refactor: 优化文件结构
This commit is contained in:
parent
08d2a2be98
commit
ce9ee6dd89
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@ -0,0 +1,238 @@
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"""AgenticLoop - Executes the Agentic Loop: LLM + Tools iteration.
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The loop:
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1. Call LLM with messages and tools
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2. Check for tool calls in response
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3. Execute tools in parallel
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4. Add results to messages
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5. Repeat (max 10 iterations)
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6. Return final response
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"""
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import json
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import uuid
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import logging
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import traceback
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from typing import List, Dict, Any, AsyncGenerator
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from luxx.tools.executor import ToolExecutor
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from luxx.services.llm_client import LLMClient
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from luxx.services.stream_context import StreamContext, _sse_event
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from luxx.services.process_result import ProcessResult
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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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def _parse_sse_line(line: str) -> tuple:
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"""Parse SSE line into (event_type, data_str)."""
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event_type = None
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data_str = None
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for part in line.strip().split('\n'):
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if part.startswith('event: '):
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event_type = part[7:].strip()
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elif part.startswith('data: '):
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data_str = part[6:].strip()
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return event_type, data_str
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class AgenticLoop:
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"""Executes the Agentic Loop: LLM + Tools iteration."""
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def __init__(self, tool_executor: ToolExecutor):
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self.tool_executor = tool_executor
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async def execute(
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self,
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llm: LLMClient,
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model: str,
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messages: List[Dict],
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tools: list,
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temperature: float,
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max_tokens: int,
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thinking_enabled: bool,
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context: 'StreamContext',
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tool_context: dict = None
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) -> AsyncGenerator[str, None]:
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"""Execute the agentic loop.
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Yields SSE events for each step.
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"""
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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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context.reset()
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has_error = False
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# Stream LLM response
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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=temperature,
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max_tokens=max_tokens,
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thinking_enabled=thinking_enabled
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):
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# Process stream line
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result = self._process_stream_line(sse_line, context, total_usage)
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# Yield events
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for event in result.events:
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yield event
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# Check for errors
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if result.has_error:
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has_error = True
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break
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# If error occurred, break the loop
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if has_error:
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break
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# Finalize current step
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context.finalize_step()
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# Check for tool calls
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if context.tool_calls_list:
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# Execute tools and yield events
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for event in self._execute_tools(context, messages, tool_context):
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yield event
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continue
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# No tools - complete
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for event in self._complete(context, total_usage):
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yield event
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return
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# Max iterations exceeded or error occurred
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if not has_error:
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yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
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def _process_stream_line(self, sse_line: str, ctx: 'StreamContext',
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total_usage: dict) -> ProcessResult:
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"""Process single SSE line from LLM, return result with events and flags."""
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result = ProcessResult()
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event_type, data_str = _parse_sse_line(sse_line)
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if not data_str:
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return result
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# Handle upstream errors
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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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error_content = error_data.get("content", "Unknown error")
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except json.JSONDecodeError:
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error_content = data_str
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result.set_error(error_content)
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result.add_event(_sse_event("error", {"content": error_content}))
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return result
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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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error_msg = f"Parse error: {data_str[:50]}"
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result.set_error(error_msg)
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result.add_event(_sse_event("error", {"content": error_msg}))
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return result
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# Extract usage
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if "usage" in chunk and chunk["usage"]:
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usage = chunk["usage"]
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total_usage.update({
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"prompt_tokens": usage.get("prompt_tokens", 0),
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"completion_tokens": usage.get("completion_tokens", 0),
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"total_tokens": usage.get("total_tokens", 0)
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})
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# Handle API errors
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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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result.set_error(error_msg)
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result.add_event(_sse_event("error", {"content": f"API Error: {error_msg}"}))
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return result
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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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content = chunk.get("content") or chunk.get("message", {}).get("content", "")
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if content:
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if not ctx.full_content:
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ctx.start_step("text")
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ctx.full_content += content
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result.set_content()
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result.add_event(_sse_event("process_step", {
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"step": {"id": ctx.current_step_id, "index": ctx.current_step_idx, "type": "text", "content": ctx.full_content}
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}))
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return result
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delta = choices[0].get("delta", {})
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# Process deltas
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event = ctx.handle_thinking(delta)
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if event:
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result.set_content()
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result.add_event(event)
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event = ctx.handle_text(delta)
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if event:
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result.set_content()
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result.add_event(event)
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# Accumulate tool calls
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for tc in delta.get("tool_calls", []):
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ctx.accumulate_tool_call(tc)
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result.set_tool_calls()
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return result
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def _execute_tools(self, ctx: 'StreamContext', messages: list,
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tool_context: dict = None) -> List[str]:
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"""Execute tools and return list of events."""
