fix: 修复聊天部分存在的问题
This commit is contained in:
parent
22a4b8a4bb
commit
fcfd1146b8
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@ -2,8 +2,9 @@
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import json
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import uuid
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import logging
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from typing import List, Dict, Any, AsyncGenerator, Optional
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from typing import List, Dict, AsyncGenerator
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from luxx.database import SessionLocal
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from luxx.models import Conversation, Message
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from luxx.tools.executor import ToolExecutor
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from luxx.tools.core import registry
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@ -11,7 +12,6 @@ from luxx.services.llm_client import LLMClient
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from luxx.config import config
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logger = logging.getLogger(__name__)
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# Maximum iterations to prevent infinite loops
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MAX_ITERATIONS = 10
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@ -40,7 +40,6 @@ def get_llm_client(conversation: Conversation = None):
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finally:
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db.close()
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# Fallback to global config
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client = LLMClient()
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return client, max_tokens
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@ -75,7 +74,6 @@ class ChatService:
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).order_by(Message.created_at).all()
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for msg in db_messages:
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# Parse JSON content if possible
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try:
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content_obj = json.loads(msg.content) if msg.content else {}
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if isinstance(content_obj, dict):
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@ -105,328 +103,223 @@ class ChatService:
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workspace: str = None,
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user_permission_level: int = 1
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) -> AsyncGenerator[Dict[str, str], None]:
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"""
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Streaming response generator
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"""Streaming response generator"""
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messages = self.build_messages(conversation)
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messages.append({
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"role": "user",
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"content": json.dumps({"text": user_message, "attachments": []})
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})
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Yields raw SSE event strings for direct forwarding.
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"""
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try:
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messages = self.build_messages(conversation)
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tools = [t for t in registry.list_all() if t.get("function", {}).get("name") in enabled_tools] if enabled_tools else []
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llm, provider_max_tokens = get_llm_client(conversation)
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model = conversation.model or llm.default_model or "gpt-4"
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max_tokens = provider_max_tokens
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all_steps, all_tool_calls, all_tool_results = [], [], []
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total_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
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for iteration in range(MAX_ITERATIONS):
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result = await self._stream_from_llm(
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llm, model, messages, tools, conversation, max_tokens,
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thinking_enabled, total_usage
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)
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messages.append({
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"role": "user",
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"content": json.dumps({"text": user_message, "attachments": []})
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})
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if result.get("error"):
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yield _sse_event("error", {"content": result["error"]})
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return
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# Get tools based on enabled_tools filter
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if enabled_tools:
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tools = [t for t in registry.list_all() if t.get("function", {}).get("name") in enabled_tools]
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else:
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tools = []
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for sse in result.get("sse_events", []):
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yield sse
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llm, provider_max_tokens = get_llm_client(conversation)
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model = conversation.model or llm.default_model or "gpt-4"
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# 直接使用 provider 的 max_tokens
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max_tokens = provider_max_tokens
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full_content = result["content"]
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full_thinking = result.get("thinking", "")
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tool_calls_list = result.get("tool_calls", [])
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text_step_id = result.get("text_step_id")
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text_step_idx = result.get("text_step_idx")
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thinking_step_id = result.get("thinking_step_id")
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thinking_step_idx = result.get("thinking_step_idx")
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# State tracking
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all_steps = []
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all_tool_calls = []
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if thinking_step_id:
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all_steps.append({"id": thinking_step_id, "index": thinking_step_idx, "type": "thinking", "content": full_thinking})
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if text_step_id:
