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fix(router): update VectorIORouter to use new params signature

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VectorIORouter was still using old individual parameter signature instead of the new params object. Updated both openai_create_vector_store and openai_create_vector_store_file_batch methods to match the API protocol.
This commit is contained in:
Ashwin Bharambe 2025-10-11 17:02:23 -07:00
parent 74e2976c1e
commit 8fa91f98ef
1 changed files with 28 additions and 30 deletions
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58
llama_stack/core/routers/vector_io.py
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@ -6,12 +6,16 @@
import asyncio import asyncio
import uuid import uuid
from typing import Any from typing import Annotated, Any
from fastapi import Body
from llama_stack.apis.common.content_types import InterleavedContent from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.models import ModelType from llama_stack.apis.models import ModelType
from llama_stack.apis.vector_io import ( from llama_stack.apis.vector_io import (
Chunk, Chunk,
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
OpenAICreateVectorStoreRequestWithExtraBody,
QueryChunksResponse, QueryChunksResponse,
SearchRankingOptions, SearchRankingOptions,
VectorIO, VectorIO,
@ -120,18 +124,13 @@ class VectorIORouter(VectorIO):
# OpenAI Vector Stores API endpoints # OpenAI Vector Stores API endpoints
async def openai_create_vector_store( async def openai_create_vector_store(
self, self,
name: str, params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
file_ids: list[str] | None = None,
expires_after: dict[str, Any] | None = None,
chunking_strategy: dict[str, Any] | None = None,
metadata: dict[str, Any] | None = None,
embedding_model: str | None = None,
embedding_dimension: int | None = None,
provider_id: str | None = None,
) -> VectorStoreObject: ) -> VectorStoreObject:
logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}") logger.debug(f"VectorIORouter.openai_create_vector_store: name={params.name}, provider_id={params.provider_id}")
# If no embedding model is provided, use the first available one # If no embedding model is provided, use the first available one
embedding_model = params.embedding_model
embedding_dimension = params.embedding_dimension
if embedding_model is None: if embedding_model is None:
embedding_model_info = await self._get_first_embedding_model() embedding_model_info = await self._get_first_embedding_model()
if embedding_model_info is None: if embedding_model_info is None:
@ -144,22 +143,23 @@ class VectorIORouter(VectorIO):
vector_db_id=vector_db_id, vector_db_id=vector_db_id,
embedding_model=embedding_model, embedding_model=embedding_model,
embedding_dimension=embedding_dimension, embedding_dimension=embedding_dimension,
provider_id=provider_id, provider_id=params.provider_id,
provider_vector_db_id=vector_db_id, provider_vector_db_id=vector_db_id,
vector_db_name=name, vector_db_name=params.name,
) )
provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier) provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier)
return await provider.openai_create_vector_store(
name=name, # Update params with resolved values
file_ids=file_ids, params.embedding_model = embedding_model
expires_after=expires_after, params.embedding_dimension = embedding_dimension
chunking_strategy=chunking_strategy, params.provider_id = registered_vector_db.provider_id
metadata=metadata,
embedding_model=embedding_model, # Add provider_vector_db_id to extra_body if not already there
embedding_dimension=embedding_dimension, if params.model_extra is None:
provider_id=registered_vector_db.provider_id, params.model_extra = {}
provider_vector_db_id=registered_vector_db.provider_resource_id, params.model_extra["provider_vector_db_id"] = registered_vector_db.provider_resource_id
)
return await provider.openai_create_vector_store(params)
async def openai_list_vector_stores( async def openai_list_vector_stores(
self, self,
@ -370,16 +370,14 @@ class VectorIORouter(VectorIO):
async def openai_create_vector_store_file_batch( async def openai_create_vector_store_file_batch(
self, self,
vector_store_id: str, vector_store_id: str,
file_ids: list[str], params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
attributes: dict[str, Any] | None = None,
chunking_strategy: VectorStoreChunkingStrategy | None = None,
) -> VectorStoreFileBatchObject: ) -> VectorStoreFileBatchObject:
logger.debug(f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(file_ids)} files") logger.debug(
f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(params.file_ids)} files"
)
return await self.routing_table.openai_create_vector_store_file_batch( return await self.routing_table.openai_create_vector_store_file_batch(
vector_store_id=vector_store_id, vector_store_id=vector_store_id,
file_ids=file_ids, params=params,
attributes=attributes,
chunking_strategy=chunking_strategy,
) )
async def openai_retrieve_vector_store_file_batch( async def openai_retrieve_vector_store_file_batch(