feedback updates

redis-developer · Jul 31, 2024 · 304b96f · 304b96f
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commit 304b96f
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Showing 4 changed files with 18,956 additions and 16 deletions.
diff --git a/backend/productsearch/api/routes/product.py b/backend/productsearch/api/routes/product.py
@@ -46,8 +46,8 @@ async def get_products(
     gender: str = "",
     category: str = "",
     index: AsyncSearchIndex = Depends(redis_helpers.get_async_index),
-):
-    """_summary_
+) -> ProductSearchResponse:
+    """Fetch and return products based on gender and category fields
 
     Args:
         limit (int, optional): _description_. Defaults to 20.
@@ -56,7 +56,7 @@ async def get_products(
         category (str, optional): _description_. Defaults to "".
 
     Returns:
-        ProductSearchResponse model
+        ProductSearchResponse: Pydantic model containing products and total count
     """
     # Build query
     filter_expression = (Tag("gender") == gender) & (Tag("category") == category)
@@ -79,12 +79,30 @@ async def find_products_by_image(
     similarity_request: SimilarityRequest,
     index: AsyncSearchIndex = Depends(redis_helpers.get_async_index),
 ) -> ProductVectorSearchResponse:
+    """Fetch and return products based on image similarity
 
+    Args:
+        SimilarityRequest:
+            number_of_results (int): Number of results to return
+            search_type (str): Search type
+            gender (str): filter criteria
+            category (str): filter criteria
+
+    Returns:
+        ProductSearchResponse:
+            total (int): Total number of results
+            products (list[VectorSearchProduct]): List of products
+                product_id (str): Product ID
+                name (str): Product name
+                gender (str): fashion tag
+                category (str): fashion tag
+                img_url (str): Image URL for displaying in FE
+                text_vector (str): Text vector for similarity computation
+                img_vector (str): Image vector for similarity computation
+    """
     # Fetch paper key and the vector from the HASH, cast to numpy array
-    product_key = f"{index.schema.index.prefix}:{similarity_request.product_id}"
-    product_img_vector = np.frombuffer(
-        await index.client.hget(product_key, "img_vector"), dtype=np.float32
-    )
+    product = await index.fetch(similarity_request.product_id)
+    product_img_vector = np.frombuffer(product["img_vector"], dtype=np.float32)
 
     # Build filter expression
     filter_expression = (Tag("gender") == similarity_request.gender) & (
@@ -119,11 +137,30 @@ async def find_products_by_text(
     similarity_request: SimilarityRequest,
     index: AsyncSearchIndex = Depends(redis_helpers.get_async_index),
 ) -> ProductVectorSearchResponse:
+    """Fetch and return products based on image similarity
+
+    Args:
+        SimilarityRequest:
+            number_of_results (int): Number of results to return
+            search_type (str): Search type
+            gender (str): filter criteria
+            category (str): filter criteria
+
+    Returns:
+        ProductSearchResponse:
+            total (int): Total number of results
+            products (list[VectorSearchProduct]): List of products
+                product_id (str): Product ID
+                name (str): Product name
+                gender (str): fashion tag
+                category (str): fashion tag
+                img_url (str): Image URL for displaying in FE
+                text_vector (str): Text vector for similarity computation
+                img_vector (str): Image vector for similarity computation
+    """
     # Fetch paper key and the vector from the HASH, cast to numpy array
-    product_key = f"{index.schema.index.prefix}:{similarity_request.product_id}"
-    product_text_vector = np.frombuffer(
-        await index.client.hget(product_key, "text_vector"), dtype=np.float32
-    )
+    product = await index.fetch(similarity_request.product_id)
+    product_text_vector = np.frombuffer(product["text_vector"], dtype=np.float32)
 
     # Build filter expression
     filter_expression = (Tag("gender") == similarity_request.gender) & (

diff --git a/backend/productsearch/config.py b/backend/productsearch/config.py
@@ -16,7 +16,6 @@
 API_DOCS = "/api/docs"
 OPENAPI_DOCS = "/api/openapi.json"
 INDEX_NAME = "products"
-INDEX_TYPE = os.environ.get("VECSIM_INDEX_TYPE", "HNSW")  # TODO: change
 REDIS_HOST = os.environ.get("REDIS_HOST", "redis-vector-db")
 REDIS_PORT = os.environ.get("REDIS_PORT", 6379)
 REDIS_DB = os.environ.get("REDIS_DB", 0)
@@ -27,11 +26,8 @@
 else:
     REDIS_URL = f"redis://{REDIS_HOST}:{REDIS_PORT}"
 
-os.environ["REDIS_DATA_URL"] = REDIS_URL
-os.environ["REDIS_OM_URL"] = REDIS_URL
 API_V1_STR = "/api/v1"
 DATA_LOCATION = os.environ.get("DATA_LOCATION", "../data")
-DEFAULT_RETURN_FIELDS = ["product_id", "product_pk", "vector_score"]
 DEPLOYMENT_ENV = os.environ.get("DEPLOYMENT", "dev")
 
 WRITE_CONCURRENCY = os.environ.get("WRITE_CONCURRENCY", 150)