mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 09:53:45 +00:00
minor fix
This commit is contained in:
Omar Abdelwahab
parent
1143db0f64
commit
376f0fcd23
2 changed files with 79 additions and 34 deletions
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@ -11,7 +11,7 @@ from typing import Any
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from llama_stack.apis.agents.openai_responses import (
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AllowedToolsFilter,
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ApprovalFilter,
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MCPAuthentication,
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MCPAuthorization,
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MCPListToolsTool,
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OpenAIResponseContentPartOutputText,
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OpenAIResponseContentPartReasoningText,
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@ -69,7 +69,9 @@ from llama_stack.apis.inference import (
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)
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from llama_stack.core.telemetry import tracing
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from llama_stack.log import get_logger
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from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
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from llama_stack.providers.utils.inference.prompt_adapter import (
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interleaved_content_as_str,
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)
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from .types import ChatCompletionContext, ChatCompletionResult
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from .utils import (
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@ -81,14 +83,14 @@ from .utils import (
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logger = get_logger(name=__name__, category="agents::meta_reference")
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def _convert_authentication_to_headers(auth: MCPAuthentication) -> dict[str, str]:
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"""Convert MCPAuthentication config to HTTP headers.
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def _convert_authentication_to_headers(auth: MCPAuthorization) -> dict[str, str]:
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"""Convert MCPAuthorization config to HTTP headers.
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Args:
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auth: Authentication configuration
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auth: Authorization configuration
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Returns:
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Dictionary of HTTP headers for authentication
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Dictionary of HTTP headers for authorization
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"""
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headers = {}
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@ -120,7 +122,9 @@ def convert_tooldef_to_chat_tool(tool_def):
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"""
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from llama_stack.models.llama.datatypes import ToolDefinition
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from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_tooldef_to_openai_tool,
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)
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internal_tool_def = ToolDefinition(
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tool_name=tool_def.name,
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@ -298,7 +302,9 @@ class StreamingResponseOrchestrator:
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# add any approval requests required
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for tool_call in approvals:
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async for evt in self._add_mcp_approval_request(
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tool_call.function.name, tool_call.function.arguments, output_messages
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tool_call.function.name,
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tool_call.function.arguments,
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output_messages,
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):
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yield evt
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@ -407,7 +413,12 @@ class StreamingResponseOrchestrator:
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else:
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non_function_tool_calls.append(tool_call)
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return function_tool_calls, non_function_tool_calls, approvals, next_turn_messages
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return (
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function_tool_calls,
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non_function_tool_calls,
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approvals,
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next_turn_messages,
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)
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def _accumulate_chunk_usage(self, chunk: OpenAIChatCompletionChunk) -> None:
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"""Accumulate usage from a streaming chunk into the response usage format."""
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@ -718,12 +729,15 @@ class StreamingResponseOrchestrator:
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# Emit output_item.added event for the new function call
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self.sequence_number += 1
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is_mcp_tool = tool_call.function.name and tool_call.function.name in self.mcp_tool_to_server
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if not is_mcp_tool and tool_call.function.name not in ["web_search", "knowledge_search"]:
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if not is_mcp_tool and tool_call.function.name not in [
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"web_search",
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"knowledge_search",
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]:
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# for MCP tools (and even other non-function tools) we emit an output message item later
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function_call_item = OpenAIResponseOutputMessageFunctionToolCall(
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arguments="", # Will be filled incrementally via delta events
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call_id=tool_call.id or "",
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name=tool_call.function.name if tool_call.function else "",
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name=(tool_call.function.name if tool_call.function else ""),
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id=tool_call_item_id,
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status="in_progress",
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)
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@ -1035,14 +1049,18 @@ class StreamingResponseOrchestrator:
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)
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async def _process_new_tools(
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self, tools: list[OpenAIResponseInputTool], output_messages: list[OpenAIResponseOutput]
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self,
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tools: list[OpenAIResponseInputTool],
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output_messages: list[OpenAIResponseOutput],
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) -> AsyncIterator[OpenAIResponseObjectStream]:
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"""Process all tools and emit appropriate streaming events."""
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from openai.types.chat import ChatCompletionToolParam
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from llama_stack.apis.tools import ToolDef
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from llama_stack.models.llama.datatypes import ToolDefinition
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from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_tooldef_to_openai_tool,
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)
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def make_openai_tool(tool_name: str, tool: ToolDef) -> ChatCompletionToolParam:
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tool_def = ToolDefinition(
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@ -1079,7 +1097,9 @@ class StreamingResponseOrchestrator:
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raise ValueError(f"Llama Stack OpenAI Responses does not yet support tool type: {input_tool.type}")
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async def _process_mcp_tool(
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self, mcp_tool: OpenAIResponseInputToolMCP, output_messages: list[OpenAIResponseOutput]
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self,
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mcp_tool: OpenAIResponseInputToolMCP,
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output_messages: list[OpenAIResponseOutput],
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) -> AsyncIterator[OpenAIResponseObjectStream]:
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"""Process an MCP tool configuration and emit appropriate streaming events."""
