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https://github.com/meta-llama/llama-stack.git
synced 2025-12-11 19:56:03 +00:00
fix(mypy): resolve tool_executor type issues (45 errors fixed)
- Add proper type annotations using Any where needed - Fix union-attr errors with getattr and walrus operator - Fix arg-type errors for datetime/enum conversions - Add type: ignore for list invariance issues - Remove event variable reuse to satisfy type checker - Use proper type narrowing for tool execution paths Patterns established: - Use getattr() with walrus operator for optional attributes - Use type: ignore for runtime-correct but mypy-incompatible cases - Separate event variables by type to avoid union conflicts
This commit is contained in:
Ashwin Bharambe
parent
f88416ef87
commit
3a437d80af
1 changed files with 81 additions and 57 deletions
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@ -7,6 +7,7 @@
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import asyncio
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import json
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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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OpenAIResponseInputToolFileSearch,
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@ -22,10 +23,12 @@ from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseObjectStreamResponseWebSearchCallSearching,
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OpenAIResponseOutputMessageFileSearchToolCall,
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OpenAIResponseOutputMessageFileSearchToolCallResults,
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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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InterleavedContent,
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TextContentItem,
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)
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from llama_stack.apis.inference import (
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@ -67,7 +70,7 @@ class ToolExecutor:
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) -> AsyncIterator[ToolExecutionResult]:
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tool_call_id = tool_call.id
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function = tool_call.function
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tool_kwargs = json.loads(function.arguments) if function.arguments else {}
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tool_kwargs = json.loads(function.arguments) if function and function.arguments else {}
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if not function or not tool_call_id or not function.name:
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yield ToolExecutionResult(sequence_number=sequence_number)
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@ -84,7 +87,16 @@ class ToolExecutor:
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error_exc, result = await self._execute_tool(function.name, tool_kwargs, ctx, mcp_tool_to_server)
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# Emit completion events for tool execution
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has_error = error_exc or (result and ((result.error_code and result.error_code > 0) or result.error_message))
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has_error = bool(
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error_exc
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or (
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result
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and (
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((error_code := getattr(result, "error_code", None)) and error_code > 0)
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or getattr(result, "error_message", None)
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)
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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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):
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@ -101,7 +113,11 @@ class ToolExecutor:
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sequence_number=sequence_number,
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final_output_message=output_message,
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final_input_message=input_message,
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citation_files=result.metadata.get("citation_files") if result and result.metadata else None,
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citation_files=(
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metadata.get("citation_files")
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if result and (metadata := getattr(result, "metadata", None))
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else None
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),
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)
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async def _execute_knowledge_search_via_vector_store(
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@ -188,8 +204,9 @@ class ToolExecutor:
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citation_files[file_id] = filename
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# Cast to proper InterleavedContent type (list invariance)
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return ToolInvocationResult(
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content=content_items,
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content=content_items, # type: ignore[arg-type]
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metadata={
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"document_ids": [r.file_id for r in search_results],
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"chunks": [r.content[0].text if r.content else "" for r in search_results],
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@ -209,51 +226,50 @@ class ToolExecutor:
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) -> AsyncIterator[ToolExecutionResult]:
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"""Emit progress events for tool execution start."""
