feat: Add filter_policy init parameter to in memory retrievers

haystack/components/retrievers/in_memory/bm25_retriever.py

@@ -6,6 +6,7 @@
 
 from haystack import DeserializationError, Document, component, default_from_dict, default_to_dict
 from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.document_stores.types import FilterPolicy
 
 
 @component
@@ -40,6 +41,7 @@ def __init__(
         filters: Optional[Dict[str, Any]] = None,
         top_k: int = 10,
         scale_score: bool = False,
+        filter_policy: FilterPolicy = FilterPolicy.REPLACE,
     ):
         """
         Create the InMemoryBM25Retriever component.
@@ -52,7 +54,7 @@ def __init__(
             The maximum number of documents to retrieve.
         :param scale_score:
             Scales the BM25 score to a unit interval in the range of 0 to 1, where 1 means extremely relevant. If set to `False`, uses raw similarity scores.
-
+        :param filter_policy: The filter policy to apply during retrieval.
         :raises ValueError:
             If the specified `top_k` is not > 0.
         """
@@ -67,6 +69,7 @@ def __init__(
         self.filters = filters
         self.top_k = top_k
         self.scale_score = scale_score
+        self.filter_policy = filter_policy
 
     def _get_telemetry_data(self) -> Dict[str, Any]:
         """
@@ -83,7 +86,12 @@ def to_dict(self) -> Dict[str, Any]:
         """
         docstore = self.document_store.to_dict()
         return default_to_dict(
-            self, document_store=docstore, filters=self.filters, top_k=self.top_k, scale_score=self.scale_score
+            self,
+            document_store=docstore,
+            filters=self.filters,
+            top_k=self.top_k,
+            scale_score=self.scale_score,
+            filter_policy=self.filter_policy.value,
         )
 
     @classmethod
@@ -101,6 +109,8 @@ def from_dict(cls, data: Dict[str, Any]) -> "InMemoryBM25Retriever":
             raise DeserializationError("Missing 'document_store' in serialization data")
         if "type" not in init_params["document_store"]:
             raise DeserializationError("Missing 'type' in document store's serialization data")
+        if "filter_policy" in init_params:
+            init_params["filter_policy"] = FilterPolicy.from_str(init_params["filter_policy"])
         data["init_parameters"]["document_store"] = InMemoryDocumentStore.from_dict(
             data["init_parameters"]["document_store"]
         )
@@ -132,8 +142,10 @@ def run(
         :raises ValueError:
             If the specified DocumentStore is not found or is not a InMemoryDocumentStore instance.
         """
-        if filters is None:
-            filters = self.filters
+        if self.filter_policy == FilterPolicy.MERGE and filters:
+            filters = {**(self.filters or {}), **filters}
+        else:
+            filters = filters or self.filters
         if top_k is None:
             top_k = self.top_k
         if scale_score is None:

haystack/components/retrievers/in_memory/embedding_retriever.py

@@ -6,6 +6,7 @@
 
 from haystack import DeserializationError, Document, component, default_from_dict, default_to_dict
 from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.document_stores.types import FilterPolicy
 
 
 @component
@@ -50,6 +51,7 @@ def __init__(
         top_k: int = 10,
         scale_score: bool = False,
         return_embedding: bool = False,
+        filter_policy: FilterPolicy = FilterPolicy.REPLACE,
     ):
         """
         Create the InMemoryEmbeddingRetriever component.
@@ -64,7 +66,7 @@ def __init__(
             Scales the BM25 score to a unit interval in the range of 0 to 1, where 1 means extremely relevant. If set to `False`, uses raw similarity scores.
         :param return_embedding:
             Whether to return the embedding of the retrieved Documents.
-
+        :param filter_policy: The filter policy to apply during retrieval.
         :raises ValueError:
             If the specified top_k is not > 0.
         """
@@ -80,6 +82,7 @@ def __init__(
         self.top_k = top_k
         self.scale_score = scale_score
         self.return_embedding = return_embedding
+        self.filter_policy = filter_policy
 
     def _get_telemetry_data(self) -> Dict[str, Any]:
         """
@@ -102,6 +105,7 @@ def to_dict(self) -> Dict[str, Any]:
             top_k=self.top_k,
             scale_score=self.scale_score,
             return_embedding=self.return_embedding,
+            filter_policy=self.filter_policy.value,
         )
 
     @classmethod
@@ -119,6 +123,8 @@ def from_dict(cls, data: Dict[str, Any]) -> "InMemoryEmbeddingRetriever":
             raise DeserializationError("Missing 'document_store' in serialization data")
         if "type" not in init_params["document_store"]:
             raise DeserializationError("Missing 'type' in document store's serialization data")
+        if "filter_policy" in init_params:
+            init_params["filter_policy"] = FilterPolicy.from_str(init_params["filter_policy"])
         data["init_parameters"]["document_store"] = InMemoryDocumentStore.from_dict(
             data["init_parameters"]["document_store"]
         )
@@ -153,8 +159,10 @@ def run(
         :raises ValueError:
             If the specified DocumentStore is not found or is not an InMemoryDocumentStore instance.
         """
-        if filters is None:
-            filters = self.filters
+        if self.filter_policy == FilterPolicy.MERGE and filters:
+            filters = {**(self.filters or {}), **filters}
+        else:
+            filters = filters or self.filters
         if top_k is None:
             top_k = self.top_k
         if scale_score is None:

haystack/document_stores/types/__init__.py

@@ -2,7 +2,8 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from .filter_policy import FilterPolicy
 from .policy import DuplicatePolicy
 from .protocol import DocumentStore
 
-__all__ = ["DocumentStore", "DuplicatePolicy"]
+__all__ = ["DocumentStore", "DuplicatePolicy", "FilterPolicy"]

haystack/document_stores/types/filter_policy.py

@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from enum import Enum
+
+
+class FilterPolicy(Enum):
+    """
+    Policy to determine how filters are applied in retrievers interacting with document stores.
+    """
+
+    # Runtime filters replace init filters during retriever run invocation.
+    REPLACE = "replace"
+
+    # Runtime filters are merged with init filters, with runtime filters overwriting init values.
+    MERGE = "merge"
+
+    def __str__(self):
+        return self.value
+
+    @staticmethod
+    def from_str(filter_policy: str) -> "FilterPolicy":
+        """
+        Convert a string to a FilterPolicy enum.
+
+        :param filter_policy: The string to convert.
+        :return: The corresponding FilterPolicy enum.
+        """
+        enum_map = {e.value: e for e in FilterPolicy}
+        policy = enum_map.get(filter_policy)
+        if policy is None:
+            msg = f"Unknown FilterPolicy type '{filter_policy}'. Supported types are: {list(enum_map.keys())}"
+            raise ValueError(msg)
+        return policy

releasenotes/notes/add-pdfminer-converter-f08f68e38ef82f4a.yaml

@@ -1,4 +1,4 @@
 ---
 enhancements:
   - |
-   Provides users the ability to customize text extraction from PDF files. It is particularly useful for PDFs with unusual layouts, such as those containing multiple text columns. For instance, users can configure the object to retain the reading order.
+   Provides users the ability to customize text extraction from PDF files. It is particularly useful for PDFs with unusual layouts, such as those containing multiple text columns. For instance, users can configure the object to retain the reading order.

releasenotes/notes/retrievers-filter-policy-enhancement-af52d026c346e9c0.yaml

@@ -0,0 +1,4 @@
+---
+enhancements:
+ - |
+   Introduced a 'filter_policy' init parameter for both InMemoryBM25Retriever and InMemoryEmbeddingRetriever, allowing users to define how runtime filters should be applied with options to either 'replace' the initial filters or 'merge' them, providing greater flexibility in filtering query results.

test/components/retrievers/test_in_memory_bm25_retriever.py

@@ -6,6 +6,7 @@
 import pytest
 
 from haystack import Pipeline, DeserializationError
+from haystack.document_stores.types import FilterPolicy
 from haystack.testing.factory import document_store_class
 from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.dataclasses import Document
@@ -56,6 +57,7 @@ def test_to_dict(self):
                 "filters": None,
                 "top_k": 10,
                 "scale_score": False,
+                "filter_policy": "replace",
             },
         }
 
@@ -77,6 +79,7 @@ def test_to_dict_with_custom_init_parameters(self):
                 "filters": {"name": "test.txt"},
                 "top_k": 5,
                 "scale_score": True,
+                "filter_policy": "replace",
             },
         }
 
@@ -99,6 +102,7 @@ def test_from_dict(self):
         assert component.filters == {"name": "test.txt"}
         assert component.top_k == 5
         assert component.scale_score is False
+        assert component.filter_policy == FilterPolicy.REPLACE
 
     def test_from_dict_without_docstore(self):
         data = {"type": "InMemoryBM25Retriever", "init_parameters": {}}

test/components/retrievers/test_in_memory_embedding_retriever.py

@@ -7,6 +7,7 @@
 import numpy as np
 
 from haystack import Pipeline, DeserializationError
+from haystack.document_stores.types import FilterPolicy
 from haystack.testing.factory import document_store_class
 from haystack.components.retrievers.in_memory.embedding_retriever import InMemoryEmbeddingRetriever
 from haystack.dataclasses import Document
@@ -47,6 +48,7 @@ def test_to_dict(self):
                 "top_k": 10,
                 "scale_score": False,
                 "return_embedding": False,
+                "filter_policy": "replace",
             },
         }
 
@@ -70,6 +72,7 @@ def test_to_dict_with_custom_init_parameters(self):
                 "top_k": 5,
                 "scale_score": True,
                 "return_embedding": True,
+                "filter_policy": "replace",
             },
         }
 
@@ -83,13 +86,15 @@ def test_from_dict(self):
                 },
                 "filters": {"name": "test.txt"},
                 "top_k": 5,
+                "filter_policy": "merge",
             },
         }
         component = InMemoryEmbeddingRetriever.from_dict(data)
         assert isinstance(component.document_store, InMemoryDocumentStore)
         assert component.filters == {"name": "test.txt"}
         assert component.top_k == 5
         assert component.scale_score is False
+        assert component.filter_policy == FilterPolicy.MERGE
 
     def test_from_dict_without_docstore(self):
         data = {