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events = []
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# Emit tool call steps
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for event in ctx.emit_tool_calls():
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events.append(event)
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# Execute in parallel
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tool_results = self.tool_executor.process_tool_calls_parallel(
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ctx.tool_calls_list, tool_context or {}
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)
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# Get tool call IDs for result linking
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tool_ids = [tc.get("id") for tc in ctx.tool_calls_list]
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tool_step_ids = [
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s["id"] for s in ctx.all_steps
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if s["type"] == "tool_call" and s.get("id_ref") in tool_ids
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]
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# Emit tool result steps
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for i, (tr, tc) in enumerate(zip(tool_results, ctx.tool_calls_list)):
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ref_id = tool_step_ids[i] if i < len(tool_step_ids) else f"step-{len(ctx.all_steps) - len(tool_results) + i}"
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_, event = ctx.emit_tool_result(tr, ref_id)
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events.append(event)
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# Prepare for next iteration
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messages.append({
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"role": "assistant",
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"content": ctx.full_content or "",
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"tool_calls": ctx.tool_calls_list
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})
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messages.extend(ctx.all_tool_results[-len(tool_results):])
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return events
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def _complete(self, ctx: 'StreamContext', total_usage: dict) -> List[str]:
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"""Complete the loop and return list of events."""
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token_count = total_usage.get("completion_tokens") or len(ctx.full_content) // 4
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msg_id = str(uuid.uuid4())
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logger.info(f"[TOKEN] usage={total_usage}, count={token_count}")
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ctx.set_completion(msg_id, token_count, total_usage)
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return [_sse_event("done", {
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"message_id": msg_id,
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"token_count": token_count,
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"usage": total_usage
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})]
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@ -1,34 +1,99 @@
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"""Chat service module"""
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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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"""Chat service module with Agentic Loop pattern.
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This module provides the core chat service that orchestrates:
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- StreamContext: Manages streaming state transitions
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- MessageBuilder: Constructs message lists
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- AgenticLoop: Executes the agentic loop (LLM + tools iteration)
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- ChatService: Core chat service facade
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"""
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import json
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import logging
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import traceback
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import httpx
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from typing import List, Dict, Any, AsyncGenerator
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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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from luxx.services.llm_client import LLMClient
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from luxx.services.stream_context import StreamContext
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from luxx.services.agentic_loop import AgenticLoop
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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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def _sse_event(event: str, data: dict) -> str:
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"""Format a Server-Sent Event string."""
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return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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# ============== MessageBuilder ==============
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class MessageBuilder:
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"""Builds message lists for LLM requests."""
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def __init__(self):
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self.messages = []
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def add_system(self, content: str) -> 'MessageBuilder':
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"""Add system message."""
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self.messages.append({"role": "system", "content": content})
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return self
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def add_user(self, content: str, attachments: list = None) -> 'MessageBuilder':
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"""Add user message in JSON format."""
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msg_content = json.dumps({
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"text": content,
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"attachments": attachments or []
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}, ensure_ascii=False)
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self.messages.append({"role": "user", "content": msg_content})
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return self
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def add_assistant(self, content: str, tool_calls: list = None) -> 'MessageBuilder':
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"""Add assistant message."""
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msg = {"role": "assistant", "content": content}
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if tool_calls:
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msg["tool_calls"] = tool_calls
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self.messages.append(msg)
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return self
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def add_tool_result(self, tool_call_id: str, content: str) -> 'MessageBuilder':
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"""Add tool result message."""
