fix: add proper type annotations for filters parameter in vector providers

Author: franciscojavierarceo

src/llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -20,6 +20,7 @@
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import ChunkForDeletion, EmbeddingIndex, VectorStoreWithIndex
 from llama_stack.providers.utils.vector_io.filters import Filter
+from llama_stack_api.filters import ComparisonFilter, CompoundFilter
 from llama_stack.providers.utils.vector_io.vector_utils import (
     WeightedInMemoryAggregator,
     load_embedded_chunk_with_backward_compat,
@@ -276,7 +277,7 @@ async def query_vector(
 
             return QueryChunksResponse(chunks=chunks, scores=scores)
 
-    async def query_keyword(self, query_string: str, k: int, score_threshold: float, filters: Any = None) -> QueryChunksResponse:
+    async def query_keyword(self, query_string: str, k: int, score_threshold: float, filters: ComparisonFilter | CompoundFilter | None = None) -> QueryChunksResponse:
         """
         Performs keyword-based search using PostgreSQL's full-text search with ts_rank scoring.
 
@@ -324,7 +325,7 @@ async def query_hybrid(
         score_threshold: float,
         reranker_type: str,
         reranker_params: dict[str, Any] | None = None,
-        filters: Any = None,
+        filters: ComparisonFilter | CompoundFilter | None = None,
     ) -> QueryChunksResponse:
         """
         Hybrid search combining vector similarity and keyword search using configurable reranking.

src/llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -24,6 +24,7 @@
 )
 from llama_stack.providers.utils.vector_io import load_embedded_chunk_with_backward_compat
 from llama_stack.providers.utils.vector_io.filters import Filter as LlamaStackFilter
+from llama_stack_api.filters import ComparisonFilter, CompoundFilter
 from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collection_name
 from llama_stack_api import (
     EmbeddedChunk,
@@ -155,7 +156,7 @@ async def delete(self, chunk_ids: list[str] | None = None) -> None:
         collection = self.client.collections.get(sanitized_collection_name)
         collection.data.delete_many(where=Filter.by_property("id").contains_any(chunk_ids))
 
-    async def query_keyword(self, query_string: str, k: int, score_threshold: float, filters: Any = None) -> QueryChunksResponse:
+    async def query_keyword(self, query_string: str, k: int, score_threshold: float, filters: ComparisonFilter | CompoundFilter | None = None) -> QueryChunksResponse:
         """
         Performs BM25-based keyword search using Weaviate's built-in full-text search.
         Args:
@@ -211,7 +212,7 @@ async def query_hybrid(
         score_threshold: float,
         reranker_type: str,
         reranker_params: dict[str, Any] | None = None,
-        filters: Any = None,
+        filters: ComparisonFilter | CompoundFilter | None = None,
     ) -> QueryChunksResponse:
         """
         Hybrid search combining vector similarity and keyword search using Weaviate's native hybrid search.