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# Emit in_progress event based on tool type (only for tools with specific streaming events)
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progress_event = None
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if mcp_tool_to_server and function_name in mcp_tool_to_server:
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sequence_number += 1
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progress_event = OpenAIResponseObjectStreamResponseMcpCallInProgress(
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mcp_progress_event = OpenAIResponseObjectStreamResponseMcpCallInProgress(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=mcp_progress_event, sequence_number=sequence_number)
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elif function_name == "web_search":
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sequence_number += 1
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progress_event = OpenAIResponseObjectStreamResponseWebSearchCallInProgress(
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web_progress_event = OpenAIResponseObjectStreamResponseWebSearchCallInProgress(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=web_progress_event, sequence_number=sequence_number)
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elif function_name == "knowledge_search":
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sequence_number += 1
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progress_event = OpenAIResponseObjectStreamResponseFileSearchCallInProgress(
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file_progress_event = OpenAIResponseObjectStreamResponseFileSearchCallInProgress(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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if progress_event:
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yield ToolExecutionResult(stream_event=progress_event, sequence_number=sequence_number)
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yield ToolExecutionResult(stream_event=file_progress_event, sequence_number=sequence_number)
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# For web search, emit searching event
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if function_name == "web_search":
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sequence_number += 1
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searching_event = OpenAIResponseObjectStreamResponseWebSearchCallSearching(
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web_searching_event = OpenAIResponseObjectStreamResponseWebSearchCallSearching(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number)
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yield ToolExecutionResult(stream_event=web_searching_event, sequence_number=sequence_number)
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# For file search, emit searching event
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if function_name == "knowledge_search":
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sequence_number += 1
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searching_event = OpenAIResponseObjectStreamResponseFileSearchCallSearching(
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file_searching_event = OpenAIResponseObjectStreamResponseFileSearchCallSearching(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number)
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yield ToolExecutionResult(stream_event=file_searching_event, sequence_number=sequence_number)
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async def _execute_tool(
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self,
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@ -261,7 +277,7 @@ class ToolExecutor:
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tool_kwargs: dict,
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ctx: ChatCompletionContext,
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mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None,
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) -> tuple[Exception | None, any]:
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) -> tuple[Exception | None, Any]:
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"""Execute the tool and return error exception and result."""
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error_exc = None
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result = None
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@ -284,10 +300,14 @@ class ToolExecutor:
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kwargs=tool_kwargs,
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)
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elif function_name == "knowledge_search":
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response_file_search_tool = next(
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response_file_search_tool = (
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next(
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(t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)),
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None,
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)
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if ctx.response_tools
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else None
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)
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if response_file_search_tool:
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# Use vector_stores.search API instead of knowledge_search tool
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# to support filters and ranking_options
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@ -322,35 +342,34 @@ class ToolExecutor:
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mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None,
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) -> AsyncIterator[ToolExecutionResult]:
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"""Emit completion or failure events for tool execution."""
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completion_event = None
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if mcp_tool_to_server and function_name in mcp_tool_to_server:
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sequence_number += 1
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if has_error:
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completion_event = OpenAIResponseObjectStreamResponseMcpCallFailed(
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mcp_failed_event = OpenAIResponseObjectStreamResponseMcpCallFailed(
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=mcp_failed_event, sequence_number=sequence_number)
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else:
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completion_event = OpenAIResponseObjectStreamResponseMcpCallCompleted(
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mcp_completed_event = OpenAIResponseObjectStreamResponseMcpCallCompleted(
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=mcp_completed_event, sequence_number=sequence_number)
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elif function_name == "web_search":
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sequence_number += 1
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completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted(
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web_completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=web_completion_event, sequence_number=sequence_number)
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elif function_name == "knowledge_search":
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sequence_number += 1
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completion_event = OpenAIResponseObjectStreamResponseFileSearchCallCompleted(
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file_completion_event = OpenAIResponseObjectStreamResponseFileSearchCallCompleted(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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if completion_event:
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yield ToolExecutionResult(stream_event=completion_event, sequence_number=sequence_number)
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yield ToolExecutionResult(stream_event=file_completion_event, sequence_number=sequence_number)
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async def _build_result_messages(
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self,
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@ -360,21 +379,18 @@ class ToolExecutor:
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tool_kwargs: dict,
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ctx: ChatCompletionContext,
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error_exc: Exception | None,
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result: any,
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result: Any,
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has_error: bool,
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mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None,
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) -> tuple[any, any]:
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) -> tuple[Any, Any]:
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"""Build output and input messages from tool execution results."""
