feat: Adding filters param to MostSimilarDocumentsPipeline run and run_batch (#3301)

* Adding filters param to MostSimilarDocumentsPipeline run and run_batch

* Adding index param to MostSimilarDocumentsPipeline run and run_batch

* Adding index param documentation to MostSimilarDocumentsPipeline run and run_batch

* Updated index param documentation to MostSimilarDocumentsPipeline run and run_batch. Updated type: ignore in run_batch

* Adding filters param to MostSimilarDocumentsPipeline run and run_batch

* Adding index param to MostSimilarDocumentsPipeline run and run_batch

* Adding index param documentation to MostSimilarDocumentsPipeline run and run_batch

* Updated index param documentation to MostSimilarDocumentsPipeline run and run_batch. Updated type: ignore in run_batch

haystack/pipelines/standard_pipelines.py

@@ -717,27 +717,43 @@ class MostSimilarDocumentsPipeline(BaseStandardPipeline):
         self.pipeline.add_node(component=document_store, name="DocumentStore", inputs=["Query"])
         self.document_store = document_store
 
-    def run(self, document_ids: List[str], top_k: int = 5):
+    def run(
+        self,
+        document_ids: List[str],
+        filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None,
+        top_k: int = 5,
+        index: Optional[str] = None,
+    ):
         """
         :param document_ids: document ids
+        :param filters: Optional filters to narrow down the search space to documents whose metadata fulfill certain conditions
         :param top_k: How many documents id to return against single document
+        :param index: Optionally specify the name of index to query the document from. If None, the DocumentStore's default index (self.index) will be used.
         """
         similar_documents: list = []
         self.document_store.return_embedding = True  # type: ignore
 
-        for document in self.document_store.get_documents_by_id(ids=document_ids):
+        for document in self.document_store.get_documents_by_id(ids=document_ids, index=index):
             similar_documents.append(
                 self.document_store.query_by_embedding(
-                    query_emb=document.embedding, return_embedding=False, top_k=top_k
+                    query_emb=document.embedding, filters=filters, return_embedding=False, top_k=top_k, index=index
                 )
             )
 
         self.document_store.return_embedding = False  # type: ignore
         return similar_documents
 
-    def run_batch(self, document_ids: List[str], top_k: int = 5):  # type: ignore
+    def run_batch(  # type: ignore
+        self,
+        document_ids: List[str],
+        filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None,
+        top_k: int = 5,
+        index: Optional[str] = None,
+    ):
         """
         :param document_ids: document ids
+        :param filters: Optional filters to narrow down the search space to documents whose metadata fulfill certain conditions
         :param top_k: How many documents id to return against single document
+        :param index: Optionally specify the name of index to query the document from. If None, the DocumentStore's default index (self.index) will be used.
         """
-        return self.run(document_ids=document_ids, top_k=top_k)
+        return self.run(document_ids=document_ids, filters=filters, top_k=top_k, index=index)

test/pipelines/test_standard_pipelines.py

@@ -200,6 +200,39 @@ def test_most_similar_documents_pipeline(retriever, document_store):
             assert isinstance(document.content, str)
 
 
+@pytest.mark.parametrize(
+    "retriever,document_store", [("embedding", "milvus1"), ("embedding", "elasticsearch")], indirect=True
+)
+def test_most_similar_documents_pipeline_with_filters(retriever, document_store):
+    documents = [
+        {"id": "a", "content": "Sample text for document-1", "meta": {"source": "wiki1"}},
+        {"id": "b", "content": "Sample text for document-2", "meta": {"source": "wiki2"}},
+        {"content": "Sample text for document-3", "meta": {"source": "wiki3"}},
+        {"content": "Sample text for document-4", "meta": {"source": "wiki4"}},
+        {"content": "Sample text for document-5", "meta": {"source": "wiki5"}},
+    ]
+
+    document_store.write_documents(documents)
+    document_store.update_embeddings(retriever)
+
+    docs_id: list = ["a", "b"]
+    filters = {"source": ["wiki3", "wiki4", "wiki5"]}
+    pipeline = MostSimilarDocumentsPipeline(document_store=document_store)
+    list_of_documents = pipeline.run(document_ids=docs_id, filters=filters)
+
+    assert len(list_of_documents[0]) > 1
+    assert isinstance(list_of_documents, list)
+    assert len(list_of_documents) == len(docs_id)
+
+    for another_list in list_of_documents:
+        assert isinstance(another_list, list)
+        for document in another_list:
+            assert isinstance(document, Document)
+            assert isinstance(document.id, str)
+            assert isinstance(document.content, str)
+            assert document.meta["source"] in ["wiki3", "wiki4", "wiki5"]
+
+
 @pytest.mark.parametrize("retriever,document_store", [("embedding", "memory")], indirect=True)
 def test_most_similar_documents_pipeline_batch(retriever, document_store):
     documents = [
@@ -229,6 +262,37 @@ def test_most_similar_documents_pipeline_batch(retriever, document_store):
             assert isinstance(document.content, str)
 
 
+@pytest.mark.parametrize("retriever,document_store", [("embedding", "memory")], indirect=True)
+def test_most_similar_documents_pipeline_with_filters_batch(retriever, document_store):
+    documents = [
+        {"id": "a", "content": "Sample text for document-1", "meta": {"source": "wiki1"}},
+        {"id": "b", "content": "Sample text for document-2", "meta": {"source": "wiki2"}},
+        {"content": "Sample text for document-3", "meta": {"source": "wiki3"}},
+        {"content": "Sample text for document-4", "meta": {"source": "wiki4"}},
+        {"content": "Sample text for document-5", "meta": {"source": "wiki5"}},
+    ]
+
+    document_store.write_documents(documents)
+    document_store.update_embeddings(retriever)
+
+    docs_id: list = ["a", "b"]
+    filters = {"source": ["wiki3", "wiki4", "wiki5"]}
+    pipeline = MostSimilarDocumentsPipeline(document_store=document_store)
+    list_of_documents = pipeline.run_batch(document_ids=docs_id, filters=filters)
+
+    assert len(list_of_documents[0]) > 1
+    assert isinstance(list_of_documents, list)
+    assert len(list_of_documents) == len(docs_id)
+
+    for another_list in list_of_documents:
+        assert isinstance(another_list, list)
+        for document in another_list:
+            assert isinstance(document, Document)
+            assert isinstance(document.id, str)
+            assert isinstance(document.content, str)
+            assert document.meta["source"] in ["wiki3", "wiki4", "wiki5"]
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
 def test_most_similar_documents_pipeline_save(tmpdir, document_store_with_docs):