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all_steps.append({"id": text_step_id, "index": text_step_idx, "type": "text", "content": full_content})
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if not tool_calls_list:
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msg_id = str(uuid.uuid4())
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token_count = total_usage.get("completion_tokens", 0) or len(full_content) // 4
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self._save_message(conversation.id, msg_id, full_content, all_tool_calls, all_steps, token_count, total_usage)
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yield _sse_event("done", {"message_id": msg_id, "token_count": token_count, "usage": total_usage})
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return
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all_tool_calls.extend(tool_calls_list)
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# Build and yield tool call steps
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start_idx = len(all_steps)
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tool_call_step_ids = []
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for i, tc in enumerate(tool_calls_list):
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step_id = f"step-{start_idx + i}"
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tool_call_step_ids.append(step_id)
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step = {
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"id": step_id, "index": start_idx + i, "type": "tool_call",
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"id_ref": tc.get("id", ""), "name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"]
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}
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all_steps.append(step)
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yield _sse_event("process_step", {"step": step})
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# Execute tools
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tool_results = self.tool_executor.process_tool_calls_parallel(
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tool_calls_list,
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{"workspace": workspace, "user_id": user_id, "username": username, "user_permission_level": user_permission_level}
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)
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# Build and yield tool result steps
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start_idx = len(all_steps)
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for i, tr in enumerate(tool_results):
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step_id = f"step-{start_idx + i}"
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step_ref = tool_call_step_ids[i] if i < len(tool_call_step_ids) else f"step-{i}"
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content = tr.get("content", "")
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try:
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content_obj = json.loads(content)
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if isinstance(content_obj, dict):
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success = content_obj.get("success", True)
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except:
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success = True
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step = {
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"id": step_id, "index": start_idx + i, "type": "tool_result",
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"id_ref": step_ref, "name": tr.get("name", ""),
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"content": content, "success": success
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}
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all_steps.append(step)
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yield _sse_event("process_step", {"step": step})
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all_tool_results.append({
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"role": "tool",
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"tool_call_id": tr.get("tool_call_id", ""),
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"content": content
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})
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messages.append({"role": "assistant", "content": full_content or "", "tool_calls": tool_calls_list})
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messages.extend(all_tool_results[-len(tool_results):])
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all_tool_results = []
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step_index = 0
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if full_content or all_tool_calls:
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msg_id = str(uuid.uuid4())
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token_count = total_usage.get("completion_tokens", 0) or len(full_content) // 4
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self._save_message(conversation.id, msg_id, full_content, all_tool_calls, all_tool_results, all_steps, token_count, total_usage)
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yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
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async def _stream_from_llm(
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self, llm, model, messages, tools, conversation, max_tokens,
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thinking_enabled, total_usage
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) -> Dict:
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"""Stream from LLM and return parsed result."""
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full_content, full_thinking = "", ""
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tool_calls_list, step_index = [], 0
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thinking_step_id, thinking_step_idx, text_step_id, text_step_idx = None, None, None, None
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sse_events = []
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async for sse_line in llm.stream_call(
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model=model, messages=messages, tools=tools,
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temperature=conversation.temperature,
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max_tokens=max_tokens or 8192,
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thinking_enabled=thinking_enabled or conversation.thinking_enabled
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):
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event_type, data_str = self._parse_sse_line(sse_line)
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if data_str is None:
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continue
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# Token usage tracking
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total_usage = {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}
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if event_type == 'error':
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try:
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error_data = json.loads(data_str)
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return {"error": error_data.get("content", "Unknown error")}
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except json.JSONDecodeError:
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return {"error": data_str}
