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添加rag置信度判断
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构建并部署 AI Agent 服务 / deploy (push) Failing after 6m31s
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root
2026-05-06 01:15:52 +08:00
parent 3ae9daa01a
commit 1260bef5cb
35 changed files with 335 additions and 221 deletions
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21
backend/app/main_graph/nodes/llm_call.py
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@@ -12,7 +12,7 @@ from langchain_core.messages import AIMessage
from ...main_graph.state import MainGraphState
from ...agent.prompts import create_system_prompt
from ...utils.logging import log_state_change
from ...logger import debug, info, error
from backend.app.logger import debug, info, error
def create_dynamic_llm_call_node(chat_services: Dict[str, BaseChatModel], tools: list):
@@ -115,24 +115,7 @@ def create_dynamic_llm_call_node(chat_services: Dict[str, BaseChatModel], tools:
):
chunks.append(chunk)
info(f"[llm_call] LLM astream 完成，共收到 {len(chunks)} 个 chunks")
for i, chunk in enumerate(chunks[:10]): # 只打印前10个避免日志过多
chunk_type = type(chunk).__name__
chunk_content = getattr(chunk, 'content', '') if hasattr(chunk, 'content') else str(chunk)
# 打印更多属性
additional_kwargs = getattr(chunk, 'additional_kwargs', {}) or {}
response_metadata = getattr(chunk, 'response_metadata', {}) or {}
# 打印所有属性
info(f"[llm_call] chunk[{i}] type={chunk_type}")
info(f"[llm_call] chunk[{i}] content长度={len(chunk_content) if chunk_content else 0}, content={repr(chunk_content[:200] if chunk_content else '')}")
info(f"[llm_call] chunk[{i}] additional_kwargs={additional_kwargs}")
info(f"[llm_call] chunk[{i}] response_metadata keys={list(response_metadata.keys()) if response_metadata else []}")
info(f"[llm_call] chunk[{i}] response_metadata={response_metadata}")
# 检查是否有其他可能存储内容的属性
if hasattr(chunk, 'tool_call_chunks'):
info(f"[llm_call] chunk[{i}] tool_call_chunks={chunk.tool_call_chunks}")
if hasattr(chunk, 'usage_metadata'):
info(f"[llm_call] chunk[{i}] usage_metadata={chunk.usage_metadata}")
info(f"[llm_call] LLM astream 完成，共收到 {len(chunks)} 个 chunks,info:{chunks}")
# 将所有 chunk 合并成最终的 AIMessage
if chunks: