||
retriever = MemoryBM25Retriever(ds)
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("retriever", retriever)
|
||||
@ -161,6 +232,38 @@ class TestMemoryRetriever:
|
||||
assert results_docs
|
||||
assert results_docs[0][0].content == query_result
|
||||
|
||||
@pytest.mark.integration
|
||||
def test_run_embedding_retriever_with_pipeline(self):
|
||||
ds = MemoryDocumentStore(embedding_similarity_function="cosine")
|
||||
top_k = 2
|
||||
docs = [
|
||||
Document(content="my document", embedding=[0.1, 0.2, 0.3, 0.4]),
|
||||
Document(content="another document", embedding=[1.0, 1.0, 1.0, 1.0]),
|
||||
Document(content="third document", embedding=[0.5, 0.7, 0.5, 0.7]),
|
||||
]
|
||||
ds.write_documents(docs)
|
||||
retriever = MemoryEmbeddingRetriever(ds, top_k=top_k)
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("retriever", retriever)
|
||||
result: Dict[str, Any] = pipeline.run(
|
||||
data={
|
||||
"retriever": {
|
||||
"queries_embeddings": [[0.2, 0.4, 0.6, 0.8], [0.1, 0.1, 0.1, 0.1]],
|
||||
"return_embedding": True,
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
assert result
|
||||
assert "retriever" in result
|
||||
results_docs = result["retriever"]["documents"]
|
||||
assert results_docs
|
||||
assert len(results_docs[0]) == top_k
|
||||
assert len(results_docs[1]) == top_k
|
||||
assert results_docs[0][0].embedding == [0.1, 0.2, 0.3, 0.4]
|
||||
assert results_docs[1][0].embedding == [1.0, 1.0, 1.0, 1.0]
|
||||
|
||||
@pytest.mark.integration
|
||||
@pytest.mark.parametrize(
|
||||
"query, query_result, top_k",
|
||||
@ -170,10 +273,10 @@ class TestMemoryRetriever:
|
||||
("Ruby", "Ruby is a popular programming language", 3),
|
||||
],
|
||||
)
|
||||
def test_run_with_pipeline_and_top_k(self, mock_docs, query: str, query_result: str, top_k: int):
|
||||
def test_run_bm25_retriever_with_pipeline_and_top_k(self, mock_docs, query: str, query_result: str, top_k: int):
|
||||
ds = MemoryDocumentStore()
|
||||
ds.write_documents(mock_docs)
|
||||
retriever = MemoryRetriever(ds)
|
||||
retriever = MemoryBM25Retriever(ds)
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("retriever", retriever)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user