chore: add DenseRetriever abstraction

docs/_src/api/api/document_store.md

@@ -1269,7 +1269,7 @@ Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
 #### BaseElasticsearchDocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever,
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       filters: Optional[Dict[str, Union[Dict, List, str, int,
                                                         float, bool]]] = None,
@@ -2097,7 +2097,7 @@ Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
 #### InMemoryDocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever: "BaseRetriever",
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       filters: Optional[Dict[str, Any]] = None,
                       update_existing_embeddings: bool = True,
@@ -2913,7 +2913,7 @@ None
 #### FAISSDocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever: "BaseRetriever",
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       update_existing_embeddings: bool = True,
                       filters: Optional[Dict[str, Any]] = None,
@@ -3277,7 +3277,7 @@ None
 #### Milvus1DocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever: "BaseRetriever",
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       batch_size: int = 10_000,
                       update_existing_embeddings: bool = True,
@@ -3681,7 +3681,7 @@ exists.
 #### Milvus2DocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever: "BaseRetriever",
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       batch_size: int = 10_000,
                       update_existing_embeddings: bool = True,
@@ -4398,7 +4398,7 @@ Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
 #### WeaviateDocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever,
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       filters: Optional[Dict[str, Union[Dict, List, str, int,
                                                         float, bool]]] = None,
@@ -5452,7 +5452,7 @@ Parameter options:
 #### PineconeDocumentStore.update\_embeddings
 
 ```python
-def update_embeddings(retriever: "BaseRetriever",
+def update_embeddings(retriever: DenseRetriever,
                       index: Optional[str] = None,
                       update_existing_embeddings: bool = True,
                       filters: Optional[Dict[str, Union[Dict, List, str, int,

docs/_src/api/api/retriever.md

@@ -547,12 +547,60 @@ Performing training on this class according to the TF-IDF algorithm.
 
 # Module dense
 
+<a id="dense.DenseRetriever"></a>
+
+## DenseRetriever
+
+```python
+class DenseRetriever(BaseRetriever)
+```
+
+Base class for all dense retrievers.
+
+<a id="dense.DenseRetriever.embed_queries"></a>
+
+#### DenseRetriever.embed\_queries
+
+```python
+@abstractmethod
+def embed_queries(queries: List[str]) -> np.ndarray
+```
+
+Create embeddings for a list of queries.
+
+**Arguments**:
+
+- `queries`: List of queries to embed.
+
+**Returns**:
+
+Embeddings, one per input query, shape: (queries, embedding_dim)
+
+<a id="dense.DenseRetriever.embed_documents"></a>
+
+#### DenseRetriever.embed\_documents
+
+```python
+@abstractmethod
+def embed_documents(documents: List[Document]) -> np.ndarray
+```
+
+Create embeddings for a list of documents.
+
+**Arguments**:
+
+- `documents`: List of documents to embed.
+
+**Returns**:
+
+Embeddings of documents, one per input document, shape: (documents, embedding_dim)
+
 <a id="dense.DensePassageRetriever"></a>
 
 ## DensePassageRetriever
 
 ```python
-class DensePassageRetriever(BaseRetriever)
+class DensePassageRetriever(DenseRetriever)
 ```
 
 Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
@@ -842,36 +890,36 @@ Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
 #### DensePassageRetriever.embed\_queries
 
 ```python
-def embed_queries(texts: List[str]) -> List[np.ndarray]
+def embed_queries(queries: List[str]) -> np.ndarray
 ```
 
-Create embeddings for a list of queries using the query encoder
+Create embeddings for a list of queries using the query encoder.
 
 **Arguments**:
 
-- `texts`: Queries to embed
+- `queries`: List of queries to embed.
 
 **Returns**:
 
-Embeddings, one per input queries
+Embeddings, one per input query, shape: (queries, embedding_dim)
 
 <a id="dense.DensePassageRetriever.embed_documents"></a>
 
 #### DensePassageRetriever.embed\_documents
 
