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3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
189 lines
6.8 KiB
Python
189 lines
6.8 KiB
Python
"""Tests for LangChain-to-OpenAI message format converters."""
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from __future__ import annotations
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import json
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from unittest.mock import MagicMock
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from deerflow.runtime.converters import (
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langchain_messages_to_openai,
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langchain_to_openai_completion,
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langchain_to_openai_message,
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)
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def _make_ai_message(content="", tool_calls=None, id="msg-123", usage_metadata=None, response_metadata=None):
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msg = MagicMock()
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msg.type = "ai"
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msg.content = content
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msg.tool_calls = tool_calls or []
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msg.id = id
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msg.usage_metadata = usage_metadata
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msg.response_metadata = response_metadata or {}
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return msg
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def _make_human_message(content="Hello"):
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msg = MagicMock()
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msg.type = "human"
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msg.content = content
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return msg
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def _make_system_message(content="You are an assistant."):
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msg = MagicMock()
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msg.type = "system"
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msg.content = content
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return msg
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def _make_tool_message(content="result", tool_call_id="call-abc"):
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msg = MagicMock()
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msg.type = "tool"
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msg.content = content
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msg.tool_call_id = tool_call_id
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return msg
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class TestLangchainToOpenaiMessage:
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def test_ai_message_text_only(self):
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msg = _make_ai_message(content="Hello world")
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result = langchain_to_openai_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] == "Hello world"
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assert "tool_calls" not in result
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def test_ai_message_with_tool_calls(self):
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tool_calls = [
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{"id": "call-1", "name": "bash", "args": {"command": "ls"}},
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]
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msg = _make_ai_message(content="", tool_calls=tool_calls)
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result = langchain_to_openai_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] is None
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assert len(result["tool_calls"]) == 1
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tc = result["tool_calls"][0]
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assert tc["id"] == "call-1"
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assert tc["type"] == "function"
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assert tc["function"]["name"] == "bash"
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# arguments must be a JSON string
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args = json.loads(tc["function"]["arguments"])
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assert args == {"command": "ls"}
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def test_ai_message_text_and_tool_calls(self):
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tool_calls = [
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{"id": "call-2", "name": "read_file", "args": {"path": "/tmp/x"}},
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]
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msg = _make_ai_message(content="Reading the file", tool_calls=tool_calls)
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result = langchain_to_openai_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] == "Reading the file"
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assert len(result["tool_calls"]) == 1
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def test_ai_message_empty_content_no_tools(self):
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msg = _make_ai_message(content="")
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result = langchain_to_openai_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] == ""
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assert "tool_calls" not in result
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def test_ai_message_list_content(self):
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# Multimodal content is preserved as-is
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list_content = [
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{"type": "text", "text": "Here is an image"},
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{"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}},
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]
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msg = _make_ai_message(content=list_content)
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result = langchain_to_openai_message(msg)
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assert result["role"] == "assistant"
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assert result["content"] == list_content
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def test_human_message(self):
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msg = _make_human_message("Tell me a joke")
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result = langchain_to_openai_message(msg)
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assert result["role"] == "user"
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assert result["content"] == "Tell me a joke"
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def test_tool_message(self):
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msg = _make_tool_message(content="file contents here", tool_call_id="call-xyz")
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result = langchain_to_openai_message(msg)
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assert result["role"] == "tool"
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assert result["tool_call_id"] == "call-xyz"
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assert result["content"] == "file contents here"
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def test_system_message(self):
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msg = _make_system_message("You are a helpful assistant.")
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result = langchain_to_openai_message(msg)
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assert result["role"] == "system"
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assert result["content"] == "You are a helpful assistant."
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class TestLangchainToOpenaiCompletion:
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def test_basic_completion(self):
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usage_metadata = {"input_tokens": 10, "output_tokens": 20}
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msg = _make_ai_message(
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content="Hello",
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id="msg-abc",
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usage_metadata=usage_metadata,
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response_metadata={"model_name": "gpt-4o", "finish_reason": "stop"},
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)
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result = langchain_to_openai_completion(msg)
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assert result["id"] == "msg-abc"
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assert result["model"] == "gpt-4o"
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assert len(result["choices"]) == 1
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choice = result["choices"][0]
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assert choice["index"] == 0
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assert choice["finish_reason"] == "stop"
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assert choice["message"]["role"] == "assistant"
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assert choice["message"]["content"] == "Hello"
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assert result["usage"] is not None
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assert result["usage"]["prompt_tokens"] == 10
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assert result["usage"]["completion_tokens"] == 20
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assert result["usage"]["total_tokens"] == 30
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def test_completion_with_tool_calls(self):
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tool_calls = [{"id": "call-1", "name": "bash", "args": {}}]
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msg = _make_ai_message(
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content="",
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tool_calls=tool_calls,
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id="msg-tc",
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response_metadata={"model_name": "gpt-4o"},
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)
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result = langchain_to_openai_completion(msg)
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assert result["choices"][0]["finish_reason"] == "tool_calls"
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def test_completion_no_usage(self):
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msg = _make_ai_message(content="Hi", id="msg-nousage", usage_metadata=None)
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result = langchain_to_openai_completion(msg)
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assert result["usage"] is None
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def test_finish_reason_from_response_metadata(self):
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msg = _make_ai_message(
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content="Done",
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id="msg-fr",
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response_metadata={"model_name": "claude-3", "finish_reason": "end_turn"},
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)
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result = langchain_to_openai_completion(msg)
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assert result["choices"][0]["finish_reason"] == "end_turn"
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def test_finish_reason_default_stop(self):
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msg = _make_ai_message(content="Done", id="msg-defstop", response_metadata={})
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result = langchain_to_openai_completion(msg)
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assert result["choices"][0]["finish_reason"] == "stop"
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class TestMessagesToOpenai:
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def test_convert_message_list(self):
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human = _make_human_message("Hi")
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ai = _make_ai_message(content="Hello!")
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tool_msg = _make_tool_message("result", "call-1")
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messages = [human, ai, tool_msg]
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result = langchain_messages_to_openai(messages)
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assert len(result) == 3
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assert result[0]["role"] == "user"
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assert result[1]["role"] == "assistant"
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assert result[2]["role"] == "tool"
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def test_empty_list(self):
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assert langchain_messages_to_openai([]) == []
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