[releases/2.23] Catch releases 2.22.2 (#1290)

Author: wanliAlex

src/marqo/api/models/recommend_query.py

@@ -2,7 +2,7 @@
 
 from marqo.core.models.interpolation_method import InterpolationMethod
 from marqo.tensor_search.models.api_models import BaseMarqoModel
-from pydantic.v1 import root_validator
+from pydantic.v1 import root_validator, Field
 from marqo.tensor_search.models.score_modifiers_object import ScoreModifierLists
 
 
@@ -22,6 +22,8 @@ class RecommendQuery(BaseMarqoModel):
     attributesToRetrieve: Union[None, List[str]] = None
     scoreModifiers: Optional[ScoreModifierLists] = None
     rerankDepth: Optional[int] = None
+    allow_missing_documents: bool = Field(default=False, alias="allowMissingDocuments")
+    allow_missing_embeddings: bool = Field(default=False, alias="allowMissingEmbeddings")
 
     @root_validator(pre=False)
     def validate_rerank_depth(cls, values):

src/marqo/core/models/hybrid_parameters.py

@@ -99,14 +99,6 @@ def validate_properties(cls, values):
             if values.get('rankingMethod') not in [RankingMethod.Lexical, RankingMethod.Tensor]:
                 raise ValueError("For retrievalMethod: tensor or lexical, rankingMethod must be: tensor or lexical")
 
-        # if tensor query is an empty dict
-        if isinstance(values.get('queryTensor'), dict):
-            if not len(values.get('queryTensor')):
-                raise ValueError(
-                    "Multi-term query for queryTensor requires at least one query. Received empty dictionary"
-                )
-
-
         return values
 
     @validator('alpha')

src/marqo/core/search/hybrid_search.py

@@ -200,10 +200,11 @@ def search(
             tensor_query = query
             lexical_query = query
 
-        if (tensor_query is None) != (lexical_query is None):
+        if lexical_query is None:
+            # We could allow queryTensor to be None as tensors might be provided with context
             if hybrid_parameters.retrievalMethod == RetrievalMethod.Disjunction:
                 raise core_exceptions.InvalidArgumentError(
-                    "Either both of 'hybridParameters.queryLexical' and 'hybridParameters.queryTensor' or just 'q'"
+                    "Either 'hybridParameters.queryLexical' or just 'q'"
                     "must be present when 'disjunction' retrieval method is used."
                 )
 

src/marqo/core/search/recommender.py

@@ -23,10 +23,14 @@ def __init__(self, vespa_client: VespaClient, index_management: IndexManagement,
         self.index_management = index_management
         self.inference = inference
 
-    def get_doc_vectors_from_ids(self,
-                  index_name: str,
-                  documents: Union[List[str], Dict[str, float]],
-                  tensor_fields: Optional[List[str]] = None) -> Dict[str, List[List[float]]]:
+    def get_doc_vectors_from_ids(
+            self,
+            index_name: str,
+            documents: Union[List[str], Dict[str, float]],
+            tensor_fields: Optional[List[str]] = None,
+            allow_missing_documents: bool = False,
+            allow_missing_embeddings: bool = False
+    ) -> Dict[str, List[List[float]]]:
         """
         This method gets documents from Vespa using their IDs, removes any unnecessary data, checks for
         lack of vectors, then returns a list of document vectors. Can be used internally (in recommend)
@@ -36,10 +40,17 @@ def get_doc_vectors_from_ids(self,
             index_name: Name of the index to search
             documents: A list of document IDs or a dictionary where the keys are document IDs and the values are weights
             tensor_fields: List of tensor fields to use for recommendation (can include text, image, audio, and video fields)
+            allow_missing_documents: If True, will not raise an error if some document IDs are not found
+            allow_missing_embeddings: If True, will not raise an error if some documents do not have embeddings
 
         Returns:
             A dictionary mapping document IDs to lists of vector embeddings. This is flattened to 1 list per document
                 ID (not separated by tensor field). Order of embeddings is not guaranteed.
+
+        Raises:
+            InvalidArgumentError:
+                - If any document IDs are not found and allow_missing_documents is False
+                - If any document IDs does not have embeddings and allow_missing_embeddings is False
         """
 
         # TODO - Extract search and get_docs from tensor_search and refactor this
@@ -84,55 +95,107 @@ def get_doc_vectors_from_ids(self,
                                                     f'Available tensor fields: {", ".join(valid_tensor_fields)}')
 
