Merge pull request #107 from weaviate/fix-cache

Fix unhashable type: 'VectorInputConfig' error with cache enabled

app.py

@@ -118,7 +118,11 @@ def log_info_about_onnx(onnx_runtime: bool):
         cuda_support = True
         cuda_core = os.getenv("CUDA_CORE")
         if cuda_core is None or cuda_core == "":
-            if use_sentence_transformers_vectorizer and use_sentence_transformers_multi_process and torch.cuda.is_available():
+            if (
+                use_sentence_transformers_vectorizer
+                and use_sentence_transformers_multi_process
+                and torch.cuda.is_available()
+            ):
                 available_workers = torch.cuda.device_count()
                 cuda_core = ",".join([f"cuda:{i}" for i in range(available_workers)])
             else:
@@ -127,8 +131,6 @@ def log_info_about_onnx(onnx_runtime: bool):
     else:
         logger.info("Running on CPU")
 
-
-
     # Batch text tokenization enabled by default
     direct_tokenize = get_t2v_transformers_direct_tokenize()
 

cicd/test.sh

@@ -14,6 +14,14 @@ python3 smoke_auth_test.py
 
 docker stop $container_id
 
+echo "Running tests with enabled cache"
+
+container_id=$(docker run -d -it -e ENABLE_CACHE='1' -p "8000:8080" "$local_repo")
+
+python3 smoke_validate_cache_test.py
+
+docker stop $container_id
+
 echo "Running tests without authorization"
 
 container_id=$(docker run -d -it -p "8000:8080" "$local_repo")

smoke_validate_cache_test.py

@@ -48,6 +48,17 @@ def _test_vectorizing_sentences(self):
             self._try_to_vectorize(self.url + "/vectors/", sentence)
             self._try_to_vectorize(self.url + "/vectors", sentence)
 
+    def test_vectorize_payload_with_config(self):
+        weaviate_facts = [
+            "Vector database for semantic search.",
+            "Supports similarity-based queries.",
+            "Integrates with ML for classification.",
+        ]
+        for _ in range(10):
+            for fact in weaviate_facts:
+                self._try_to_vectorize(self.url + "/vectors/", fact, "query")
+                self._try_to_vectorize(self.url + "/vectors", fact, "passage")
+
     def test_vectorizing_cached_results(self):
         start = time.time()
         before = {}

vectorizer.py

@@ -34,11 +34,30 @@ class VectorInputConfig(BaseModel):
     pooling_strategy: Optional[str] = None
     task_type: Optional[str] = None
 
+    def __hash__(self):
+        return hash((self.pooling_strategy, self.task_type))
+
+    def __eq__(self, other):
+        if isinstance(other, VectorInputConfig):
+            return (
+                self.pooling_strategy == other.pooling_strategy
+                and self.task_type == other.task_type
+            )
+        return False
+
 
 class VectorInput(BaseModel):
     text: str
     config: Optional[VectorInputConfig] = None
 
+    def __hash__(self):
+        return hash((self.text, self.config))
+
+    def __eq__(self, other):
+        if isinstance(other, VectorInput):
+            return self.text == other.text and self.config == other.config
+        return False
+
 
 class Vectorizer:
     executor: ThreadPoolExecutor