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feat: Updating Rag Tool to use Files API and Vector Stores API

Browse source 
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-09-06 00:44:44 -04:00
parent 47b640370e
commit ab5ab6e979
6 changed files with 93 additions and 39 deletions
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5
docs/source/getting_started/demo_script.py
View file

@ -18,12 +18,13 @@ embedding_model_id = (
).identifier
embedding_dimension = em.metadata["embedding_dimension"]
_ = client.vector_dbs.register(
vector_db = client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
provider_id="faiss",
)
vector_db_id = vector_db.identifier
source = "https://www.paulgraham.com/greatwork.html"
print("rag_tool> Ingesting document:", source)
document = RAGDocument(
@ -35,7 +36,7 @@ document = RAGDocument(
client.tool_runtime.rag_tool.insert(
documents=[document],
vector_db_id=vector_db_id,
chunk_size_in_tokens=50,
chunk_size_in_tokens=100,
)
agent = Agent(
client,

2
llama_stack/providers/inline/tool_runtime/rag/__init__.py
View file

@ -14,6 +14,6 @@ from .config import RagToolRuntimeConfig
async def get_provider_impl(config: RagToolRuntimeConfig, deps: dict[Api, Any]):
from .memory import MemoryToolRuntimeImpl
impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference])
impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference], deps[Api.files])
await impl.initialize()
return impl

74
llama_stack/providers/inline/tool_runtime/rag/memory.py
View file

@ -5,10 +5,15 @@
# the root directory of this source tree.
import asyncio
import base64
import io
import mimetypes
import secrets
import string
from typing import Any
import httpx
from fastapi import UploadFile
from pydantic import TypeAdapter
from llama_stack.apis.common.content_types import (
@ -17,6 +22,7 @@ from llama_stack.apis.common.content_types import (
InterleavedContentItem,
TextContentItem,
)
from llama_stack.apis.files import Files, OpenAIFilePurpose
from llama_stack.apis.inference import Inference
from llama_stack.apis.tools import (
ListToolDefsResponse,
@ -30,13 +36,18 @@ from llama_stack.apis.tools import (
ToolParameter,
ToolRuntime,
)
from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO
from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
VectorStoreChunkingStrategyStatic,
VectorStoreChunkingStrategyStaticConfig,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
from llama_stack.providers.utils.memory.vector_store import (
content_from_doc,
make_overlapped_chunks,
parse_data_url,
)
from .config import RagToolRuntimeConfig
@ -55,10 +66,12 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
config: RagToolRuntimeConfig,
vector_io_api: VectorIO,
inference_api: Inference,
files_api: Files,
):
self.config = config
self.vector_io_api = vector_io_api
self.inference_api = inference_api
self.files_api = files_api
async def initialize(self):
pass
@ -78,27 +91,50 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
vector_db_id: str,
chunk_size_in_tokens: int = 512,
) -> None:
chunks = []
if not documents:
return
for doc in documents:
content = await content_from_doc(doc)
# TODO: we should add enrichment here as URLs won't be added to the metadata by default
chunks.extend(
make_overlapped_chunks(
doc.document_id,
content,
chunk_size_in_tokens,
chunk_size_in_tokens // 4,
doc.metadata,
if isinstance(doc.content, URL):
if doc.content.uri.startswith("data:"):
parts = parse_data_url(doc.content.uri)
file_data = base64.b64decode(parts["data"]) if parts["is_base64"] else parts["data"].encode()
mime_type = parts["mimetype"]
else:
async with httpx.AsyncClient() as client:
response = await client.get(doc.content.uri)
file_data = response.content
mime_type = doc.mime_type or response.headers.get("content-type", "application/octet-stream")
else:
content_str = await content_from_doc(doc)
file_data = content_str.encode("utf-8")
mime_type = doc.mime_type or "text/plain"
file_extension = mimetypes.guess_extension(mime_type) or ".txt"
filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}")
file_obj = io.BytesIO(file_data)
file_obj.name = filename
upload_file = UploadFile(file=file_obj, filename=filename)
created_file = await self.files_api.openai_upload_file(
file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS
)
chunking_strategy = VectorStoreChunkingStrategyStatic(
static=VectorStoreChunkingStrategyStaticConfig(
max_chunk_size_tokens=chunk_size_in_tokens,
chunk_overlap_tokens=chunk_size_in_tokens // 4,
)
)
if not chunks:
return
await self.vector_io_api.insert_chunks(
chunks=chunks,
vector_db_id=vector_db_id,
)
await self.vector_io_api.openai_attach_file_to_vector_store(
vector_store_id=vector_db_id,
file_id=created_file.id,
attributes=doc.metadata,
chunking_strategy=chunking_strategy,
)
async def query(
self,