         # Use the new optimized method to get only embeddings
+        # TODO - Consolidate these two method into one place
         doc_embeddings_by_field = tensor_search.get_doc_vectors_per_tensor_field_by_ids(
             config.Config(self.vespa_client, inference=self.inference),
             index_name, 
             document_ids, 
-            tensor_fields=tensor_fields
+            tensor_fields=tensor_fields,
+            allow_missing_documents=allow_missing_documents,
         )
 
+        return self._sanitize_doc_embeddins_by_field(
+            all_documents_ids = document_ids,
+            marqo_index=marqo_index,
+            doc_embeddings_by_field=doc_embeddings_by_field,
+            tensor_fields=tensor_fields,
+            allow_missing_documents=allow_missing_documents,
+            allow_missing_embeddings=allow_missing_embeddings,
+        )
+
+    def _sanitize_doc_embeddins_by_field(
+            self,
+            all_documents_ids: List[str],
+            marqo_index: MarqoIndex,
+            doc_embeddings_by_field: Dict[str, Dict[str, List[List[float]]]],
+            tensor_fields: Optional[List[str]],
+            allow_missing_documents: bool,
+            allow_missing_embeddings: bool
+    ) -> Dict[str, List[List[float]]]:
+        """
+        Sanitize the document embeddings by checking for missing documents and embeddings,
+        and flattening the structure to a simple mapping of document ID to list of embeddings.
+
+        If allow_missing_documents is False, raises an error if any document IDs are not found.
+        If allow_missing_embeddings is False, raises an error if any documents do not have embeddings.
+
+        Documents with no embeddings are removed from the result.
+        Args:
+            all_documents_ids: The list of all document IDs that were requested
+            marqo_index: The marqo index object containing metadata about the index
+            doc_embeddings_by_field: The document embeddings by field returned from
+                tensor_search.get_doc_vectors_per_tensor_field_by_ids
+            tensor_fields: tensor fields to include in the result. If None, all fields are included.
+            allow_missing_documents: If True, will not raise an error if some document IDs are not found.
+            allow_missing_embeddings: If True, will not raise an error if some documents do not have embeddings.
+
+        Returns:
+            A dictionary mapping document IDs to lists of vector embeddings.
+            E.g.,
+            {
+                "doc1": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
+                "doc2": [[0.7, 0.8, 0.9]]
+            }
+            where each list contains embeddings from all tensor fields where order of embeddings is not preserved.
+
+        Raises:
+            InvalidArgumentError: If any document IDs are not found and allow_missing_documents is False,
+                                  or if any documents do not have embeddings and allow_missing_embeddings is False.
+        """
+
         # Check that all documents were found
-        not_found = []
-        for doc_id in document_ids:
+        not_found_docs = []
+        for doc_id in all_documents_ids:
             if doc_id not in doc_embeddings_by_field:
-                not_found.append(doc_id)
+                not_found_docs.append(doc_id)
 
-        if len(not_found) > 0:
-            raise InvalidArgumentError(f'The following document IDs were not found: {", ".join(not_found)}')
+        if len(not_found_docs) > 0 and not allow_missing_documents:
+            raise InvalidArgumentError(f'The following document IDs were not found: {", ".join(not_found_docs)}')
 
         # Flatten the embeddings structure to match the expected return format
-        # Convert from Dict[doc_id, Dict[field_name, List[List[float]]]] 
+        # Convert from Dict[doc_id, Dict[field_name, List[List[float]]]]
         # to Dict[doc_id, List[List[float]]]
         doc_vectors: Dict[str, List[List[float]]] = {}
         docs_without_vectors = []
-        
+
         for doc_id, field_embeddings in doc_embeddings_by_field.items():
             vectors: List[List[float]] = []
-            
+
             # Flatten all embeddings from all fields for this document
             for field_name, embedding_list in field_embeddings.items():
                 # For legacy unstructured indices, field_name will be "marqo__embeddings"
                 # and we should include all embeddings regardless of tensor_fields filter
                 # since all embeddings are stored together in marqo__embeddings
-                if (tensor_fields is None or 
+                if (tensor_fields is None or
                     field_name in tensor_fields or
                     (marqo_index.type == IndexType.Unstructured and
                      field_name == unstructured_common.VESPA_DOC_EMBEDDINGS)):
                     vectors.extend(embedding_list)
-            
+
             doc_vectors[doc_id] = vectors
 
             if len(vectors) == 0:
                 docs_without_vectors.append(doc_id)
 