 ```python
-def embed_documents(docs: List[Document]) -> List[np.ndarray]
+def embed_documents(documents: List[Document]) -> np.ndarray
 ```
 
-Create embeddings for a list of documents using the passage encoder
+Create embeddings for a list of documents using the passage encoder.
 
 **Arguments**:
 
-- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack.
+- `documents`: List of documents to embed.
 
 **Returns**:
 
-Embeddings of documents / passages shape (batch_size, embedding_dim)
+Embeddings of documents, one per input document, shape: (documents, embedding_dim)
 
 <a id="dense.DensePassageRetriever.train"></a>
 
@@ -1005,7 +1053,7 @@ Load DensePassageRetriever from the specified directory.
 ## TableTextRetriever
 
 ```python
-class TableTextRetriever(BaseRetriever)
+class TableTextRetriever(DenseRetriever)
 ```
 
 Retriever that uses a tri-encoder to jointly retrieve among a database consisting of text passages and tables
@@ -1198,25 +1246,25 @@ Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
 #### TableTextRetriever.embed\_queries
 
 ```python
-def embed_queries(texts: List[str]) -> List[np.ndarray]
+def embed_queries(queries: List[str]) -> np.ndarray
 ```
 
-Create embeddings for a list of queries using the query encoder
+Create embeddings for a list of queries using the query encoder.
 
 **Arguments**:
 
-- `texts`: Queries to embed
+- `queries`: List of queries to embed.
 
 **Returns**:
 
-Embeddings, one per input queries
+Embeddings, one per input query, shape: (queries, embedding_dim)
 
 <a id="dense.TableTextRetriever.embed_documents"></a>
 
 #### TableTextRetriever.embed\_documents
 
 ```python
-def embed_documents(docs: List[Document]) -> List[np.ndarray]
+def embed_documents(documents: List[Document]) -> np.ndarray
 ```
 
 Create embeddings for a list of text documents and / or tables using the text passage encoder and
@@ -1225,12 +1273,11 @@ the table encoder.
 
 **Arguments**:
 
-- `docs`: List of Document objects used to represent documents / passages in
-a standardized way within Haystack.
+- `documents`: List of documents to embed.
 
 **Returns**:
 
-Embeddings of documents / passages. Shape: (batch_size, embedding_dim)
+Embeddings of documents, one per input document, shape: (documents, embedding_dim)
 
 <a id="dense.TableTextRetriever.train"></a>
 
@@ -1370,7 +1417,7 @@ Load TableTextRetriever from the specified directory.
 ## EmbeddingRetriever
 
 ```python
-class EmbeddingRetriever(BaseRetriever)
+class EmbeddingRetriever(DenseRetriever)
 ```
 
 <a id="dense.EmbeddingRetriever.__init__"></a>
@@ -1638,36 +1685,36 @@ Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
 #### EmbeddingRetriever.embed\_queries
 
 ```python
-def embed_queries(texts: List[str]) -> List[np.ndarray]
+def embed_queries(queries: List[str]) -> np.ndarray
 ```
 
 Create embeddings for a list of queries.
 
 **Arguments**:
 
-- `texts`: Queries to embed
+- `queries`: List of queries to embed.
 
 **Returns**:
 
-Embeddings, one per input queries
+Embeddings, one per input query, shape: (queries, embedding_dim)
 
 <a id="dense.EmbeddingRetriever.embed_documents"></a>
 
 #### EmbeddingRetriever.embed\_documents
 
