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Merge remote-tracking branch 'origin/main' into migrate-safety-to-openai

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This commit is contained in:
Ashwin Bharambe 2025-09-29 13:10:39 -07:00
commit 9d27f214d7
31 changed files with 247 additions and 720 deletions
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2
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -924,7 +924,7 @@ async def get_raw_document_text(document: Document) -> str:
DeprecationWarning,
stacklevel=2,
)
elif not (document.mime_type.startswith("text/") or document.mime_type == "application/yaml"):
elif not (document.mime_type.startswith("text/") or document.mime_type in ("application/yaml", "application/json")):
raise ValueError(f"Unexpected document mime type: {document.mime_type}")
if isinstance(document.content, URL):

36
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -11,21 +11,17 @@ from botocore.client import BaseClient
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
OpenAIEmbeddingsResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -47,8 +43,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
)
from .models import MODEL_ENTRIES
@ -218,36 +212,6 @@ class BedrockInferenceAdapter(
),
}
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
# Convert foundation model ID to inference profile ID
region_name = self.client.meta.region_name
inference_profile_id = _to_inference_profile_id(model.provider_resource_id, region_name)
embeddings = []
for content in contents:
assert not content_has_media(content), "Bedrock does not support media for embeddings"
input_text = interleaved_content_as_str(content)
input_body = {"inputText": input_text}
body = json.dumps(input_body)
response = self.client.invoke_model(
body=body,
modelId=inference_profile_id,
accept="application/json",
contentType="application/json",
)
response_body = json.loads(response.get("body").read())
embeddings.append(response_body.get("embedding"))
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,

14
llama_stack/providers/remote/inference/cerebras/cerebras.py
View file

@ -11,21 +11,17 @@ from cerebras.cloud.sdk import AsyncCerebras
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
CompletionRequest,
CompletionResponse,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
OpenAIEmbeddingsResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -187,16 +183,6 @@ class CerebrasInferenceAdapter(
**get_sampling_options(request.sampling_params),
}
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def openai_embeddings(
self,
model: str,

14
llama_stack/providers/remote/inference/databricks/databricks.py
View file

@ -11,15 +11,12 @@ from databricks.sdk import WorkspaceClient
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +24,6 @@ from llama_stack.apis.inference import (
OpenAICompletion,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -118,16 +114,6 @@ class DatabricksInferenceAdapter(
) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
raise NotImplementedError()
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def list_models(self) -> list[Model] | None:
self._model_cache = {} # from OpenAIMixin
ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async

31
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -10,22 +10,18 @@ from fireworks.client import Fireworks
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
CompletionResponse,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
ResponseFormat,
ResponseFormatType,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -48,8 +44,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
request_has_media,
)
@ -259,28 +253,3 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee
logger.debug(f"params to fireworks: {params}")
return params
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
kwargs = {}
if model.metadata.get("embedding_dimension"):
kwargs["dimensions"] = model.metadata.get("embedding_dimension")
assert all(not content_has_media(content) for content in contents), (
"Fireworks does not support media for embeddings"
)
response = self._get_client().embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
**kwargs,
)
embeddings = [data.embedding for data in response.data]
return EmbeddingsResponse(embeddings=embeddings)