-        if len(docs_without_vectors) > 0:
+
+        if len(docs_without_vectors) > 0 and not allow_missing_embeddings:
             raise InvalidArgumentError(
                 f'The following documents do not have embeddings: {", ".join(docs_without_vectors)}'
             )
-
+        for doc_id in docs_without_vectors:
+            del doc_vectors[doc_id]
         return doc_vectors
 
-
     def recommend(self,
                   index_name: str,
                   documents: Union[List[str], Dict[str, float]],
@@ -150,7 +213,9 @@ def recommend(self,
                   filter: str = None,
                   attributes_to_retrieve: Optional[List[str]] = None,
                   score_modifiers: Optional[ScoreModifierLists] = None,
-                  rerank_depth: Optional[int] = None
+                  rerank_depth: Optional[int] = None,
+                  allow_missing_documents: bool = False,
+                  allow_missing_embeddings: bool = False,
                   ):
         """
         Recommend documents similar to the provided documents.
@@ -191,7 +256,9 @@ def recommend(self,
         doc_vectors = self.get_doc_vectors_from_ids(
             index_name=index_name,
             documents=documents,
-            tensor_fields=tensor_fields
+            tensor_fields=tensor_fields,
+            allow_missing_documents=allow_missing_documents,
+            allow_missing_embeddings=allow_missing_embeddings,
         )
 
         # Save original document IDs for filtering
@@ -208,9 +275,16 @@ def recommend(self,
                 weight = documents[document_id]
             else:
                 weight = 1
+
             vectors.extend(vector_list)
             weights.extend([weight] * len(vector_list))
 
+        if len(vectors) == 0:
+            raise InvalidArgumentError(
+                "Marqo could not collect any valid vector from the documents. "
+                "Please check if the provided documents exist or if the documents have valid embeddings. "
+            )
+
         try:
             interpolated_vector = vector_interpolation.interpolate(
                 vectors, weights
@@ -253,7 +327,7 @@ def recommend(self,
             attributes_to_retrieve=attributes_to_retrieve,
             score_modifiers=score_modifiers,
             processing_start=t0,
-            rerank_depth=rerank_depth
+            rerank_depth=rerank_depth,
         )
 
         return results

src/marqo/tensor_search/api.py

@@ -480,7 +480,9 @@ def recommend(query_dict: dict, index_name: str,
             filter=query.filter,
             attributes_to_retrieve=query.attributesToRetrieve,
             score_modifiers=query.scoreModifiers,
-            rerank_depth=query.rerankDepth
+            rerank_depth=query.rerankDepth,
+            allow_missing_documents=query.allow_missing_documents,
+            allow_missing_embeddings=query.allow_missing_embeddings,
         )
 
 

src/marqo/tensor_search/models/search.py

@@ -69,6 +69,8 @@ class SearchContextTensor(BaseModel):
 class SearchContextDocumentsParameters(BaseModel):
     tensor_fields: Optional[List[str]] = Field(None, alias='tensorFields')
     exclude_input_documents: bool = Field(True, alias='excludeInputDocuments')
+    allow_missing_documents: bool = Field(False, alias='allowMissingDocuments')
+    allow_missing_embeddings: bool = Field(False, alias='allowMissingEmbeddings')
 
     @validator('tensor_fields', pre=True, always=True)
     def check_tensor_fields_not_empty(cls, v):

src/marqo/tensor_search/tensor_search.py

@@ -908,14 +908,16 @@ def get_query_vectors_from_jobs(
 ) -> Dict[Qidx, List[float]]:
     """
     Retrieve the vectorised content associated to each query from the set of batch vectorise jobs.
-    Handles multi-modal queries, by weighting and combining queries into a single vector
+    Handles multi-modal queries, by weighting and combining queries into a single vector.
 
     Args:
         - queries: Original search queries.
         - qidx_to_job: VectorisedJobPointer for each query
         - job_to_vectors: inference output from each VectorisedJob
         - config: standard Marqo config.
 
