Multi queries

src/marqo/s2_inference/processing/custom_clip_utils.py

@@ -6,7 +6,9 @@
 import os
 import urllib
 from tqdm import tqdm
-from src.marqo.s2_inference.configs import ModelCache
+from marqo.s2_inference.configs import ModelCache
+
+
 def whitespace_clean(text):
     text = re.sub(r'\s+', ' ', text)
     text = text.strip()

src/marqo/s2_inference/s2_inference.py

@@ -45,7 +45,7 @@ def vectorise(model_name: str, content: Union[str, List[str]], model_properties:
     try:
         vectorised = available_models[model_cache_key].encode(content, normalize=normalize_embeddings, **kwargs)
     except UnidentifiedImageError as e:
-        raise VectoriseError from e
+        raise VectoriseError(str(e)) from e
 
     return _convert_vectorized_output(vectorised)
 

src/marqo/tensor_search/models/api_models.py

@@ -12,7 +12,7 @@
 
 
 class SearchQuery(BaseModel):
-    q: str
+    q: Union[str, dict]
     searchableAttributes: Union[None, List[str]] = None
     searchMethod: Union[None, str] = "TENSOR"
     limit: int = 10

src/marqo/tensor_search/tensor_search.py

@@ -38,6 +38,7 @@
 import typing
 import uuid
 from typing import List, Optional, Union, Iterable, Sequence, Dict, Any
+import numpy as np
 from PIL import Image
 from marqo.tensor_search.enums import (
     MediaType, MlModel, TensorField, SearchMethod, OpenSearchDataType,
@@ -765,7 +766,8 @@ def refresh_index(config: Config, index_name: str):
     return HttpRequests(config).post(path=F"{index_name}/_refresh")
 
 
-def search(config: Config, index_name: str, text: str, result_count: int = 3, offset: int = 0, highlights=True, return_doc_ids=True,
+def search(config: Config, index_name: str, text: Union[str, dict],
+           result_count: int = 3, offset: int = 0, highlights=True, return_doc_ids=True,
            search_method: Union[str, SearchMethod, None] = SearchMethod.TENSOR,
            searchable_attributes: Iterable[str] = None, verbose: int = 0, num_highlights: int = 3,
            reranker: Union[str, Dict] = None, simplified_format: bool = True, filter: str = None,
@@ -802,12 +804,15 @@ def search(config: Config, index_name: str, text: str, result_count: int = 3, of
     if offset < 0:
         raise errors.IllegalRequestedDocCount("search result offset cannot be less than 0!")   
 
+    # validate query
+    validation.validate_query(q=text, search_method=search_method)
+
     # Validate result_count + offset <= int(max_docs_limit)
     max_docs_limit = utils.read_env_vars_and_defaults(EnvVars.MARQO_MAX_RETRIEVABLE_DOCS)
     check_upper = True if max_docs_limit is None else result_count + offset <= int(max_docs_limit)
     if not check_upper:
         upper_bound_explanation = ("The search result limit + offset must be less than or equal to the "
-                                  f"MARQO_MAX_RETRIEVABLE_DOCS limit of [{max_docs_limit}]. ")
+                                   f"MARQO_MAX_RETRIEVABLE_DOCS limit of [{max_docs_limit}]. ")
 
         raise errors.IllegalRequestedDocCount(f"{upper_bound_explanation} Marqo received search result limit of `{result_count}` "
                                             f"and offset of `{offset}`.")
@@ -835,7 +840,7 @@ def search(config: Config, index_name: str, text: str, result_count: int = 3, of
 
     if search_method.upper() == SearchMethod.TENSOR:
         search_result = _vector_text_search(
-            config=config, index_name=index_name, text=text, result_count=result_count, offset=offset,
+            config=config, index_name=index_name, query=text, result_count=result_count, offset=offset,
             return_doc_ids=return_doc_ids, searchable_attributes=searchable_attributes, verbose=verbose,
             number_of_highlights=num_highlights, simplified_format=simplified_format,
             filter_string=filter, device=device, attributes_to_retrieve=attributes_to_retrieve
@@ -1001,16 +1006,17 @@ def _lexical_search(
 
 
 def _vector_text_search(
-        config: Config, index_name: str, text: str, result_count: int = 5, offset: int = 0, return_doc_ids=False,
-        searchable_attributes: Iterable[str] = None, number_of_highlights=3,
+        config: Config, index_name: str, query: Union[str, dict], result_count: int = 5, offset: int = 0,
+        return_doc_ids=False, searchable_attributes: Iterable[str] = None, number_of_highlights=3,
         verbose=0, raise_on_searchable_attribs=False, hide_vectors=True, k=500,
         simplified_format=True, filter_string: str = None, device=None,
         attributes_to_retrieve: Optional[List[str]] = None):
     """
     Args:
         config:
         index_name:
-        text:
+        query: either a string query (which can be a URL or natural language text), or a dict of
+            <query string>:<weight float> pairs.
         result_count:
         offset:
         return_doc_ids: if True adds doc _id to the docs. Otherwise just returns the docs as-is
@@ -1049,12 +1055,31 @@ def _vector_text_search(
         raise errors.IndexNotFoundError(message="Tried to search a non-existent index: {}".format(index_name))
     selected_device = config.indexing_device if device is None else device
 