59
llama_stack/providers/remote/inference/nvidia/nvidia.py
View file

@ -11,8 +11,6 @@ from openai import NOT_GIVEN, APIConnectionError
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
TextContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
@ -21,8 +19,6 @@ from llama_stack.apis.inference import (
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -31,7 +27,6 @@ from llama_stack.apis.inference import (
OpenAIEmbeddingUsage,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
)
@ -156,60 +151,6 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
# we pass n=1 to get only one completion
return convert_openai_completion_choice(response.choices[0])
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
if any(content_has_media(content) for content in contents):
raise NotImplementedError("Media is not supported")
#
# Llama Stack: contents = list[str] | list[InterleavedContentItem]
# ->
# OpenAI: input = str | list[str]
#
# we can ignore str and always pass list[str] to OpenAI
#
flat_contents = [content.text if isinstance(content, TextContentItem) else content for content in contents]
input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents]
provider_model_id = await self._get_provider_model_id(model_id)
extra_body = {}
if text_truncation is not None:
text_truncation_options = {
TextTruncation.none: "NONE",
TextTruncation.end: "END",
TextTruncation.start: "START",
}
extra_body["truncate"] = text_truncation_options[text_truncation]
if output_dimension is not None:
extra_body["dimensions"] = output_dimension
if task_type is not None:
task_type_options = {
EmbeddingTaskType.document: "passage",
EmbeddingTaskType.query: "query",
}
extra_body["input_type"] = task_type_options[task_type]
response = await self.client.embeddings.create(
model=provider_model_id,
input=input,
extra_body=extra_body,
)
#
# OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...)
# ->
# Llama Stack: EmbeddingsResponse(embeddings=list[list[float]])
#
return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
async def openai_embeddings(
self,
model: str,

27
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -14,7 +14,6 @@ from ollama import AsyncClient as AsyncOllamaClient
from llama_stack.apis.common.content_types import (
ImageContentItem,
InterleavedContent,
InterleavedContentItem,
TextContentItem,
)
from llama_stack.apis.common.errors import UnsupportedModelError
@ -25,8 +24,6 @@ from llama_stack.apis.inference import (
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
GrammarResponseFormat,
InferenceProvider,
JsonSchemaResponseFormat,
@ -34,7 +31,6 @@ from llama_stack.apis.inference import (
Message,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -66,9 +62,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
content_has_media,
convert_image_content_to_url,
interleaved_content_as_str,
request_has_media,
)
@ -363,27 +357,6 @@ class OllamaInferenceAdapter(
async for chunk in process_chat_completion_stream_response(stream, request):
yield chunk
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self._get_model(model_id)
assert all(not content_has_media(content) for content in contents), (
"Ollama does not support media for embeddings"
)
response = await self.ollama_client.embed(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
)
embeddings = response["embeddings"]
return EmbeddingsResponse(embeddings=embeddings)
async def register_model(self, model: Model) -> Model:
if await self.check_model_availability(model.provider_model_id):
return model

22
llama_stack/providers/remote/inference/passthrough/passthrough.py
View file

@ -14,8 +14,6 @@ from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionMessage,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +25,6 @@ from llama_stack.apis.inference import (
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -190,25 +187,6 @@ class PassthroughInferenceAdapter(Inference):
chunk = convert_to_pydantic(ChatCompletionResponseStreamChunk, chunk)
yield chunk
async def embeddings(
self,
model_id: str,
contents: list[InterleavedContent],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
client = self._get_client()
model = await self.model_store.get_model(model_id)
return await client.inference.embeddings(
model_id=model.provider_resource_id,
contents=contents,
text_truncation=text_truncation,
output_dimension=output_dimension,
task_type=task_type,
)
async def openai_embeddings(
self,
model: str,

10
llama_stack/providers/remote/inference/runpod/runpod.py
View file

@ -136,16 +136,6 @@ class RunpodInferenceAdapter(
**get_sampling_options(request.sampling_params),
}
async def embeddings(
self,
model: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def openai_embeddings(
self,
model: str,

14
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -12,14 +12,11 @@ from pydantic import SecretStr
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +24,6 @@ from llama_stack.apis.inference import (
ResponseFormat,
ResponseFormatType,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -306,16 +302,6 @@ class _HfAdapter(
**self._build_options(request.sampling_params, request.response_format),
)
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def openai_embeddings(
self,
model: str,

26
llama_stack/providers/remote/inference/together/together.py
View file

@ -12,14 +12,11 @@ from together.constants import BASE_URL
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +24,6 @@ from llama_stack.apis.inference import (
ResponseFormat,
ResponseFormatType,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -50,8 +46,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
request_has_media,
)
@ -247,26 +241,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need
logger.debug(f"params to together: {params}")
return params
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
assert all(not content_has_media(content) for content in contents), (
"Together does not support media for embeddings"
)
client = self._get_client()
r = await client.embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
)
embeddings = [item.embedding for item in r.data]
return EmbeddingsResponse(embeddings=embeddings)
async def list_models(self) -> list[Model] | None:
self._model_cache = {}
# Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client

