Merge branch 'main' into routeur

src/llama_stack_api/agents.py

@@ -72,6 +72,7 @@ class Agents(Protocol):
         model: str,
         prompt: OpenAIResponsePrompt | None = None,
         instructions: str | None = None,
+        parallel_tool_calls: bool | None = True,
         previous_response_id: str | None = None,
         conversation: str | None = None,
         store: bool | None = True,

src/llama_stack_api/internal/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Internal subpackage for shared interfaces that are not part of the public API.
+
+__all__: list[str] = []

src/llama_stack_api/internal/kvstore.py

@@ -0,0 +1,26 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from datetime import datetime
+from typing import Protocol
+
+
+class KVStore(Protocol):
+    """Protocol for simple key/value storage backends."""
+
+    # TODO: make the value type bytes instead of str
+    async def set(self, key: str, value: str, expiration: datetime | None = None) -> None: ...
+
+    async def get(self, key: str) -> str | None: ...
+
+    async def delete(self, key: str) -> None: ...
+
+    async def values_in_range(self, start_key: str, end_key: str) -> list[str]: ...
+
+    async def keys_in_range(self, start_key: str, end_key: str) -> list[str]: ...
+
+
+__all__ = ["KVStore"]

src/llama_stack_api/internal/sqlstore.py

@@ -0,0 +1,79 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from collections.abc import Mapping, Sequence
+from enum import Enum
+from typing import Any, Literal, Protocol
+
+from pydantic import BaseModel
+
+from llama_stack_api import PaginatedResponse
+
+
+class ColumnType(Enum):
+    INTEGER = "INTEGER"
+    STRING = "STRING"
+    TEXT = "TEXT"
+    FLOAT = "FLOAT"
+    BOOLEAN = "BOOLEAN"
+    JSON = "JSON"
+    DATETIME = "DATETIME"
+
+
+class ColumnDefinition(BaseModel):
+    type: ColumnType
+    primary_key: bool = False
+    nullable: bool = True
+    default: Any = None
+
+
+class SqlStore(Protocol):
+    """Protocol for common SQL-store functionality."""
+
+    async def create_table(self, table: str, schema: Mapping[str, ColumnType | ColumnDefinition]) -> None: ...
+
+    async def insert(self, table: str, data: Mapping[str, Any] | Sequence[Mapping[str, Any]]) -> None: ...
+
+    async def upsert(
+        self,
+        table: str,
+        data: Mapping[str, Any],
+        conflict_columns: list[str],
+        update_columns: list[str] | None = None,
+    ) -> None: ...
+
+    async def fetch_all(
+        self,
+        table: str,
+        where: Mapping[str, Any] | None = None,
+        where_sql: str | None = None,
+        limit: int | None = None,
+        order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
+        cursor: tuple[str, str] | None = None,
+    ) -> PaginatedResponse: ...
+
+    async def fetch_one(
+        self,
+        table: str,
+        where: Mapping[str, Any] | None = None,
+        where_sql: str | None = None,
+        order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
+    ) -> dict[str, Any] | None: ...
+
+    async def update(self, table: str, data: Mapping[str, Any], where: Mapping[str, Any]) -> None: ...
+
+    async def delete(self, table: str, where: Mapping[str, Any]) -> None: ...
+
+    async def add_column_if_not_exists(
+        self,
+        table: str,
+        column_name: str,
+        column_type: ColumnType,
+        nullable: bool = True,
+    ) -> None: ...
+
+
+__all__ = ["ColumnDefinition", "ColumnType", "SqlStore"]

src/llama_stack_api/openai_responses.py

@@ -585,7 +585,7 @@ class OpenAIResponseObject(BaseModel):
     :param model: Model identifier used for generation
     :param object: Object type identifier, always "response"
     :param output: List of generated output items (messages, tool calls, etc.)
-    :param parallel_tool_calls: Whether tool calls can be executed in parallel
+    :param parallel_tool_calls: (Optional) Whether to allow more than one function tool call generated per turn.
     :param previous_response_id: (Optional) ID of the previous response in a conversation
     :param prompt: (Optional) Reference to a prompt template and its variables.
     :param status: Current status of the response generation
@@ -605,7 +605,7 @@ class OpenAIResponseObject(BaseModel):
     model: str
     object: Literal["response"] = "response"
     output: Sequence[OpenAIResponseOutput]
-    parallel_tool_calls: bool = False
+    parallel_tool_calls: bool | None = True
     previous_response_id: str | None = None
     prompt: OpenAIResponsePrompt | None = None
     status: str