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# Global step IDs for thinking and text (persist across iterations)
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thinking_step_id = None
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thinking_step_idx = None
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text_step_id = None
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text_step_idx = None
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try:
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chunk = json.loads(data_str)
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except json.JSONDecodeError:
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return {"error": f"Failed to parse response: {data_str}"}
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for iteration in range(MAX_ITERATIONS):
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# Stream from LLM
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full_content = ""
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full_thinking = ""
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tool_calls_list = []
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# Step tracking - use unified step-{index} format
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thinking_step_id = None
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thinking_step_idx = None
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text_step_id = None
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text_step_idx = None
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async for sse_line in llm.stream_call(
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model=model,
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messages=messages,
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tools=tools,
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temperature=conversation.temperature,
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max_tokens=max_tokens or 8192,
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thinking_enabled=thinking_enabled or conversation.thinking_enabled
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):
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# Parse SSE line
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# Format: "event: xxx\ndata: {...}\n\n"
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event_type = None
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data_str = None
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for line in sse_line.strip().split('\n'):
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if line.startswith('event: '):
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event_type = line[7:].strip()
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elif line.startswith('data: '):
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data_str = line[6:].strip()
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if data_str is None:
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continue
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# Handle error events from LLM
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if event_type == 'error':
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try:
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error_data = json.loads(data_str)
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yield _sse_event("error", {"content": error_data.get("content", "Unknown error")})
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except json.JSONDecodeError:
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yield _sse_event("error", {"content": data_str})
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return
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# Parse the data
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try:
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chunk = json.loads(data_str)
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except json.JSONDecodeError:
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yield _sse_event("error", {"content": f"Failed to parse response: {data_str}"})
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return
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# 提取 API 返回的 usage 信息
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if "usage" in chunk:
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usage = chunk["usage"]
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total_usage["prompt_tokens"] = usage.get("prompt_tokens", 0)
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total_usage["completion_tokens"] = usage.get("completion_tokens", 0)
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total_usage["total_tokens"] = usage.get("total_tokens", 0)
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# Check for error in response
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if "error" in chunk:
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error_msg = chunk["error"].get("message", str(chunk["error"]))
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yield _sse_event("error", {"content": f"API Error: {error_msg}"})
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return
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# Get delta
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choices = chunk.get("choices", [])
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if not choices:
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# Check if there's any content in the response (for non-standard LLM responses)
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if chunk.get("content") or chunk.get("message"):
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content = chunk.get("content") or chunk.get("message", {}).get("content", "")
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if content:
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# BUG FIX: Update full_content so it gets saved to database
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prev_content_len = len(full_content)
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full_content += content
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if prev_content_len == 0: # New text stream started
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text_step_idx = step_index
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text_step_id = f"step-{step_index}"
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step_index += 1
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yield _sse_event("process_step", {
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"step": {
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"id": text_step_id if prev_content_len == 0 else f"step-{step_index - 1}",
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"index": text_step_idx if prev_content_len == 0 else step_index - 1,
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"type": "text",
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"content": full_content # Always send accumulated content
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}
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})
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continue
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delta = choices[0].get("delta", {})
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# Handle reasoning (thinking)
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reasoning = delta.get("reasoning_content", "")
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if reasoning:
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prev_thinking_len = len(full_thinking)