+    Raises:
+        api_exceptions.InvalidArgError: If this method can not collect a valid vector from the query
     """
     result: Dict[Qidx, List[float]] = defaultdict(list)
     for qidx, ptrs in qidx_to_job.items():
@@ -964,7 +966,9 @@ def get_query_vectors_from_jobs(
                                             context_doc_vectors = config.recommender.get_doc_vectors_from_ids(
                             index_name=q.index.name,
                             documents=context_documents.ids,
-                            tensor_fields=context_documents.parameters.tensor_fields
+                            tensor_fields=context_documents.parameters.tensor_fields,
+                            allow_missing_documents=context_documents.parameters.allow_missing_documents,
+                            allow_missing_embeddings= context_documents.parameters.allow_missing_embeddings
                         )
 
                 # Update weights and vectors list
@@ -989,13 +993,14 @@ def get_query_vectors_from_jobs(
             # Use interpolation to combine all vectors
             vector_interpolation = from_interpolation_method(interpolation_method)
             with RequestMetricsStore.for_request().time(f"search.vectorise.interpolate_vectors"):
-                merged_vector = vector_interpolation.interpolate(
-                    vectors=collected_vectors,
-                    weights=collected_weights
-                )
-
-            result[qidx] = list(merged_vector)
-
+                if collected_vectors:
+                    merged_vector = vector_interpolation.interpolate(
+                        vectors=collected_vectors,
+                        weights=collected_weights
+                    )
+                    result[qidx] = list(merged_vector)
+                else:
+                    result[qidx] = []
         elif isinstance(q.q, str):
             if q.context:
                 raise core_exceptions.InvalidArgumentError(
@@ -1009,6 +1014,14 @@ def get_query_vectors_from_jobs(
             )
         else:
             raise ValueError(f"Unexpected query type: {type(q.q).__name__}")
+
+        if not result[qidx]:
+            raise api_exceptions.InvalidArgError(
+                f"Marqo could not collect any vectors from the search query but the retrieval or ranking method requires "
+                f"at least one valid vector. "
+                f"Please check the provided query, context (if any), or queryTensor(for Hybrid search) "
+            )
+
     return result
 
 
@@ -1090,7 +1103,7 @@ def add_prefix_to_queries(queries: List[BulkSearchQueryEntity]) -> List[BulkSear
 def run_vectorise_pipeline(config: Config, queries: List[BulkSearchQueryEntity], device: Union[Device, str],
                            interpolation_method: InterpolationMethod = None) -> Dict[
     Qidx, List[float]]:
-    """Run the query vectorisation process
+    """Run the query vectorisation process. This is a pipeline used for both Tensor search and Hybrid search.
 
     Raise:
         api_exceptions.InvalidArgError: If the vectorisation process fails or if the media cannot be downloaded.
@@ -1206,6 +1219,9 @@ def _vector_text_search(
         qidx_to_vectors: Dict[Qidx, List[float]] = run_vectorise_pipeline(config, queries, device, interpolation_method)
     vectorised_text = list(qidx_to_vectors.values())[0]
 
+    if not vectorised_text: # pragma: no cover
+        raise InternalError(f"No vector is generated for the tensor query: {query}. ")
+
     marqo_query = MarqoTensorQuery(
         index_name=index_name,
         vector_query=vectorised_text,
@@ -1374,6 +1390,7 @@ def get_doc_vectors_per_tensor_field_by_ids(
     index_name: str, 
     document_ids: List[str],
     tensor_fields: Optional[List[str]] = None,
+    allow_missing_documents: bool = False,
 ) -> Dict[str, Dict[str, List[List[float]]]]:
     """
     Get only the embeddings for documents by their IDs.
@@ -1383,9 +1400,18 @@ def get_doc_vectors_per_tensor_field_by_ids(
         index_name: Name of the index
         document_ids: List of document IDs to fetch
         tensor_fields: Specific tensor fields to get. If None, get all tensor fields.
+        allow_missing_documents: If True, will not raise an error if a document is not found
     
     Returns:
         Dict mapping document_id to field_name to list of embedding vectors
+        E.g.,
+        {
+            "doc_id_1": {
+                "field_name_1": [[0.1, 0.2, ...], ...],
+                "field_name_2": [[0.3, 0.4, ...], ...],
+            },
+            "doc_id_2": {"field_name_1": [[0.5, 0.6, ...], ...]}
+         }
     """
 
     # We can just use the cache here since we refresh every 1s.
@@ -1438,6 +1464,9 @@ def get_doc_vectors_per_tensor_field_by_ids(
                 else:
                     # Otherwise, field is empty list
                     result[doc_id][marqo_tensor_field_name] = []
+        elif response.status == 404 and allow_missing_documents:
+                # If the document is not found and we are allowing missing documents, continue to next response
+                continue
         else:
             # If the response is not successful, error out
             raise core_exceptions.InvalidArgumentError(