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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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# Build output message
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message: Any
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if mcp_tool_to_server and function.name in mcp_tool_to_server:
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from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseOutputMessageMCPCall,
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)
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message = OpenAIResponseOutputMessageMCPCall(
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id=item_id,
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arguments=function.arguments,
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@ -383,10 +399,14 @@ class ToolExecutor:
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)
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if error_exc:
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message.error = str(error_exc)
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elif (result and result.error_code and result.error_code > 0) or (result and result.error_message):
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message.error = f"Error (code {result.error_code}): {result.error_message}"
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elif result and result.content:
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message.output = interleaved_content_as_str(result.content)
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elif (
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result and (error_code := getattr(result, "error_code", None)) and error_code > 0
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) or (result and (error_message := getattr(result, "error_message", None))):
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ec = getattr(result, "error_code", "unknown")
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em = getattr(result, "error_message", "")
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message.error = f"Error (code {ec}): {em}"
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elif result and (content := getattr(result, "content", None)):
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message.output = interleaved_content_as_str(content)
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else:
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if function.name == "web_search":
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message = OpenAIResponseOutputMessageWebSearchToolCall(
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@ -401,17 +421,17 @@ class ToolExecutor:
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queries=[tool_kwargs.get("query", "")],
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status="completed",
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)
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if result and "document_ids" in result.metadata:
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if result and (metadata := getattr(result, "metadata", None)) and "document_ids" in metadata:
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message.results = []
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for i, doc_id in enumerate(result.metadata["document_ids"]):
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text = result.metadata["chunks"][i] if "chunks" in result.metadata else None
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score = result.metadata["scores"][i] if "scores" in result.metadata else None
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for i, doc_id in enumerate(metadata["document_ids"]):
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text = metadata["chunks"][i] if "chunks" in metadata else None
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score = metadata["scores"][i] if "scores" in metadata else None
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message.results.append(
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OpenAIResponseOutputMessageFileSearchToolCallResults(
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file_id=doc_id,
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filename=doc_id,
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text=text,
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score=score,
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text=text if text is not None else "",
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score=score if score is not None else 0.0,
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attributes={},
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)
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)
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@ -421,27 +441,31 @@ class ToolExecutor:
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raise ValueError(f"Unknown tool {function.name} called")
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# Build input message
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input_message = None
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if result and result.content:
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if isinstance(result.content, str):
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content = result.content
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elif isinstance(result.content, list):
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content = []
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for item in result.content:
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input_message: OpenAIToolMessageParam | None = None
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if result and (result_content := getattr(result, "content", None)):
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if isinstance(result_content, str):
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msg_content: str | list[Any] = result_content
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elif isinstance(result_content, list):
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content_list: list[Any] = []
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for item in result_content:
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part: Any
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if isinstance(item, TextContentItem):
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part = OpenAIChatCompletionContentPartTextParam(text=item.text)
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elif isinstance(item, ImageContentItem):
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if item.image.data:
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url = f"data:image;base64,{item.image.data}"
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url_value = f"data:image;base64,{item.image.data}"
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else:
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url = item.image.url
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part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url))
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url_value = str(item.image.url) if item.image.url else ""
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part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url_value))
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else:
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raise ValueError(f"Unknown result content type: {type(item)}")
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content.append(part)
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content_list.append(part)
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msg_content = content_list
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else:
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raise ValueError(f"Unknown result content type: {type(result.content)}")
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input_message = OpenAIToolMessageParam(content=content, tool_call_id=tool_call_id)
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raise ValueError(f"Unknown result content type: {type(result_content)}")
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# OpenAIToolMessageParam accepts str | list[TextParam] but we may have images
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# This is runtime-safe as the API accepts it, but mypy complains
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input_message = OpenAIToolMessageParam(content=msg_content, tool_call_id=tool_call_id) # type: ignore[arg-type]
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else:
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text = str(error_exc) if error_exc else "Tool execution failed"
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input_message = OpenAIToolMessageParam(content=text, tool_call_id=tool_call_id)
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