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from llama_stack.providers.utils.tools.mcp import list_mcp_tools
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@ -1108,10 +1128,10 @@ class StreamingResponseOrchestrator:
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"server_url": mcp_tool.server_url,
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"mcp_list_tools_id": list_id,
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}
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# Prepare headers with authentication from tool config
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# Prepare headers with authorization from tool config
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headers = dict(mcp_tool.headers or {})
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if mcp_tool.authentication:
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auth_headers = _convert_authentication_to_headers(mcp_tool.authentication)
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if mcp_tool.authorization:
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auth_headers = _convert_authentication_to_headers(mcp_tool.authorization)
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# Don't override existing headers (case-insensitive check)
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existing_keys_lower = {k.lower() for k in headers.keys()}
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for key, value in auth_headers.items():
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@ -1200,7 +1220,10 @@ class StreamingResponseOrchestrator:
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return True
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async def _add_mcp_approval_request(
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self, tool_name: str, arguments: str, output_messages: list[OpenAIResponseOutput]
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self,
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tool_name: str,
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arguments: str,
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output_messages: list[OpenAIResponseOutput],
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) -> AsyncIterator[OpenAIResponseObjectStream]:
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mcp_server = self.mcp_tool_to_server[tool_name]
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mcp_approval_request = OpenAIResponseMCPApprovalRequest(
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@ -1227,7 +1250,9 @@ class StreamingResponseOrchestrator:
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)
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async def _add_mcp_list_tools(
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self, mcp_list_message: OpenAIResponseOutputMessageMCPListTools, output_messages: list[OpenAIResponseOutput]
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self,
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mcp_list_message: OpenAIResponseOutputMessageMCPListTools,
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output_messages: list[OpenAIResponseOutput],
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) -> AsyncIterator[OpenAIResponseObjectStream]:
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# Add the MCP list message to output
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output_messages.append(mcp_list_message)
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@ -1260,11 +1285,15 @@ class StreamingResponseOrchestrator:
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)
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async def _reuse_mcp_list_tools(
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self, original: OpenAIResponseOutputMessageMCPListTools, output_messages: list[OpenAIResponseOutput]
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self,
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original: OpenAIResponseOutputMessageMCPListTools,
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output_messages: list[OpenAIResponseOutput],
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) -> AsyncIterator[OpenAIResponseObjectStream]:
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for t in original.tools:
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from llama_stack.models.llama.datatypes import ToolDefinition
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from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_tooldef_to_openai_tool,
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)
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# convert from input_schema to map of ToolParamDefinitions...
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tool_def = ToolDefinition(
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@ -10,7 +10,7 @@ from collections.abc import AsyncIterator
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from typing import Any
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from llama_stack.apis.agents.openai_responses import (
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MCPAuthentication,
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MCPAuthorization,
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OpenAIResponseInputToolFileSearch,
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OpenAIResponseInputToolMCP,
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OpenAIResponseObjectStreamResponseFileSearchCallCompleted,
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@ -27,10 +27,7 @@ from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseOutputMessageMCPCall,
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OpenAIResponseOutputMessageWebSearchToolCall,
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)
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from llama_stack.apis.common.content_types import (
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ImageContentItem,
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TextContentItem,
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)
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from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem
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from llama_stack.apis.inference import (
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OpenAIChatCompletionContentPartImageParam,
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OpenAIChatCompletionContentPartTextParam,
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@ -48,8 +45,8 @@ from .types import ChatCompletionContext, ToolExecutionResult
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logger = get_logger(name=__name__, category="agents::meta_reference")
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def _convert_authentication_to_headers(auth: MCPAuthentication) -> dict[str, str]:
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"""Convert MCPAuthentication config to HTTP headers.
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def _convert_authentication_to_headers(auth: MCPAuthorization) -> dict[str, str]:
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"""Convert MCPAuthorization config to HTTP headers.
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Args:
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auth: Authentication configuration
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@ -106,7 +103,12 @@ class ToolExecutor:
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# Emit progress events for tool execution start
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async for event_result in self._emit_progress_events(
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function.name, ctx, sequence_number, output_index, item_id, mcp_tool_to_server
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function.name,
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ctx,
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sequence_number,
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output_index,
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item_id,
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mcp_tool_to_server,
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):
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sequence_number = event_result.sequence_number
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yield event_result
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@ -126,14 +128,28 @@ class ToolExecutor:
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)
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)
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async for event_result in self._emit_completion_events(
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function.name, ctx, sequence_number, output_index, item_id, has_error, mcp_tool_to_server
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function.name,
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ctx,
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sequence_number,
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output_index,
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item_id,
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has_error,
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mcp_tool_to_server,
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):
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sequence_number = event_result.sequence_number
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yield event_result
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# Build result messages from tool execution
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output_message, input_message = await self._build_result_messages(
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function, tool_call_id, item_id, tool_kwargs, ctx, error_exc, result, has_error, mcp_tool_to_server
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function,
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tool_call_id,
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item_id,
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tool_kwargs,
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ctx,
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error_exc,
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result,
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has_error,
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mcp_tool_to_server,
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)
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# Yield the final result
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@ -328,10 +344,10 @@ class ToolExecutor:
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"server_url": mcp_tool.server_url,
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"tool_name": function_name,
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}
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# Prepare headers with authentication from tool config
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# Prepare headers with authorization from tool config
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headers = dict(mcp_tool.headers or {})
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if mcp_tool.authentication:
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auth_headers = _convert_authentication_to_headers(mcp_tool.authentication)
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if mcp_tool.authorization:
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auth_headers = _convert_authentication_to_headers(mcp_tool.authorization)
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# Don't override existing headers (case-insensitive check)
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existing_keys_lower = {k.lower() for k in headers.keys()}
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for key, value in auth_headers.items():
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