Merge branch 'master' of http://192.168.0.200/knowledge-base-bj/deerflow-server into hwg
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@@ -6,8 +6,16 @@ import re
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from langchain_core.runnables import RunnableConfig
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from langchain_core.messages import HumanMessage, SystemMessage
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from ..state import AIWritingState
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from ._utils import llm_json
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from ._utils import (
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ThinkTagParser,
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_content_to_text,
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get_model,
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parse_json,
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safe_get_stream_writer,
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)
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logger = logging.getLogger(__name__)
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@@ -138,42 +146,79 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
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if fallback_type not in valid_keys:
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fallback_type = default_key
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result = await llm_json(
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system,
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user,
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fallback={
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"article_type": fallback_type,
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"target_audience": state.get("target_audience", "普通读者"),
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"word_count_target": state.get("word_count_target", 800),
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"refined_intent": state["user_intent"],
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},
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config=config,
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)
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fallback_result = {
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"article_type": fallback_type,
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"target_audience": state.get("target_audience", "普通读者"),
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"word_count_target": state.get("word_count_target", 800),
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"refined_intent": state["user_intent"],
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}
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# 意图解析模型调用失败时以非致命 notice 提示用户（llm_json 已用 fallback 兜底，
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# 流程仍可继续，但用户需知道结构化配置可能不准）。
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# 流式调用：与 writer_outline / writer_draft 同套机制 —— LLM 逐字吐出 JSON，
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# 通过 ``intent_chunk`` 推给前端打字机；完成后 ``parse_json`` 解析结构化结果，
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# 再通过 ``intent_ready`` 推一份结构化字段给前端结果卡片。
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writer = safe_get_stream_writer()
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llm_error: str | None = None
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full_text = ""
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parser = ThinkTagParser()
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try:
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model = get_model(config)
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async for chunk in model.astream(
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[SystemMessage(content=system), HumanMessage(content=user)]
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):
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delta = _content_to_text(chunk.content)
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if not delta:
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continue
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for is_thinking, text in parser.feed(delta):
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if not is_thinking:
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full_text += text
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writer({
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"type": "intent_chunk",
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"text": text,
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"is_thinking": is_thinking,
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})
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except Exception as exc:
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# 模型 / 网络挂掉 —— 不阻断流程，用 fallback 继续。
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logger.error("intent_parser streaming failed: %s", exc, exc_info=True)
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llm_error = f"{type(exc).__name__}: {exc}"
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result = parse_json(full_text, fallback_result) if full_text else fallback_result
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# 意图解析模型调用失败时以非致命 notice 提示用户（fallback 已兜底，流程仍可继续）
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progress_events: list[dict] = []
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if result.get("_llm_error"):
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if llm_error:
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progress_events.append({
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"type": "notice",
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"agent_name": "系统",
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"title": "模型错误",
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"detail": f"意图解析失败，已按默认配置兜底。原因：{result['_llm_error']}",
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"detail": f"意图解析失败，已按默认配置兜底。原因：{llm_error}",
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})
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progress_events.append({"type": "step_done", "agent_name": "系统", "message": "意图解析完成"})
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updates: dict = {"status": "researching", "progress_events": progress_events}
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# 校验 LLM 返回的 article_type 是否在有效范围内，不合法时回退到默认值
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raw_type = result.get("article_type", "")
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updates["article_type"] = raw_type if raw_type in valid_keys else fallback_type
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if result.get("target_audience"):
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updates["target_audience"] = result["target_audience"]
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final_article_type = raw_type if raw_type in valid_keys else fallback_type
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final_target_audience = result.get("target_audience") or fallback_result["target_audience"]
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word_count = _coerce_word_count(result.get("word_count_target"))
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final_word_count = word_count if word_count is not None else fallback_result["word_count_target"]
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final_refined_intent = result.get("refined_intent") or state["user_intent"]
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# 推送结构化结果给前端 —— 结果卡片显示文章类型 / 目标读者 / 字数 / 精炼意图
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writer({
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"type": "intent_ready",
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"article_type": final_article_type,