30
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -16,7 +16,6 @@ from openai.types.chat.chat_completion_chunk import (
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
TextDelta,
ToolCallDelta,
ToolCallParseStatus,
@ -31,8 +30,6 @@ from llama_stack.apis.inference import (
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
GrammarResponseFormat,
Inference,
JsonSchemaResponseFormat,
@ -41,7 +38,6 @@ from llama_stack.apis.inference import (
ModelStore,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -74,8 +70,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
request_has_media,
)
@ -550,27 +544,3 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
"stream": request.stream,
**options,
}
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self._get_model(model_id)
kwargs = {}
assert model.model_type == ModelType.embedding
assert model.metadata.get("embedding_dimension")
kwargs["dimensions"] = model.metadata.get("embedding_dimension")
assert all(not content_has_media(content) for content in contents), "VLLM does not support media for embeddings"
response = await self.client.embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
**kwargs,
)
embeddings = [data.embedding for data in response.data]
return EmbeddingsResponse(embeddings=embeddings)

15
llama_stack/providers/remote/inference/watsonx/watsonx.py
View file

@ -11,13 +11,11 @@ from ibm_watsonx_ai.foundation_models import Model
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
from openai import AsyncOpenAI
from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem
from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
EmbeddingsResponse,
EmbeddingTaskType,
GreedySamplingStrategy,
Inference,
LogProbConfig,
@ -30,7 +28,6 @@ from llama_stack.apis.inference import (
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -265,16 +262,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
}
return params
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError("embedding is not supported for watsonx")
async def openai_embeddings(
self,
model: str,

22
llama_stack/providers/utils/inference/embedding_mixin.py
View file

@ -15,16 +15,11 @@ if TYPE_CHECKING:
from sentence_transformers import SentenceTransformer
from llama_stack.apis.inference import (
EmbeddingsResponse,
EmbeddingTaskType,
InterleavedContentItem,
ModelStore,
OpenAIEmbeddingData,
OpenAIEmbeddingsResponse,
OpenAIEmbeddingUsage,
TextTruncation,
)
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
EMBEDDING_MODELS = {}
@ -35,23 +30,6 @@ log = get_logger(name=__name__, category="providers::utils")
class SentenceTransformerEmbeddingMixin:
model_store: ModelStore
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
embedding_model = await self._load_sentence_transformer_model(model.provider_resource_id)
embeddings = await asyncio.to_thread(
embedding_model.encode,
[interleaved_content_as_str(content) for content in contents],
show_progress_bar=False,
)
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,

25
llama_stack/providers/utils/inference/litellm_openai_mixin.py
View file

@ -11,14 +11,11 @@ import litellm
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
InferenceProvider,
JsonSchemaResponseFormat,
LogProbConfig,
@ -32,7 +29,6 @@ from llama_stack.apis.inference import (
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -50,9 +46,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
prepare_openai_completion_params,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
interleaved_content_as_str,
)
logger = get_logger(name=__name__, category="providers::utils")
@ -269,24 +262,6 @@ class LiteLLMOpenAIMixin(
)
return api_key
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
response = litellm.embedding(
model=self.get_litellm_model_name(model.provider_resource_id),
input=[interleaved_content_as_str(content) for content in contents],
)
embeddings = [data["embedding"] for data in response["data"]]
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,