-    # TODO average over vectorized inputs with weights
+    # query, weight pairs, if query is a dict:
+    ordered_queries = None
+
+    if isinstance(query, str):
+        to_be_vectorised = query
+    else:  # is dict:
+        ordered_queries = list(query.items())
+        to_be_vectorised = [k for k, _ in ordered_queries]
+
     try:
         vectorised_text = s2_inference.vectorise(
-            model_name=index_info.model_name, model_properties=_get_model_properties(index_info), content=text,
-            device=selected_device,
-            normalize_embeddings=index_info.index_settings['index_defaults']['normalize_embeddings'])[0]
+            model_name=index_info.model_name, model_properties=_get_model_properties(index_info),
+            content=to_be_vectorised, device=selected_device,
+            normalize_embeddings=index_info.index_settings['index_defaults']['normalize_embeddings'])
+        if ordered_queries:
+            # multiple queries. We have to weight and combine them:
+            weighted_vectors = [np.asarray(vec) * weight for vec, weight in zip(vectorised_text, [w for _, w in ordered_queries])]
+            vectorised_text = np.mean(weighted_vectors, axis=0)
+            if index_info.index_settings['index_defaults']['normalize_embeddings']:
+                norm = np.linalg.norm(vectorised_text, axis=-1, keepdims=True)
+                if norm > 0:
+                    vectorised_text /= np.linalg.norm(vectorised_text, axis=-1, keepdims=True)
+            vectorised_text = list(vectorised_text)
+        else:
+            vectorised_text = vectorised_text[0]
     except (s2_inference_errors.UnknownModelError,
             s2_inference_errors.InvalidModelPropertiesError,
             s2_inference_errors.ModelLoadError) as model_error:
@@ -1182,9 +1207,10 @@ def _vector_text_search(
                     "Try reducing the query's limit parameter") from e
             elif 'parse_exception' in response["responses"][0]["error"]["root_cause"][0]["reason"]:
                 raise errors.InvalidArgError("Syntax error, could not parse filter string") from e
-            elif contextualised_filter in response["responses"][0]["error"]["root_cause"][0]["reason"]:
+            elif (contextualised_filter
+                  and contextualised_filter in response["responses"][0]["error"]["root_cause"][0]["reason"]):
                 raise errors.InvalidArgError("Syntax error, could not parse filter string") from e
-            raise e
+            raise errors.BackendCommunicationError(f"Error communicating with Marqo-OS backend:\n{response}")
         except (KeyError, IndexError) as e2:
             raise e
 

src/marqo/tensor_search/validation.py

@@ -3,17 +3,45 @@
 import typing
 from marqo.tensor_search import constants
 from marqo.tensor_search import enums, utils
-from typing import Iterable, Container
+from typing import Iterable, Container, Union
 from marqo.errors import (
     MarqoError, InvalidFieldNameError, InvalidArgError, InternalError,
     InvalidDocumentIdError, DocTooLargeError, InvalidIndexNameError)
-from marqo.tensor_search.enums import TensorField
+from marqo.tensor_search.enums import TensorField, SearchMethod
 from marqo.tensor_search import constants
 from typing import Any, Type
 import inspect
 from enum import Enum
 
 
+def validate_query(q: Union[dict, str], search_method: Union[str, SearchMethod]):
+    """
+    Returns q if an error is not raised"""
+    if isinstance(q, dict):
+        if search_method.upper() != SearchMethod.TENSOR:
+            raise InvalidArgError(
+                'Multi-query search is currently only supported for search_method="TENSOR" '
+                f"\nReceived search_method `{search_method}`")
+        if not len(q):
+            raise InvalidArgError("Multi-query search requires at least one query! Received empty dictionary. ")
+        for k, v in q.items():
+            base_invalid_kv_message = (
+                "Multi queries dictionaries must be <string>:<float> pairs. See usage here: "
+                ""  # FIXME: add link
+            )
+            if not isinstance(k, str):
+                raise InvalidArgError(f"{base_invalid_kv_message}\n"
+                                 f"    Found key of type `{type(k)}` instead of string. Key=`{k}`")
+            if not isinstance(v, (int, float)):
+                raise InvalidArgError(f"{base_invalid_kv_message}\n"
+                                 f"    Found value of type `{type(v)}` instead of float. Value=`{v}`")
+    elif not isinstance(q, str):
+        raise InvalidArgError(
+            f"q must be a string or dict! Received q of type `{type(q)}`. "
+            f"\nq=`{q}`")
+    return q
+
+
 def validate_str_against_enum(value: Any, enum_class: Type[Enum], case_sensitive: bool = True):
     """Checks whether a value is found as the value of a str attribute of the
      given enum_class.