Fix update_embeddings() for FAISSDocumentStore (#978)

.github/workflows/ci.yml

@@ -77,7 +77,7 @@ jobs:
       run: docker run -d -p 19530:19530 -p 19121:19121 milvusdb/milvus:1.0.0-cpu-d030521-1ea92e
 
     - name: Run GraphDB
-      run: docker run -d -p 7200:7200 --name haystack_test_graphdb docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11
+      run: docker run -d -p 7200:7200 --name haystack_test_graphdb deepset/graphdb-free:9.4.1-adoptopenjdk11
 
     - name: Run Apache Tika
       run: docker run -d -p 9998:9998 -e "TIKA_CHILD_JAVA_OPTS=-JXms128m" -e "TIKA_CHILD_JAVA_OPTS=-JXmx128m" apache/tika:1.24.1

haystack/document_store/elasticsearch.py

@@ -490,6 +490,32 @@ class ElasticsearchDocumentStore(BaseDocumentStore):
         """
         return self.get_document_count(index=index)
 
+    def get_embedding_count(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int:
+        """
+        Return the count of embeddings in the document store.
+        """
+
+        index = index or self.index
+
+        body: dict = {"query": {"bool": {"must": [{"exists": {"field": self.embedding_field}}]}}}
+        if filters:
+            filter_clause = []
+            for key, values in filters.items():
+                if type(values) != list:
+                    raise ValueError(
+                        f'Wrong filter format for key "{key}": Please provide a list of allowed values for each key. '
+                        'Example: {"name": ["some", "more"], "category": ["only_one"]} ')
+                filter_clause.append(
+                    {
+                        "terms": {key: values}
+                    }
+                )
+            body["query"]["bool"]["filter"] = filter_clause
+
+        result = self.client.count(index=index, body=body)
+        count = result["count"]
+        return count
+
     def get_all_documents(
         self,
         index: Optional[str] = None,

haystack/document_store/faiss.py

@@ -199,6 +199,14 @@ class FAISSDocumentStore(SQLDocumentStore):
         """
 
         index = index or self.index
+
+        if update_existing_embeddings is True:
+            if filters is None:
+                self.faiss_indexes[index].reset()
+                self.reset_vector_ids(index)
+            else:
+                raise Exception("update_existing_embeddings=True is not supported with filters.")
+
         if not self.faiss_indexes.get(index):
             raise ValueError("Couldn't find a FAISS index. Try to init the FAISSDocumentStore() again ...")
 
@@ -290,6 +298,15 @@ class FAISSDocumentStore(SQLDocumentStore):
                     doc.embedding = self.faiss_indexes[index].reconstruct(int(doc.meta["vector_id"]))
         return documents
 
+    def get_embedding_count(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int:
+        """
+        Return the count of embeddings in the document store.
+        """
+        if filters:
+            raise Exception("filters are not supported for get_embedding_count in FAISSDocumentStore")
+        index = index or self.index
+        return self.faiss_indexes[index].ntotal
+
     def train_index(
         self,
         documents: Optional[Union[List[dict], List[Document]]],

haystack/document_store/memory.py

@@ -220,6 +220,14 @@ class InMemoryDocumentStore(BaseDocumentStore):
         documents = self.get_all_documents(index=index, filters=filters)
         return len(documents)
 
+    def get_embedding_count(self, filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int:
+        """
+        Return the count of embeddings in the document store.
+        """
+        documents = self.get_all_documents(filters=filters, index=index)
+        embedding_count = sum(doc.embedding is not None for doc in documents)
+        return embedding_count
+
     def get_label_count(self, index: Optional[str] = None) -> int:
         """
         Return the number of labels in the document store

haystack/document_store/milvus.py

@@ -520,3 +520,15 @@ class MilvusDocumentStore(SQLDocumentStore):
             return list()
 
         return vectors
+
+    def get_embedding_count(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int:
+        """
+        Return the count of embeddings in the document store.
+        """
+        if filters:
+            raise Exception("filters are not supported for get_embedding_count in MilvusDocumentStore.")
+        index = index or self.index
+        _, embedding_count = self.milvus_server.count_entities(index)
+        if embedding_count is None:
+            embedding_count = 0
+        return embedding_count

test/conftest.py

@@ -347,6 +347,10 @@ def get_document_store(document_store_type, embedding_field="embedding"):
             embedding_field=embedding_field,
             index="haystack_test",
         )
+        _, collections = document_store.milvus_server.list_collections()
+        for collection in collections:
+            if collection.startswith("haystack_test"):
+                document_store.milvus_server.drop_collection(collection)
         return document_store
     else:
         raise Exception(f"No document store fixture for '{document_store_type}'")

test/test_document_store.py

@@ -5,6 +5,7 @@ from elasticsearch import Elasticsearch
 from conftest import get_document_store
 from haystack import Document, Label
 from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
+from haystack.document_store.faiss import FAISSDocumentStore
 
 
 @pytest.mark.elasticsearch
@@ -274,19 +275,34 @@ def test_update_embeddings(document_store, retriever):
     np.testing.assert_array_equal(embedding_before_update, embedding_after_update)
 
     # test updating with filters
-    document_store.update_embeddings(
+    if isinstance(document_store, FAISSDocumentStore):
-        retriever, index="haystack_test_1", batch_size=3, filters={"meta_field": ["value_0", "value_1"]}
+        with pytest.raises(Exception):
-    )
+            document_store.update_embeddings(
-    doc_after_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
+                retriever, index="haystack_test_1", update_existing_embeddings=True, filters={"meta_field": ["value"]}
-    embedding_after_update = doc_after_update.embedding
+            )
-    np.testing.assert_array_equal(embedding_before_update, embedding_after_update)
+    else:
+        document_store.update_embeddings(
+            retriever, index="haystack_test_1", batch_size=3, filters={"meta_field": ["value_0", "value_1"]}
+        )
+        doc_after_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
+        embedding_after_update = doc_after_update.embedding
+        np.testing.assert_array_equal(embedding_before_update, embedding_after_update)
 
     # test update all embeddings
     document_store.update_embeddings(retriever, index="haystack_test_1", batch_size=3, update_existing_embeddings=True)
+    assert document_store.get_embedding_count(index="haystack_test_1") == 11
     doc_after_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
     embedding_after_update = doc_after_update.embedding
     np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, embedding_before_update, embedding_after_update)
 
+    # test update embeddings for newly added docs
+    documents = []
+    for i in range(12, 15):
+        documents.append({"text": f"text_{i}", "id": str(i), "meta_field": f"value_{i}"})
+    document_store.write_documents(documents, index="haystack_test_1")
+    document_store.update_embeddings(retriever, index="haystack_test_1", batch_size=3, update_existing_embeddings=False)
+    assert document_store.get_embedding_count(index="haystack_test_1") == 14
+
 
 @pytest.mark.elasticsearch
 def test_delete_all_documents(document_store_with_docs):