2
llama_stack/providers/registry/tool_runtime.py
View file

@ -32,7 +32,7 @@ def available_providers() -> list[ProviderSpec]:
],
module="llama_stack.providers.inline.tool_runtime.rag",
config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig",
api_dependencies=[Api.vector_io, Api.inference],
api_dependencies=[Api.vector_io, Api.inference, Api.files],
description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.",
),
remote_provider_spec(

41
tests/integration/tool_runtime/test_rag_tool.py
View file

@ -17,10 +17,14 @@ def client_with_empty_registry(client_with_models):
client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id)
clear_registry()
try:
client_with_models.toolgroups.register(toolgroup_id="builtin::rag", provider_id="rag-runtime")
except Exception:
pass
yield client_with_models
# you must clean after the last test if you were running tests against
# a stateful server instance
clear_registry()
@ -66,12 +70,13 @@ def assert_valid_text_response(response):
def test_vector_db_insert_inline_and_query(
client_with_empty_registry, sample_documents, embedding_model_id, embedding_dimension
):
vector_db_id = "test_vector_db"
client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_id,
vector_db_name = "test_vector_db"
vector_db = client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_name,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
)
vector_db_id = vector_db.identifier
client_with_empty_registry.tool_runtime.rag_tool.insert(
documents=sample_documents,
@ -134,7 +139,11 @@ def test_vector_db_insert_from_url_and_query(
# list to check memory bank is successfully registered
available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
assert vector_db_id in available_vector_dbs
# VectorDB is being migrated to VectorStore, so the ID will be different
# Just check that at least one vector DB was registered
assert len(available_vector_dbs) > 0
# Use the actual registered vector_db_id for subsequent operations
actual_vector_db_id = available_vector_dbs[0]
urls = [
"memory_optimizations.rst",
@ -153,13 +162,13 @@ def test_vector_db_insert_from_url_and_query(
client_with_empty_registry.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
chunk_size_in_tokens=512,
)
# Query for the name of method
response1 = client_with_empty_registry.vector_io.query(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
query="What's the name of the fine-tunning method used?",
)
assert_valid_chunk_response(response1)
@ -167,7 +176,7 @@ def test_vector_db_insert_from_url_and_query(
# Query for the name of model
response2 = client_with_empty_registry.vector_io.query(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
query="Which Llama model is mentioned?",
)
assert_valid_chunk_response(response2)
@ -187,7 +196,11 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i
)
available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
assert vector_db_id in available_vector_dbs
# VectorDB is being migrated to VectorStore, so the ID will be different
# Just check that at least one vector DB was registered
assert len(available_vector_dbs) > 0
# Use the actual registered vector_db_id for subsequent operations
actual_vector_db_id = available_vector_dbs[0]
urls = [
"memory_optimizations.rst",
@ -206,19 +219,19 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i
client_with_empty_registry.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
chunk_size_in_tokens=512,
)
response_with_metadata = client_with_empty_registry.tool_runtime.rag_tool.query(
vector_db_ids=[vector_db_id],
vector_db_ids=[actual_vector_db_id],
content="What is the name of the method used for fine-tuning?",
)
assert_valid_text_response(response_with_metadata)
assert any("metadata:" in chunk.text.lower() for chunk in response_with_metadata.content)
response_without_metadata = client_with_empty_registry.tool_runtime.rag_tool.query(
vector_db_ids=[vector_db_id],
vector_db_ids=[actual_vector_db_id],
content="What is the name of the method used for fine-tuning?",
query_config={
"include_metadata_in_content": True,
@ -230,7 +243,7 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i
with pytest.raises((ValueError, BadRequestError)):
client_with_empty_registry.tool_runtime.rag_tool.query(
vector_db_ids=[vector_db_id],
vector_db_ids=[actual_vector_db_id],
content="What is the name of the method used for fine-tuning?",
query_config={
"chunk_template": "This should raise a ValueError because it is missing the proper template variables",

8
tests/unit/rag/test_rag_query.py
View file

@ -19,12 +19,16 @@ from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRunti
class TestRagQuery:
async def test_query_raises_on_empty_vector_db_ids(self):
rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock())
rag_tool = MemoryToolRuntimeImpl(
config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
)
with pytest.raises(ValueError):
await rag_tool.query(content=MagicMock(), vector_db_ids=[])
async def test_query_chunk_metadata_handling(self):
rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock())
rag_tool = MemoryToolRuntimeImpl(
config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
)
content = "test query content"
vector_db_ids = ["db1"]