src/llama_stack_api/vector_io.py

@@ -11,7 +11,7 @@
 from typing import Annotated, Any, Literal, Protocol, runtime_checkable
 
 from fastapi import Body, Query
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 
 from llama_stack_api.common.tracing import telemetry_traceable
 from llama_stack_api.inference import InterleavedContent
@@ -372,6 +372,65 @@ VectorStoreFileStatus = Literal["completed"] | Literal["in_progress"] | Literal[
 register_schema(VectorStoreFileStatus, name="VectorStoreFileStatus")
 
 
+# VectorStoreFileAttributes type with OpenAPI constraints
+VectorStoreFileAttributes = Annotated[
+    dict[str, Annotated[str, Field(max_length=512)] | float | bool],
+    Field(
+        max_length=16,
+        json_schema_extra={
+            "propertyNames": {"type": "string", "maxLength": 64},
+            "x-oaiTypeLabel": "map",
+        },
+        description=(
+            "Set of 16 key-value pairs that can be attached to an object. This can be "
+            "useful for storing additional information about the object in a structured "
+            "format, and querying for objects via API or the dashboard. Keys are strings "
+            "with a maximum length of 64 characters. Values are strings with a maximum "
+            "length of 512 characters, booleans, or numbers."
+        ),
+    ),
+]
+
+
+def _sanitize_vector_store_attributes(metadata: dict[str, Any] | None) -> dict[str, str | float | bool]:
+    """
+    Sanitize metadata to VectorStoreFileAttributes spec (max 16 properties, primitives only).
+
+    Converts dict[str, Any] to dict[str, str | float | bool]:
+    - Preserves: str (truncated to 512 chars), bool, int/float (as float)
+    - Converts: list -> comma-separated string
+    - Filters: dict, None, other types
+    - Enforces: max 16 properties, max 64 char keys, max 512 char string values
+    """
+    if not metadata:
+        return {}
+
+    sanitized: dict[str, str | float | bool] = {}
+    for key, value in metadata.items():
+        # Enforce max 16 properties
+        if len(sanitized) >= 16:
+            break
+
+        # Enforce max 64 char keys
+        if len(key) > 64:
+            continue
+
+        # Convert to supported primitive types
+        if isinstance(value, bool):
+            sanitized[key] = value
+        elif isinstance(value, int | float):
+            sanitized[key] = float(value)
+        elif isinstance(value, str):
+            # Enforce max 512 char string values
+            sanitized[key] = value[:512] if len(value) > 512 else value
+        elif isinstance(value, list):
+            # Convert lists to comma-separated strings (max 512 chars)
+            list_str = ", ".join(str(item) for item in value)
+            sanitized[key] = list_str[:512] if len(list_str) > 512 else list_str
+
+    return sanitized
+
+
 @json_schema_type
 class VectorStoreFileObject(BaseModel):
     """OpenAI Vector Store File object.
@@ -389,7 +448,7 @@ class VectorStoreFileObject(BaseModel):
 
     id: str
     object: str = "vector_store.file"
-    attributes: dict[str, Any] = Field(default_factory=dict)
+    attributes: VectorStoreFileAttributes = Field(default_factory=dict)
     chunking_strategy: VectorStoreChunkingStrategy
     created_at: int
     last_error: VectorStoreFileLastError | None = None
@@ -397,6 +456,12 @@ class VectorStoreFileObject(BaseModel):
     usage_bytes: int = 0
     vector_store_id: str
 
+    @field_validator("attributes", mode="before")
+    @classmethod
+    def _validate_attributes(cls, v: dict[str, Any] | None) -> dict[str, str | float | bool]:
+        """Sanitize attributes to match VectorStoreFileAttributes OpenAPI spec."""
+        return _sanitize_vector_store_attributes(v)
+
 
 @json_schema_type
 class VectorStoreListFilesResponse(BaseModel):