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self.messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": content
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})
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return self
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def build(self) -> List[Dict]:
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"""Build and return message list."""
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return self.messages.copy()
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@staticmethod
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def extract_text(content: str) -> str:
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"""Extract text from message content (supports JSON format)."""
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if not content:
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return ""
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try:
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parsed = json.loads(content)
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if isinstance(parsed, dict):
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return parsed.get("text", content)
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except (json.JSONDecodeError, TypeError):
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pass
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return content
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def get_llm_client(conversation: Conversation = None):
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"""Get LLM client, optionally using conversation's provider. Returns (client, max_tokens)"""
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# ============== Factory Function ==============
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def get_llm_client(conversation=None) -> tuple:
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"""Get LLM client based on conversation provider. Returns (client, max_tokens)"""
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from luxx.models import LLMProvider
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from luxx.database import SessionLocal
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max_tokens = None
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if conversation and conversation.provider_id:
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from luxx.models import LLMProvider
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from luxx.database import SessionLocal
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db = SessionLocal()
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try:
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provider = db.query(LLMProvider).filter(LLMProvider.id == conversation.provider_id).first()
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provider = db.query(LLMProvider).filter(
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LLMProvider.id == conversation.provider_id
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).first()
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if provider:
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max_tokens = provider.max_tokens
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client = LLMClient(
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@ -40,184 +105,27 @@ 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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return LLMClient(), max_tokens
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class StreamContext:
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"""Context for streaming response state management."""
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def __init__(
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self,
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step_index: int = 0,
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current_step_id: str = None,
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current_step_idx: int = None,
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current_stream_type: str = None,
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full_content: str = "",
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full_thinking: str = ""
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):
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self.step_index = step_index
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self.current_step_id = current_step_id
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self.current_step_idx = current_step_idx
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self.current_stream_type = current_stream_type
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self.full_content = full_content
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self.full_thinking = full_thinking
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self.all_steps = []
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self.all_tool_calls = []
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self.all_tool_results = []
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self.tool_calls_list = []
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def reset_iteration(self):
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"""Reset streaming step tracker for new iteration."""
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self.current_step_id = None
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self.current_step_idx = None
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self.current_stream_type = None
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self.full_content = ""
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self.full_thinking = ""
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self.tool_calls_list = []
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def start_stream_step(self, step_type: str) -> str:
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"""Start a new streaming step. Returns the step_id."""
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self.current_step_idx = self.step_index
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self.current_step_id = f"step-{self.step_index}"
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self.current_stream_type = step_type
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self.step_index += 1
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return self.current_step_id
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def yield_stream_step(self, step_type: str, content: str) -> Dict[str, Any]:
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"""Yield a streaming step event."""
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return _sse_event("process_step", {
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"step": {
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"id": self.current_step_id,
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"index": self.current_step_idx,
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"type": step_type,
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"content": content
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}
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})
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def save_streaming_step(self):
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"""Save the current streaming step to all_steps."""
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if self.current_step_id is None:
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return
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if self.current_stream_type == "thinking":
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self.all_steps.append({
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"id": self.current_step_id,
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"index": self.current_step_idx,
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"type": "thinking",
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"content": self.full_thinking
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})
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elif self.current_stream_type == "text":
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self.all_steps.append({
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"id": self.current_step_id,
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"index": self.current_step_idx,
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"type": "text",
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"content": self.full_content
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})
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def handle_thinking_stream(self, delta: Dict) -> Optional[Dict]:
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"""Handle reasoning/thinking delta. Returns yield_obj if step was yielded."""
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reasoning = delta.get("reasoning_content", "")
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if not reasoning:
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return None
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prev_len = len(self.full_thinking)
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self.full_thinking += reasoning
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if prev_len == 0: # New thinking stream started
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self.start_stream_step("thinking")
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return self.yield_stream_step("thinking", self.full_thinking)
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def handle_text_stream(self, delta: Dict) -> Optional[Dict]:
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"""Handle content delta. Returns yield_obj if step was yielded."""
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content = delta.get("content", "")
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if not content:
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return None
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prev_len = len(self.full_content)
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self.full_content += content
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if prev_len == 0: # New text stream started
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self.start_stream_step("text")
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return self.yield_stream_step("text", self.full_content)
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def handle_tool_call(self) -> tuple:
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"""Handle tool calls. Returns (tool_call_step_ids, tool_call_steps, yield_objs)."""
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tool_call_step_ids = []
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tool_call_steps = []
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yield_objs = []
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for tc in self.tool_calls_list:
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call_step_idx = self.step_index
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call_step_id = f"step-{self.step_index}"
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tool_call_step_ids.append(call_step_id)
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self.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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tool_call_steps.append(call_step)
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yield_objs.append(_sse_event("process_step", {"step": call_step}))
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return tool_call_step_ids, tool_call_steps, yield_objs
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def handle_tool_result(self, tool_result: Dict, tool_call_step_id: str) -> tuple:
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"""Handle single tool result. Returns (result_step, yield_obj)."""
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result_step_idx = self.step_index
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result_step_id = f"step-{self.step_index}"
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self.step_index += 1
|
||||
|
||||
content = tool_result.get("content", "")
|
||||
success = True
|
||||
try:
|
||||
content_obj = json.loads(content)
|
||||
if isinstance(content_obj, dict):
|
||||
success = content_obj.get("success", True)
|
||||
except:
|
||||
pass
|
||||
|
||||
result_step = {
|
||||
"id": result_step_id,
|
||||
"index": result_step_idx,
|
||||
"type": "tool_result",
|
||||
"id_ref": tool_call_step_id,
|
||||
"name": tool_result.get("name", ""),
|
||||
"content": content,
|
||||
"success": success
|
||||
}
|
||||
return result_step, _sse_event("process_step", {"step": result_step})
|
||||
|
||||
# ============== ChatService ==============
|
||||
|
||||
class ChatService:
|
||||
"""Chat service with tool support"""
|
||||
"""Core chat service with Agentic Loop support."""
|
||||
|
||||
def __init__(self):
|
||||
self.tool_executor = ToolExecutor()
|
||||
self.agentic_loop = AgenticLoop(self.tool_executor)
|
||||
|
||||
def build_messages(
|
||||
self,
|
||||
conversation: Conversation,
|
||||
include_system: bool = True
|
||||
) -> List[Dict[str, str]]:
|
||||
"""Build message list"""
|
||||
def build_messages(self, conversation, include_system: bool = True) -> List[Dict]:
|
||||
"""Build message list from conversation history."""
|
||||
from luxx.database import SessionLocal
|
||||
from luxx.models import Message
|
||||
|
||||
messages = []
|
||||
|
||||
if include_system and conversation.system_prompt:
|
||||
messages.append({
|
||||
"role": "system",
|
||||
"content": conversation.system_prompt
|
||||
})
|
||||
messages.append({"role": "system", "content": conversation.system_prompt})
|
||||
|
||||
db = SessionLocal()
|
||||
try:
|
||||
|
|
@ -226,28 +134,23 @@ class ChatService:
|
|||
).order_by(Message.created_at).all()
|
||||
|
||||
for msg in db_messages:
|
||||
# Parse JSON content if possible
|
||||
try:
|
||||
content_obj = json.loads(msg.content) if msg.content else {}
|
||||
if isinstance(content_obj, dict):
|
||||
content = content_obj.get("text", msg.content)
|
||||
else:
|
||||
content = msg.content
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
content = msg.content
|
||||
|
||||
messages.append({
|
||||
"role": msg.role,
|
||||
"content": content
|
||||
})
|
||||
content = MessageBuilder.extract_text(msg.content)
|
||||
messages.append({"role": msg.role, "content": content})
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
return messages
|
||||
|
||||
def _get_tools(self, enabled_tools: list) -> list:
|
||||
"""Filter tools based on enabled_tools list."""
|
||||
if not enabled_tools:
|
||||
return []
|
||||
return [t for t in registry.list_all()
|
||||
if t.get("function", {}).get("name") in enabled_tools]
|
||||
|
||||
async def stream_response(
|
||||
self,
|
||||
conversation: Conversation,
|
||||
conversation,
|
||||
user_message: str,
|
||||
thinking_enabled: bool = False,
|
||||
enabled_tools: list = None,
|
||||
|
|
@ -255,256 +158,118 @@ class ChatService:
|
|||
username: str = None,
|
||||
workspace: str = None,
|
||||
user_permission_level: int = 1
|
||||
) -> AsyncGenerator[Dict[str, str], None]:
|
||||
"""
|
||||
Streaming response generator
|
||||
|
||||
Yields raw SSE event strings for direct forwarding.
|
||||
"""
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Streaming response with Agentic Loop."""
|
||||
try:
|
||||
# Build initial messages
|
||||
messages = self.build_messages(conversation)
|
||||
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": json.dumps({"text": user_message, "attachments": []})
|
||||
})
|
||||
|
||||
# Get tools based on enabled_tools filter
|
||||
if enabled_tools:
|
||||
tools = [t for t in registry.list_all() if t.get("function", {}).get("name") in enabled_tools]
|
||||
else:
|
||||
tools = []
|
||||
|
||||
# Get tools and LLM client
|
||||
tools = self._get_tools(enabled_tools)
|
||||
llm, provider_max_tokens = get_llm_client(conversation)
|
||||
model = conversation.model or llm.default_model or "gpt-4"
|
||||
# 直接使用 provider 的 max_tokens
|
||||
max_tokens = provider_max_tokens
|
||||
max_tokens = provider_max_tokens or 8192
|
||||
|
||||
# Token usage tracking
|
||||
total_usage = {
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"total_tokens": 0
|
||||
# Tool execution context
|
||||
tool_context = {
|
||||
"workspace": workspace,
|
||||
"user_id": user_id,
|
||||
"username": username,
|
||||
"user_permission_level": user_permission_level
|
||||
}
|
||||
actual_token_count = 0
|
||||
|
||||
# Streaming context for state management
|
||||
# Stream context
|
||||
ctx = StreamContext()
|
||||
|
||||
for iteration in range(MAX_ITERATIONS):
|
||||
# Reset streaming context for this iteration
|
||||
ctx.reset_iteration()
|
||||
|
||||
async for sse_line in llm.stream_call(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
temperature=conversation.temperature,
|
||||
max_tokens=max_tokens or 8192,
|
||||
thinking_enabled=thinking_enabled or conversation.thinking_enabled
|
||||
):
|
||||
# Parse SSE line
|
||||
# Format: "event: xxx\ndata: {...}\n\n"
|
||||
event_type = None
|
||||
data_str = None
|
||||
|
||||
for line in sse_line.strip().split('\n'):
|
||||
if line.startswith('event: '):
|
||||
event_type = line[7:].strip()
|
||||
elif line.startswith('data: '):
|
||||
data_str = line[6:].strip()
|
||||
|
||||
if data_str is None:
|
||||
continue
|
||||
|
||||
# Handle error events from LLM
|
||||
if event_type == 'error':
|
||||
try:
|
||||
error_data = json.loads(data_str)
|
||||
yield _sse_event("error", {"content": error_data.get("content", "Unknown error")})
|
||||
except json.JSONDecodeError:
|
||||
yield _sse_event("error", {"content": data_str})
|
||||
return
|
||||
|
||||
# Parse the data
|
||||
try:
|
||||
chunk = json.loads(data_str)
|
||||
except json.JSONDecodeError:
|
||||
yield _sse_event("error", {"content": f"Failed to parse response: {data_str}"})
|
||||
return
|
||||
|
||||
# 提取 API 返回的 usage 信息
|
||||
if "usage" in chunk:
|
||||
usage = chunk["usage"]
|
||||
total_usage["prompt_tokens"] = usage.get("prompt_tokens", 0)
|
||||
total_usage["completion_tokens"] = usage.get("completion_tokens", 0)
|
||||
