feat: File search tool for Responses API (#2426)

# What does this PR do?

This is an initial working prototype of wiring up the `file_search`
builtin tool for the Responses API to our existing rag knowledge search
tool.

This is me seeing what I could pull together on top of the bits we
already have merged. This may not be the ideal way to implement this,
and things like how I shuffle the vector store ids from the original
response API tool request to the actual tool execution feel a bit hacky
(grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see
what I mean).

## Test Plan

I stubbed in some new tests to exercise this using text and pdf
documents.

Note that this is currently under tests/verification only because it
sometimes flakes with tool calling of the small Llama-3.2-3B model we
run in CI (and that I use as an example below). We'd want to make the
test a bit more robust in some way if we moved this over to
tests/integration and ran it in CI.

### OpenAI SaaS (to verify test correctness)

```
pytest -sv tests/verifications/openai_api/test_responses.py \
  -k 'file_search' \
  --base-url=https://api.openai.com/v1 \
  --model=gpt-4o
```

### Fireworks with faiss vector store

```
llama stack run llama_stack/templates/fireworks/run.yaml

pytest -sv tests/verifications/openai_api/test_responses.py \
  -k 'file_search' \
  --base-url=http://localhost:8321/v1/openai/v1 \
  --model=meta-llama/Llama-3.3-70B-Instruct
```

### Ollama with faiss vector store

This sometimes flakes on Ollama because the quantized small model
doesn't always choose to call the tool to answer the user's question.
But, it often works.

```
ollama run llama3.2:3b

INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
  --image-type venv \
  --env OLLAMA_URL="http://0.0.0.0:11434"

pytest -sv tests/verifications/openai_api/test_responses.py \
  -k'file_search' \
  --base-url=http://localhost:8321/v1/openai/v1 \
  --model=meta-llama/Llama-3.2-3B-Instruct
```

### OpenAI provider with sqlite-vec vector store

```
llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv

 pytest -sv tests/verifications/openai_api/test_responses.py \
  -k 'file_search' \
  --base-url=http://localhost:8321/v1/openai/v1 \
  --model=openai/gpt-4o-mini
```

### Ensure existing vector store integration tests still pass

```
ollama run llama3.2:3b

INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
  --image-type venv \
  --env OLLAMA_URL="http://0.0.0.0:11434"

LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io \
  --text-model "meta-llama/Llama-3.2-3B-Instruct" \
  --embedding-model=all-MiniLM-L6-v2
```

---------

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

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -23,6 +23,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreObject,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
@@ -241,3 +242,12 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         rewrite_query: bool | None = False,
     ) -> VectorStoreSearchResponsePage:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
+
+    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 API is not supported in Chroma")

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -25,6 +25,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreObject,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
@@ -240,6 +241,15 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
     ) -> VectorStoreSearchResponsePage:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
 
+    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 API is not supported in Milvus")
+
 
 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/remote/vector_io/qdrant/qdrant.py

@@ -23,6 +23,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreObject,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
@@ -241,3 +242,12 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         rewrite_query: bool | None = False,
     ) -> VectorStoreSearchResponsePage:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
+
+    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 API is not supported in Qdrant")