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full_thinking += reasoning
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if prev_thinking_len == 0: # New thinking stream started
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thinking_step_idx = step_index
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thinking_step_id = f"step-{step_index}"
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step_index += 1
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yield _sse_event("process_step", {
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"step": {
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"id": thinking_step_id,
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"index": thinking_step_idx,
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"type": "thinking",
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"content": full_thinking
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}
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})
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# Handle content
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content = delta.get("content", "")
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if "usage" in chunk:
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usage = chunk["usage"]
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total_usage["prompt_tokens"] = usage.get("prompt_tokens", 0)
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total_usage["completion_tokens"] = usage.get("completion_tokens", 0)
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total_usage["total_tokens"] = usage.get("total_tokens", 0)
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if "error" in chunk:
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return {"error": chunk["error"].get("message", str(chunk["error"]))}
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choices = chunk.get("choices", [])
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if not choices:
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if chunk.get("content") or chunk.get("message"):
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content = chunk.get("content") or chunk.get("message", {}).get("content", "")
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if content:
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prev_content_len = len(full_content)
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prev_len = len(full_content)
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full_content += content
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if prev_content_len == 0: # New text stream started
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if prev_len == 0:
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text_step_idx = step_index
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text_step_id = f"step-{step_index}"
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step_index += 1
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yield _sse_event("process_step", {
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"step": {
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"id": text_step_id,
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"index": text_step_idx,
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"type": "text",
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"content": full_content
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}
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})
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# Accumulate tool calls
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tool_calls_delta = delta.get("tool_calls", [])
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for tc in tool_calls_delta:
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idx = tc.get("index", 0)
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if idx >= len(tool_calls_list):
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tool_calls_list.append({
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"id": tc.get("id", ""),
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"type": "function",
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"function": {"name": "", "arguments": ""}
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})
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func = tc.get("function", {})
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if func.get("name"):
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tool_calls_list[idx]["function"]["name"] += func["name"]
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if func.get("arguments"):
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tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
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# Save thinking step
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if thinking_step_id is not None:
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all_steps.append({
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"id": thinking_step_id,
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"index": thinking_step_idx,
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"type": "thinking",
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"content": full_thinking
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})
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# Save text step
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if text_step_id is not None:
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all_steps.append({
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"id": text_step_id,
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"index": text_step_idx,
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"type": "text",
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"content": full_content
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})
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# Handle tool calls
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if tool_calls_list:
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all_tool_calls.extend(tool_calls_list)
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# Yield tool_call steps - use unified step-{index} format
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tool_call_step_ids = [] # Track step IDs for tool calls
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for tc in tool_calls_list:
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call_step_idx = step_index
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call_step_id = f"step-{step_index}"
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tool_call_step_ids.append(call_step_id)
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step_index += 1
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call_step = {
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"id": call_step_id,
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"index": call_step_idx,
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"type": "tool_call",
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"id_ref": tc.get("id", ""),
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"]
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}
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all_steps.append(call_step)
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yield _sse_event("process_step", {"step": call_step})
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# Execute tools
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tool_context = {
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"workspace": workspace,
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"user_id": user_id,
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"username": username,
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"user_permission_level": user_permission_level
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}
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tool_results = self.tool_executor.process_tool_calls_parallel(
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tool_calls_list, tool_context
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)
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# Yield tool_result steps - use unified step-{index} format
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for i, tr in enumerate(tool_results):
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tool_call_step_id = tool_call_step_ids[i] if i < len(tool_call_step_ids) else f"step-{i}"
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result_step_idx = step_index
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result_step_id = f"step-{step_index}"
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step_index += 1
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# 解析 content 中的 success 状态
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content = tr.get("content", "")
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success = True
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try:
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content_obj = json.loads(content)
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if isinstance(content_obj, dict):
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success = content_obj.get("success", True)
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except:
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pass
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result_step = {
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"id": result_step_id,
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"index": result_step_idx,
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"type": "tool_result",
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"id_ref": tool_call_step_id, # Reference to the tool_call step
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"name": tr.get("name", ""),
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"content": content,
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"success": success
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}
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all_steps.append(result_step)
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yield _sse_event("process_step", {"step": result_step})
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all_tool_results.append({
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"role": "tool",
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"tool_call_id": tr.get("tool_call_id", ""),
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"content": tr.get("content", "")
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})
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# Add assistant message with tool calls for next iteration
|
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messages.append({
|
||||
"role": "assistant",
|
||||
"content": full_content or "",
|
||||
"tool_calls": tool_calls_list
|
||||
})
|
||||
messages.extend(all_tool_results[-len(tool_results):])
|
||||
all_tool_results = []
|
||||
continue
|
||||
|
||||
# No tool calls - final iteration, save message
|
||||
msg_id = str(uuid.uuid4())
|
||||
|
||||
# 使用 API 返回的真实 completion_tokens，如果 API 没返回则降级使用估算值
|
||||
actual_token_count = total_usage.get("completion_tokens", 0) or len(full_content) // 4
|
||||
logger.info(f"[TOKEN] total_usage: {total_usage}, actual_token_count: {actual_token_count}")
|
||||
|
||||
self._save_message(
|
||||
conversation.id,
|
||||
msg_id,
|
||||
full_content,
|
||||
all_tool_calls,
|
||||
all_tool_results,
|
||||
all_steps,
|
||||
actual_token_count,
|
||||
total_usage
|
||||
)
|
||||
|
||||
yield _sse_event("done", {
|
||||
"message_id": msg_id,
|
||||
"token_count": actual_token_count,
|
||||
"usage": total_usage
|
||||
})
|
||||
return
|
||||
sse_events.append(_sse_event("process_step", {
|
||||
"step": {"id": text_step_id, "index": text_step_idx, "type": "text", "content": full_content}
|
||||
}))
|
||||
continue
|
||||
|
||||
# Max iterations exceeded - save message before error
|
||||
if full_content or all_tool_calls:
|
||||
msg_id = str(uuid.uuid4())
|
||||
self._save_message(
|
||||
conversation.id,
|
||||
msg_id,
|
||||
full_content,
|
||||
all_tool_calls,
|
||||
all_tool_results,
|
||||
all_steps,
|
||||
actual_token_count,
|
||||
total_usage
|
||||
)
|
||||
yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
|
||||
delta = choices[0].get("delta", {})
|
||||
reasoning = delta.get("reasoning_content", "")
|
||||
if reasoning:
|
||||
prev_len = len(full_thinking)
|
||||
full_thinking += reasoning
|
||||
if prev_len == 0:
|
||||
thinking_step_idx = step_index
|
||||
thinking_step_id = f"step-{step_index}"
|
||||
step_index += 1
|
||||
sse_events.append(_sse_event("process_step", {
|
||||
"step": {"id": thinking_step_id, "index": thinking_step_idx, "type": "thinking", "content": full_thinking}
|
||||
}))
|
||||
|
||||
except Exception as e:
|
||||
yield _sse_event("error", {"content": str(e)})
|
||||
content = delta.get("content", "")
|
||||
if content:
|
||||
prev_len = len(full_content)
|
||||
full_content += content
|
||||
if prev_len == 0:
|
||||
text_step_idx = step_index
|
||||
text_step_id = f"step-{step_index}"
|
||||
step_index += 1
|
||||
sse_events.append(_sse_event("process_step", {
|
||||
"step": {"id": text_step_id, "index": text_step_idx, "type": "text", "content": full_content}
|
||||
}))
|
||||
|
||||
for tc in delta.get("tool_calls", []):
|
||||
idx = tc.get("index", 0)
|
||||
if idx >= len(tool_calls_list):
|
||||
tool_calls_list.append({"id": tc.get("id", ""), "type": "function", "function": {"name": "", "arguments": ""}})
|
||||
func = tc.get("function", {})
|
||||
if func.get("name"):
|
||||
tool_calls_list[idx]["function"]["name"] += func["name"]
|
||||
if func.get("arguments"):
|
||||
tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
|
||||
|
||||
return {
|
||||
"content": full_content, "thinking": full_thinking,
|
||||
"tool_calls": tool_calls_list, "text_step_id": text_step_id,
|
||||
"text_step_idx": text_step_idx, "thinking_step_id": thinking_step_id,
|
||||
"thinking_step_idx": thinking_step_idx, "sse_events": sse_events
|
||||
}
|
||||
|
||||
def _parse_sse_line(self, line: str) -> tuple:
|
||||
"""Parse SSE line. Returns (event_type, data_str)."""
|
||||
event_type, data_str = None, None
|
||||
for part in line.strip().split('\n'):
|
||||
if part.startswith('event: '):
|
||||
event_type = part[7:].strip()
|
||||
elif part.startswith('data: '):
|
||||
data_str = part[6:].strip()
|
||||
return event_type, data_str
|
||||
|
||||
def _save_message(
|
||||
self,
|
||||
|
|
@ -434,19 +327,14 @@ class ChatService:
|
|||
msg_id: str,
|
||||
full_content: str,
|
||||
all_tool_calls: list,
|
||||
all_tool_results: list,
|
||||
all_steps: list,
|
||||
token_count: int = 0,
|
||||
usage: dict = None
|
||||
):
|
||||
"""Save the assistant message to database."""
|
||||
from luxx.database import SessionLocal
|
||||
from luxx.models import Message
|
||||
|
||||
|
||||
content_json = {
|
||||
"text": full_content,
|
||||
"steps": all_steps
|
||||
}
|
||||
content_json = {"text": full_content, "steps": all_steps}
|
||||
if all_tool_calls:
|
||||
content_json["tool_calls"] = all_tool_calls
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue