sqlite-vec support for Responses file_search

This wires up the Files API optional dependency into sqlite_vec and
adds the localfs Files provider to our starter template, so that
Responses API file_search tool works out of the box for sqlite_vec in
that template.

Some additional testing with this provider plus some other inference
models led me to loosen the verification test results checking a bit -
not for the tool call, but just around the assistant response with the
file_search tool call. Some providers, such as OpenAI SaaS, make
multiple tool calls to resolve the query sometimes, especially when it
cannot find an answer so tries a few permutations before returning
empty results to the user in that test.

Signed-off-by: Ben Browning <bbrownin@redhat.com>

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

@@ -15,6 +15,6 @@ async def get_provider_impl(config: SQLiteVectorIOConfig, deps: dict[Api, Any]):
     from .sqlite_vec import SQLiteVecVectorIOAdapter
 
     assert isinstance(config, SQLiteVectorIOConfig), f"Unexpected config type: {type(config)}"
-    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference])
+    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files, None))
     await impl.initialize()
     return impl

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

@@ -17,6 +17,7 @@ import numpy as np
 import sqlite_vec
 from numpy.typing import NDArray
 
+from llama_stack.apis.files.files import Files
 from llama_stack.apis.inference.inference import Inference
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
@@ -24,7 +25,6 @@ from llama_stack.apis.vector_io import (
     QueryChunksResponse,
     VectorIO,
 )
-from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
 from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import EmbeddingIndex, VectorDBWithIndex
@@ -302,9 +302,10 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
     and creates a cache of VectorDBWithIndex instances (each wrapping a SQLiteVecIndex).
     """
 
-    def __init__(self, config, inference_api: Inference) -> None:
+    def __init__(self, config, inference_api: Inference, files_api: Files | None) -> None:
         self.config = config
         self.inference_api = inference_api
+        self.files_api = files_api
         self.cache: dict[str, VectorDBWithIndex] = {}
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
 
@@ -490,15 +491,6 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
             raise ValueError(f"Vector DB {vector_db_id} not found")
         return await self.cache[vector_db_id].query_chunks(query, params)
 
-    async def openai_attach_file_to_vector_store(
-        self,
-        vector_store_id: str,
-        file_id: str,
-        attributes: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores Files API is not supported in sqlite_vec")
-
 
 def generate_chunk_id(document_id: str, chunk_text: str) -> str:
     """Generate a unique chunk ID using a hash of document ID and chunk text."""

llama_stack/providers/registry/vector_io.py

@@ -44,6 +44,7 @@ def available_providers() -> list[ProviderSpec]:
             module="llama_stack.providers.inline.vector_io.sqlite_vec",
             config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
             api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
         ),
         InlineProviderSpec(
             api=Api.vector_io,
@@ -53,6 +54,7 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
             deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
             api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
         ),
         remote_provider_spec(
             Api.vector_io,

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -425,7 +425,7 @@ class OpenAIVectorStoreMixin(ABC):
             vector_store_id=vector_store_id,
         )
 
-        if not self.files_api:
+        if not hasattr(self, "files_api") or not self.files_api:
             vector_store_file_object.status = "failed"
             vector_store_file_object.last_error = VectorStoreFileLastError(
                 code="server_error",

llama_stack/templates/starter/build.yaml

@@ -17,6 +17,8 @@ distribution_spec:
     - inline::sqlite-vec
     - remote::chromadb
     - remote::pgvector
+    files:
+    - inline::localfs
     safety:
     - inline::llama-guard
     agents:

llama_stack/templates/starter/run.yaml

@@ -4,6 +4,7 @@ apis:
 - agents
 - datasetio
 - eval
+- files
 - inference
 - safety
 - scoring
@@ -75,6 +76,14 @@ providers:
       db: ${env.PGVECTOR_DB:}
       user: ${env.PGVECTOR_USER:}
       password: ${env.PGVECTOR_PASSWORD:}
+  files:
+  - provider_id: meta-reference-files
+    provider_type: inline::localfs
+    config:
+      storage_dir: ${env.FILES_STORAGE_DIR:~/.llama/distributions/starter/files}
+      metadata_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/files_metadata.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard

llama_stack/templates/starter/starter.py

@@ -12,6 +12,7 @@ from llama_stack.distribution.datatypes import (
     ShieldInput,
     ToolGroupInput,
 )
+from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
 from llama_stack.providers.inline.inference.sentence_transformers import (
     SentenceTransformersInferenceConfig,
 )
@@ -134,6 +135,7 @@ def get_distribution_template() -> DistributionTemplate:
     providers = {
         "inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
         "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
+        "files": ["inline::localfs"],
         "safety": ["inline::llama-guard"],
         "agents": ["inline::meta-reference"],
         "telemetry": ["inline::meta-reference"],
@@ -170,6 +172,11 @@ def get_distribution_template() -> DistributionTemplate:
             ),
         ),
     ]
+    files_provider = Provider(
+        provider_id="meta-reference-files",
+        provider_type="inline::localfs",
+        config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+    )
     embedding_provider = Provider(
         provider_id="sentence-transformers",
         provider_type="inline::sentence-transformers",
@@ -212,6 +219,7 @@ def get_distribution_template() -> DistributionTemplate:
                 provider_overrides={
                     "inference": inference_providers + [embedding_provider],
                     "vector_io": vector_io_providers,
+                    "files": [files_provider],
                 },
                 default_models=default_models + [embedding_model],
                 default_tool_groups=default_tool_groups,

tests/verifications/openai_api/test_responses.py

@@ -340,7 +340,7 @@ def test_response_non_streaming_file_search(
     response = openai_client.responses.create(
         model=model,
         input=case["input"],
-        tools=case["tools"],
+        tools=tools,
         stream=False,
         include=["file_search_call.results"],
     )
@@ -354,11 +354,7 @@ def test_response_non_streaming_file_search(
     assert case["output"].lower() in response.output[0].results[0].text.lower()
     assert response.output[0].results[0].score > 0
 
-    # Verify the assistant response that summarizes the results
-    assert response.output[1].type == "message"
-    assert response.output[1].status == "completed"
-    assert response.output[1].role == "assistant"
-    assert len(response.output[1].content) > 0
+    # Verify the output_text generated by the response
     assert case["output"].lower() in response.output_text.lower().strip()
 
 
@@ -390,11 +386,8 @@ def test_response_non_streaming_file_search_empty_vector_store(
     assert response.output[0].queries  # ensure it's some non-empty list
     assert not response.output[0].results  # ensure we don't get any results
 
-    # Verify the assistant response that summarizes the results
-    assert response.output[1].type == "message"
-    assert response.output[1].status == "completed"
-    assert response.output[1].role == "assistant"
-    assert len(response.output[1].content) > 0
+    # Verify some output_text was generated by the response
+    assert response.output_text
 
 
 @pytest.mark.parametrize(