Run filter using the specified packages/ecosystem

Author: ptelang

src/codegate/llm_utils/extractor.py

@@ -30,7 +30,7 @@ async def extract_packages(
         system_prompt = Config.get_config().prompts.lookup_packages
 
         result = await LLMClient.complete(
-            content=content,
+            content=content.lower(),
             system_prompt=system_prompt,
             provider=provider,
             model=model,
@@ -41,6 +41,9 @@ async def extract_packages(
 
         # Handle both formats: {"packages": [...]} and direct list [...]
         packages = result if isinstance(result, list) else result.get("packages", [])
+
+        # Filter packages based on the content
+        packages = [package.lower() for package in packages if package.lower() in content]
         logger.info(f"Extracted packages: {packages}")
         return packages
 

src/codegate/pipeline/codegate_context_retriever/codegate.py

@@ -29,12 +29,12 @@ def name(self) -> str:
         """
         return "codegate-context-retriever"
 
-    async def get_objects_from_search(
-        self, search: str, ecosystem, packages: list[str] = None
+    async def get_objects_from_db(
+        self, ecosystem, packages: list[str] = None
     ) -> list[object]:
         storage_engine = StorageEngine()
         objects = await storage_engine.search(
-            search, distance=0.8, ecosystem=ecosystem, packages=packages
+            distance=0.8, ecosystem=ecosystem, packages=packages
         )
         return objects
 
@@ -103,39 +103,25 @@ async def process(
         # Extract packages from the user message
         ecosystem = await self.__lookup_ecosystem(user_messages, context)
         packages = await self.__lookup_packages(user_messages, context)
-        packages = [pkg.lower() for pkg in packages]
 
-        # If user message does not reference any packages, then just return
-        if len(packages) == 0:
-            return PipelineResult(request=request)
-
-        # Look for matches in vector DB using list of packages as filter
-        searched_objects = await self.get_objects_from_search(user_messages, ecosystem, packages)
+        context_str = "CodeGate did not find any malicious or archived packages."
 
-        logger.info(
-            f"Found {len(searched_objects)} matches in the database",
-            searched_objects=searched_objects,
-        )
+        if len(packages) > 0:
+            # Look for matches in DB using packages and ecosystem
+            searched_objects = await self.get_objects_from_db(ecosystem, packages)
 
-        # Remove searched objects that are not in packages. This is needed
-        # since Weaviate performs substring match in the filter.
-        updated_searched_objects = []
-        for searched_object in searched_objects:
-            if searched_object.properties["name"].lower() in packages:
-                updated_searched_objects.append(searched_object)
-        searched_objects = updated_searched_objects
+            logger.info(
+                f"Found {len(searched_objects)} matches in the database",
+                searched_objects=searched_objects,
+            )
 
-        # Generate context string using the searched objects
-        logger.info(f"Adding {len(searched_objects)} packages to the context")
+            # Generate context string using the searched objects
+            logger.info(f"Adding {len(searched_objects)} packages to the context")
 
-        if len(searched_objects) > 0:
-            context_str = self.generate_context_str(searched_objects, context)
-        else:
-            context_str = "CodeGate did not find any malicious or archived packages."
+            if len(searched_objects) > 0:
+                context_str = self.generate_context_str(searched_objects, context)
 
         last_user_idx = self.get_last_user_message_idx(request)
-        if last_user_idx == -1:
-            return PipelineResult(request=request, context=context)
 
         # Make a copy of the request
         new_request = request.copy()

src/codegate/storage/storage_engine.py

@@ -121,7 +121,6 @@ async def search_by_property(self, name: str, properties: List[str]) -> list[obj
             return []
 
         try:
-
             packages = self.weaviate_client.collections.get("Package")
             response = packages.query.fetch_objects(
                 filters=Filter.by_property(name).contains_any(properties),
@@ -145,10 +144,19 @@ async def search_by_property(self, name: str, properties: List[str]) -> list[obj
             return []
 
     async def search(
-        self, query: str, limit=5, distance=0.3, ecosystem=None, packages=None
+        self,
+        query: str = None,
+        ecosystem: str = None,
+        packages: List[str] = None,
+        limit: int = 5,
+        distance: float = 0.3,
     ) -> list[object]:
         """
-        Search the 'Package' collection based on a query string.
+        Search the 'Package' collection based on a query string, ecosystem and packages.
+        If packages and ecosystem are both not none, then filter the objects using them.
+        If packages is not none and ecosystem is none, then filter the objects using
+        package names.
+        If packages is none, then perform vector search.
 
         Args:
             query (str): The text query for which to search.
@@ -160,26 +168,40 @@ async def search(
         Returns:
             list: A list of matching results with their properties and distances.
         """
-        # Generate the vector for the query
-        query_vector = await self.inference_engine.embed(self.model_path, [query])
 
-        # Perform the vector search
         try:
             collection = self.weaviate_client.collections.get("Package")
-            if packages:
-                # filter by packages and ecosystem if present
-                filters = []
-                if ecosystem and ecosystem in VALID_ECOSYSTEMS:
-                    filters.append(wvc.query.Filter.by_property("type").equal(ecosystem))
-                filters.append(wvc.query.Filter.by_property("name").contains_any(packages))
-                response = collection.query.near_vector(
-                    query_vector[0],
-                    limit=limit,
-                    distance=distance,
-                    filters=wvc.query.Filter.all_of(filters),
-                    return_metadata=MetadataQuery(distance=True),
+
+            response = None
+            if packages and ecosystem and ecosystem in VALID_ECOSYSTEMS:
+                response = collection.query.fetch_objects(
+                    filters=wvc.query.Filter.all_of([
+                        wvc.query.Filter.by_property("name").contains_any(packages),
+                        wvc.query.Filter.by_property("type").equal(ecosystem)
+                    ]),
                 )
-            else:
+                response.objects = [
+                    obj
+                    for obj in response.objects
+                    if obj.properties["name"].lower() in packages
+                    and obj.properties["type"].lower() == ecosystem.lower()
+                ]
+            elif packages and not ecosystem:
+                response = collection.query.fetch_objects(
+                    filters=wvc.query.Filter.all_of([
+                        wvc.query.Filter.by_property("name").contains_any(packages),
+                    ]),
+                )
+                response.objects = [
+                    obj
+                    for obj in response.objects
+                    if obj.properties["name"].lower() in packages
+                ]
+            elif query:
+                # Perform the vector search
+                # Generate the vector for the query
+                query_vector = await self.inference_engine.embed(self.model_path, [query])
+
                 response = collection.query.near_vector(
                     query_vector[0],
                     limit=limit,

tests/test_storage.py

@@ -52,7 +52,7 @@ async def test_search(mock_weaviate_client, mock_inference_engine):
             storage_engine = StorageEngine.recreate_instance(data_path="./weaviate_data")
 
             # Invoke the search method
-            results = await storage_engine.search("test query", 5, 0.3)
+            results = await storage_engine.search(query="test query", limit=5, distance=0.3)
 
             # Assertions to validate the expected behavior
             assert len(results) == 1  # Assert that one result is returned