Check to ensure correct embedding vector dimensions are used (#177)

Currently our semantic cache allows for specifying the vector in calls
to store() and check(), but if the vector dimension does not match the
schema dimensions this fails silently. This PR adds a check to verify
correct vector dimensions and raises an error if they do not match.

redisvl/extensions/llmcache/semantic.py

@@ -234,6 +234,17 @@ def _search_cache(
                 )
         return cache_hits
 
+    def _check_vector_dims(self, vector: List[float]):
+        """Checks the size of the provided vector and raises an error if it
+        doesn't match the search index vector dimensions."""
+        schema_vector_dims = self._index.schema.fields[self.vector_field_name].attrs.dims  # type: ignore
+        if schema_vector_dims != len(vector):
+            raise ValueError(
+                "Invalid vector dimensions! "
+                f"Vector has dims defined as {len(vector)}",
+                f"Vector field has dims defined as {schema_vector_dims}",
+            )
+
     def check(
         self,
         prompt: Optional[str] = None,
@@ -266,6 +277,7 @@ def check(
 
         Raises:
             ValueError: If neither a `prompt` nor a `vector` is specified.
+            ValueError: if 'vector' has incorrect dimensions.
             TypeError: If `return_fields` is not a list when provided.
 
         .. code-block:: python
@@ -279,6 +291,7 @@ def check(
 
         # Use provided vector or create from prompt
         vector = vector or self._vectorize_prompt(prompt)
+        self._check_vector_dims(vector)
 
         # Check for cache hits by searching the cache
         cache_hits = self._search_cache(vector, num_results, return_fields)
@@ -307,6 +320,7 @@ def store(
 
         Raises:
             ValueError: If neither prompt nor vector is specified.
+            ValueError: if vector has incorrect dimensions.
             TypeError: If provided metadata is not a dictionary.
 
         .. code-block:: python
@@ -319,6 +333,8 @@ def store(
         """
         # Vectorize prompt if necessary and create cache payload
         vector = vector or self._vectorize_prompt(prompt)
+        self._check_vector_dims(vector)
+
         # Construct semantic cache payload
         id_field = self.entry_id_field_name
         payload = {

tests/integration/test_llmcache.py

@@ -210,3 +210,31 @@ def test_store_and_check_with_provided_client(cache_with_redis_client, vectorize
 def test_delete(cache_no_cleanup):
     cache_no_cleanup.delete()
     assert not cache_no_cleanup.index.exists()
+
+
+# Test we can only store and check vectors of correct dimensions
+def test_vector_size(cache, vectorizer):
+    prompt = "This is test prompt."
+    response = "This is a test response."
+
+    vector = vectorizer.embed(prompt)
+    cache.store(prompt=prompt, response=response, vector=vector)
+
+    # Test we can query with modified embeddings of correct size
+    vector_2 = [v * 0.99 for v in vector]  # same dimensions
+    check_result = cache.check(vector=vector_2)
+    assert check_result[0]["prompt"] == prompt
+
+    # Test that error is raised when we try to load wrong size vectors
+    with pytest.raises(ValueError):
+        cache.store(prompt=prompt, response=response, vector=vector[0:-1])
+
+    with pytest.raises(ValueError):
+        cache.store(prompt=prompt, response=response, vector=[1, 2, 3])
+
+    # Test that error is raised when we try to query with wrong size vector
+    with pytest.raises(ValueError):
+        cache.check(vector=vector[0:-1])
+
+    with pytest.raises(ValueError):
+        cache.check(vector=[1, 2, 3])