 ```python
-def embed_documents(docs: List[Document]) -> List[np.ndarray]
+def embed_documents(documents: List[Document]) -> np.ndarray
 ```
 
 Create embeddings for a list of documents.
 
 **Arguments**:
 
-- `docs`: List of documents to embed
+- `documents`: List of documents to embed.
 
 **Returns**:
 
-Embeddings, one per input document
+Embeddings, one per input document, shape: (docs, embedding_dim)
 
 <a id="dense.EmbeddingRetriever.train"></a>
 

haystack/document_stores/base.py

@@ -12,7 +12,7 @@
 
 from haystack.schema import Document, Label, MultiLabel
 from haystack.nodes.base import BaseComponent
-from haystack.errors import DuplicateDocumentError
+from haystack.errors import DuplicateDocumentError, DocumentStoreError
 from haystack.nodes.preprocessor import PreProcessor
 from haystack.document_stores.utils import eval_data_from_json, eval_data_from_jsonl, squad_json_to_jsonl
 
@@ -698,6 +698,27 @@ def _get_duplicate_labels(
 
         return [label for label in labels if label.id in duplicate_ids]
 
+    @classmethod
+    def _validate_embeddings_shape(cls, embeddings: np.ndarray, num_documents: int, embedding_dim: int):
+        """
+        Validates the shape of model-generated embeddings against expected values for indexing.
+
+        :param embeddings: Embeddings to validate
+        :param num_documents: Number of documents the embeddings were generated for
+        :param embedding_dim: Number of embedding dimensions to expect
+        """
+        num_embeddings, embedding_size = embeddings.shape
+        if num_embeddings != num_documents:
+            raise DocumentStoreError(
+                "The number of embeddings does not match the number of documents: "
+                f"({num_embeddings} != {num_documents})"
+            )
+        if embedding_size != embedding_dim:
+            raise RuntimeError(
+                f"Embedding dimensions of the model ({embedding_size}) don't match the embedding dimensions of the document store ({embedding_dim}). "
+                f"Initiate {cls.__name__} again with arg embedding_dim={embedding_size}."
+            )
+
 
 class KeywordDocumentStore(BaseDocumentStore):
     """

haystack/document_stores/elasticsearch.py

@@ -27,6 +27,7 @@
 from haystack.document_stores.base import get_batches_from_generator
 from haystack.document_stores.filter_utils import LogicalFilterClause
 from haystack.errors import DocumentStoreError, HaystackError
+from haystack.nodes.retriever import DenseRetriever
 
 logger = logging.getLogger(__name__)
 
@@ -1400,7 +1401,7 @@ def _get_raw_similarity_score(self, score):
 
     def update_embeddings(
         self,
-        retriever,
+        retriever: DenseRetriever,
         index: Optional[str] = None,
         filters: Optional[Dict[str, Union[Dict, List, str, int, float, bool]]] = None,
         update_existing_embeddings: bool = True,
@@ -1482,16 +1483,10 @@ def update_embeddings(
         with tqdm(total=document_count, position=0, unit=" Docs", desc="Updating embeddings") as progress_bar:
             for result_batch in get_batches_from_generator(result, batch_size):
                 document_batch = [self._convert_es_hit_to_document(hit, return_embedding=False) for hit in result_batch]
-                embeddings = retriever.embed_documents(document_batch)  # type: ignore
-                if len(document_batch) != len(embeddings):
-                    raise DocumentStoreError(
-                        "The number of embeddings does not match the number of documents in the batch "
-                        f"({len(embeddings)} != {len(document_batch)})"
-                    )
-                if embeddings[0].shape[0] != self.embedding_dim:
-                    raise RuntimeError(
-                        f"Embedding dimensions of the model ({embeddings[0].shape[0]}) doesn't match the embedding dimensions of the document store ({self.embedding_dim}). Please reinitiate ElasticsearchDocumentStore() with arg embedding_dim={embeddings[0].shape[0]}."
-                    )
+                embeddings = retriever.embed_documents(document_batch)
+                self._validate_embeddings_shape(
+                    embeddings=embeddings, num_documents=len(document_batch), embedding_dim=self.embedding_dim
+                )
 
                 doc_updates = []
                 for doc, emb in zip(document_batch, embeddings):