mypy: inline::{vector_io,tool_runtime,telemetry}

This makes a minor change to tools API, allowing for empty content from
tool invocation. This is because RAGQueryResult allows for empty
content.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -15,11 +15,13 @@ import faiss
 import numpy as np
 from numpy.typing import NDArray
 
-from llama_stack.apis.inference import InterleavedContent
+from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.inference.inference import Inference
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
-from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
+from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
@@ -35,16 +37,14 @@ FAISS_INDEX_PREFIX = f"faiss_index:{VERSION}::"
 
 
 class FaissIndex(EmbeddingIndex):
-    chunk_by_index: Dict[int, str]
-
-    def __init__(self, dimension: int, kvstore=None, bank_id: str = None):
+    def __init__(self, dimension: int, kvstore: KVStore | None = None, bank_id: str | None = None):
         self.index = faiss.IndexFlatL2(dimension)
-        self.chunk_by_index = {}
+        self.chunk_by_index: dict[int, Chunk] = {}
         self.kvstore = kvstore
         self.bank_id = bank_id
 
     @classmethod
-    async def create(cls, dimension: int, kvstore=None, bank_id: str = None):
+    async def create(cls, dimension: int, kvstore: KVStore | None = None, bank_id: str | None = None):
         instance = cls(dimension, kvstore, bank_id)
         await instance.initialize()
         return instance
@@ -114,11 +114,11 @@ class FaissIndex(EmbeddingIndex):
 
 
 class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: FaissVectorIOConfig, inference_api: Api.inference) -> None:
+    def __init__(self, config: FaissVectorIOConfig, inference_api: Inference) -> None:
         self.config = config
         self.inference_api = inference_api
-        self.cache = {}
-        self.kvstore = None
+        self.cache: dict[str, VectorDBWithIndex] = {}
+        self.kvstore: KVStore | None = None
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)
@@ -144,6 +144,8 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         self,
         vector_db: VectorDB,
     ) -> None:
+        assert self.kvstore is not None
+
         key = f"{VECTOR_DBS_PREFIX}{vector_db.identifier}"
         await self.kvstore.set(
             key=key,
@@ -161,6 +163,8 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         return [i.vector_db for i in self.cache.values()]
 
     async def unregister_vector_db(self, vector_db_id: str) -> None:
+        assert self.kvstore is not None
+
         if vector_db_id not in self.cache:
             logger.warning(f"Vector DB {vector_db_id} not found")
             return

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -15,9 +15,10 @@ import numpy as np
 import sqlite_vec
 from numpy.typing import NDArray
 
+from llama_stack.apis.inference.inference import Inference
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
-from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.memory.vector_store import EmbeddingIndex, VectorDBWithIndex
 
 logger = logging.getLogger(__name__)
@@ -78,6 +79,8 @@ class SQLiteVecIndex(EmbeddingIndex):
         embedding (serialized to raw bytes) into the virtual table using the assigned rowid.
         If any insert fails, the transaction is rolled back to maintain consistency.
         """
+        assert all(isinstance(chunk.content, str) for chunk in chunks), "SQLiteVecIndex only supports text chunks"
+
         cur = self.connection.cursor()
         try:
             # Start transaction
@@ -89,6 +92,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                 metadata_data = [
                     (generate_chunk_id(chunk.metadata["document_id"], chunk.content), chunk.model_dump_json())
                     for chunk in batch_chunks
+                    if isinstance(chunk.content, str)
                 ]
                 # Insert metadata (ON CONFLICT to avoid duplicates)
                 cur.executemany(
@@ -103,6 +107,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                 embedding_data = [
                     (generate_chunk_id(chunk.metadata["document_id"], chunk.content), serialize_vector(emb.tolist()))
                     for chunk, emb in zip(batch_chunks, batch_embeddings, strict=True)
+                    if isinstance(chunk.content, str)
                 ]
                 # Insert embeddings in batch
                 cur.executemany(f"INSERT INTO {self.vector_table} (id, embedding) VALUES (?, ?);", embedding_data)
@@ -154,7 +159,7 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
     and creates a cache of VectorDBWithIndex instances (each wrapping a SQLiteVecIndex).
     """
 
-    def __init__(self, config, inference_api: Api.inference) -> None:
+    def __init__(self, config, inference_api: Inference) -> None:
         self.config = config
         self.inference_api = inference_api
         self.cache: Dict[str, VectorDBWithIndex] = {}