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"target_audience": final_target_audience,
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"word_count_target": final_word_count,
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"refined_intent": final_refined_intent,
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})
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progress_events.append({"type": "step_done", "agent_name": "系统", "message": "意图解析完成"})
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updates: dict = {"status": "researching", "progress_events": progress_events}
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updates["article_type"] = final_article_type
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if result.get("target_audience"):
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updates["target_audience"] = final_target_audience
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if word_count is not None:
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updates["word_count_target"] = word_count
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updates["word_count_target"] = final_word_count
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if result.get("refined_intent"):
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updates["user_intent"] = result["refined_intent"]
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updates["user_intent"] = final_refined_intent
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return updates
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@@ -1,51 +1,53 @@
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[project]
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name = "deer-flow"
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version = "0.1.0"
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description = "LangGraph-based AI agent system with sandbox execution capabilities"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"deerflow-harness",
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"fastapi>=0.115.0",
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"httpx>=0.28.0",
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"python-multipart>=0.0.26",
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"sse-starlette>=2.1.0",
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"uvicorn[standard]>=0.34.0",
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"lark-oapi>=1.4.0",
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"slack-sdk>=3.33.0",
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"python-telegram-bot>=21.0",
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"langgraph-sdk>=0.1.51",
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"markdown-to-mrkdwn>=0.3.1",
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"wecom-aibot-python-sdk>=0.1.6",
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"dingtalk-stream>=0.24.3",
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"bcrypt>=4.0.0",
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"pyjwt>=2.9.0",
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"email-validator>=2.0.0",
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"croniter>=6.2.2",
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]
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[project]
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name = "deer-flow"
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version = "0.1.0"
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description = "LangGraph-based AI agent system with sandbox execution capabilities"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"deerflow-harness",
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"fastapi>=0.115.0",
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"httpx>=0.28.0",
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"python-multipart>=0.0.26",
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"sse-starlette>=2.1.0",
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"uvicorn[standard]>=0.34.0",
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"lark-oapi>=1.4.0",
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"slack-sdk>=3.33.0",
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"python-telegram-bot>=21.0",
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"langgraph-sdk>=0.1.51",
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"markdown-to-mrkdwn>=0.3.1",
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"wecom-aibot-python-sdk>=0.1.6",
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"dingtalk-stream>=0.24.3",
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"bcrypt>=4.0.0",
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"pyjwt>=2.9.0",
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"email-validator>=2.0.0",
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"croniter>=6.2.2",
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"hindsight-client>=0.4.22",
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"asyncmy>=0.2.11",
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]
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[project.optional-dependencies]
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postgres = ["deerflow-harness[postgres]"]
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mysql = ["deerflow-harness[mysql]"]
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[dependency-groups]
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dev = [
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"prompt-toolkit>=3.0.0",
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"pytest>=9.0.3",
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"pytest-asyncio>=1.3.0",
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"ruff>=0.14.11",
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]
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[tool.pytest.ini_options]
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markers = [
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"no_auto_user: disable the conftest autouse contextvar fixture for this test",
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]
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[tool.uv]
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index-url = "https://pypi.org/simple"
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[tool.uv.workspace]
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members = ["packages/harness"]
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[tool.uv.sources]
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deerflow-harness = { workspace = true }
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[dependency-groups]
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dev = [
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"prompt-toolkit>=3.0.0",
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"pytest>=9.0.3",
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"pytest-asyncio>=1.3.0",
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"ruff>=0.14.11",
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]
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[tool.pytest.ini_options]
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markers = [
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"no_auto_user: disable the conftest autouse contextvar fixture for this test",
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]
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[tool.uv]
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index-url = "https://pypi.org/simple"
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[tool.uv.workspace]
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members = ["packages/harness"]
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[tool.uv.sources]
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deerflow-harness = { workspace = true }
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