110
llama_stack/providers/utils/responses/responses_store.py
View file

@ -3,6 +3,9 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import asyncio
from typing import Any
from llama_stack.apis.agents import (
Order,
)
@ -14,24 +17,51 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseObject,
OpenAIResponseObjectWithInput,
)
from llama_stack.core.datatypes import AccessRule
from llama_stack.core.datatypes import AccessRule, ResponsesStoreConfig
from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
from llama_stack.log import get_logger
from ..sqlstore.api import ColumnDefinition, ColumnType
from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore
from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl
from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, SqlStoreType, sqlstore_impl
logger = get_logger(name=__name__, category="responses_store")
class ResponsesStore:
def __init__(self, sql_store_config: SqlStoreConfig, policy: list[AccessRule]):
if not sql_store_config:
sql_store_config = SqliteSqlStoreConfig(
def __init__(
self,
config: ResponsesStoreConfig | SqlStoreConfig,
policy: list[AccessRule],
):
# Handle backward compatibility
if not isinstance(config, ResponsesStoreConfig):
# Legacy: SqlStoreConfig passed directly as config
config = ResponsesStoreConfig(
sql_store_config=config,
)
self.config = config
self.sql_store_config = config.sql_store_config
if not self.sql_store_config:
self.sql_store_config = SqliteSqlStoreConfig(
db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
)
self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config), policy)
self.sql_store = None
self.policy = policy
# Disable write queue for SQLite to avoid concurrency issues
self.enable_write_queue = self.sql_store_config.type != SqlStoreType.sqlite
# Async write queue and worker control
self._queue: asyncio.Queue[tuple[OpenAIResponseObject, list[OpenAIResponseInput]]] | None = None
self._worker_tasks: list[asyncio.Task[Any]] = []
self._max_write_queue_size: int = config.max_write_queue_size
self._num_writers: int = max(1, config.num_writers)
async def initialize(self):
"""Create the necessary tables if they don't exist."""
self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config), self.policy)
await self.sql_store.create_table(
"openai_responses",
{
@ -42,9 +72,68 @@ class ResponsesStore:
},
)
if self.enable_write_queue:
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
for _ in range(self._num_writers):
self._worker_tasks.append(asyncio.create_task(self._worker_loop()))
else:
logger.info("Write queue disabled for SQLite to avoid concurrency issues")
async def shutdown(self) -> None:
if not self._worker_tasks:
return
if self._queue is not None:
await self._queue.join()
for t in self._worker_tasks:
if not t.done():
t.cancel()
for t in self._worker_tasks:
try:
await t
except asyncio.CancelledError:
pass
self._worker_tasks.clear()
async def flush(self) -> None:
"""Wait for all queued writes to complete. Useful for testing."""
if self.enable_write_queue and self._queue is not None:
await self._queue.join()
async def store_response_object(
self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput]
) -> None:
if self.enable_write_queue:
if self._queue is None:
raise ValueError("Responses store is not initialized")
try:
self._queue.put_nowait((response_object, input))
except asyncio.QueueFull:
logger.warning(f"Write queue full; adding response id={getattr(response_object, 'id', '<unknown>')}")
await self._queue.put((response_object, input))
else:
await self._write_response_object(response_object, input)
async def _worker_loop(self) -> None:
assert self._queue is not None
while True:
try:
item = await self._queue.get()
except asyncio.CancelledError:
break
response_object, input = item
try:
await self._write_response_object(response_object, input)
except Exception as e: # noqa: BLE001
logger.error(f"Error writing response object: {e}")
finally:
self._queue.task_done()
async def _write_response_object(
self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput]
) -> None:
if self.sql_store is None:
raise ValueError("Responses store is not initialized")
data = response_object.model_dump()
data["input"] = [input_item.model_dump() for input_item in input]
@ -73,6 +162,9 @@ class ResponsesStore:
:param model: The model to filter by.
:param order: The order to sort the responses by.
"""
if not self.sql_store:
raise ValueError("Responses store is not initialized")
if not order:
order = Order.desc
@ -100,6 +192,9 @@ class ResponsesStore:
"""
Get a response object with automatic access control checking.
"""
if not self.sql_store:
raise ValueError("Responses store is not initialized")
row = await self.sql_store.fetch_one(
"openai_responses",
where={"id": response_id},
@ -113,6 +208,9 @@ class ResponsesStore:
return OpenAIResponseObjectWithInput(**row["response_object"])
async def delete_response_object(self, response_id: str) -> OpenAIDeleteResponseObject:
if not self.sql_store:
raise ValueError("Responses store is not initialized")
row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id})
if not row:
raise ValueError(f"Response with id {response_id} not found")