total_usage["total_tokens"] = usage.get("total_tokens", 0)
|
||||
|
||||
# Check for error in response
|
||||
if "error" in chunk:
|
||||
error_msg = chunk["error"].get("message", str(chunk["error"]))
|
||||
yield _sse_event("error", {"content": f"API Error: {error_msg}"})
|
||||
return
|
||||
|
||||
# Get delta
|
||||
choices = chunk.get("choices", [])
|
||||
if not choices:
|
||||
# Check if there's any content in the response (for non-standard LLM responses)
|
||||
if chunk.get("content") or chunk.get("message"):
|
||||
content = chunk.get("content") or chunk.get("message", {}).get("content", "")
|
||||
if content:
|
||||
prev_len = len(ctx.full_content)
|
||||
ctx.full_content += content
|
||||
if prev_len == 0: # New text stream started
|
||||
ctx.start_stream_step("text")
|
||||
yield _sse_event("process_step", {
|
||||
"step": {
|
||||
"id": ctx.current_step_id if prev_len == 0 else f"step-{ctx.step_index - 1}",
|
||||
"index": ctx.current_step_idx if prev_len == 0 else ctx.step_index - 1,
|
||||
"type": "text",
|
||||
"content": ctx.full_content
|
||||
}
|
||||
})
|
||||
continue
|
||||
|
||||
delta = choices[0].get("delta", {})
|
||||
|
||||
# Handle reasoning (thinking)
|
||||
yield_obj = ctx.handle_thinking_stream(delta)
|
||||
if yield_obj:
|
||||
yield yield_obj
|
||||
|
||||
# Handle content
|
||||
yield_obj = ctx.handle_text_stream(delta)
|
||||
if yield_obj:
|
||||
yield yield_obj
|
||||
|
||||
# Accumulate tool calls
|
||||
tool_calls_delta = delta.get("tool_calls", [])
|
||||
for tc in tool_calls_delta:
|
||||
idx = tc.get("index", 0)
|
||||
if idx >= len(ctx.tool_calls_list):
|
||||
ctx.tool_calls_list.append({
|
||||
"id": tc.get("id", ""),
|
||||
"type": "function",
|
||||
"function": {"name": "", "arguments": ""}
|
||||
})
|
||||
func = tc.get("function", {})
|
||||
if func.get("name"):
|
||||
ctx.tool_calls_list[idx]["function"]["name"] += func["name"]
|
||||
if func.get("arguments"):
|
||||
ctx.tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
|
||||
|
||||
# Save streaming step (thinking or text)
|
||||
ctx.save_streaming_step()
|
||||
|
||||
# Handle tool calls
|
||||
if ctx.tool_calls_list:
|
||||
ctx.all_tool_calls.extend(ctx.tool_calls_list)
|
||||
|
||||
# Handle tool_call steps
|
||||
tool_call_step_ids, tool_call_steps, yield_objs = ctx.handle_tool_call()
|
||||
ctx.all_steps.extend(tool_call_steps)
|
||||
for yield_obj in yield_objs:
|
||||
yield yield_obj
|
||||
|
||||
# Execute tools
|
||||
tool_context = {
|
||||
"workspace": workspace,
|
||||
"user_id": user_id,
|
||||
"username": username,
|
||||
"user_permission_level": user_permission_level
|
||||
}
|
||||
tool_results = self.tool_executor.process_tool_calls_parallel(
|
||||
ctx.tool_calls_list, tool_context
|
||||
)
|
||||
|
||||
# Handle tool_result steps
|
||||
for i, tr in enumerate(tool_results):
|
||||
tool_call_step_id = tool_call_step_ids[i] if i < len(tool_call_step_ids) else f"step-{i}"
|
||||
result_step, yield_obj = ctx.handle_tool_result(tr, tool_call_step_id)
|
||||
ctx.all_steps.append(result_step)
|
||||
yield yield_obj
|
||||
|
||||
ctx.all_tool_results.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tr.get("tool_call_id", ""),
|
||||
"content": tr.get("content", "")
|
||||
})
|
||||
|
||||
# Add assistant message with tool calls for next iteration
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": ctx.full_content or "",
|
||||
"tool_calls": ctx.tool_calls_list
|
||||
})
|
||||
messages.extend(ctx.all_tool_results[-len(tool_results):])
|
||||
ctx.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(ctx.full_content) // 4
|
||||
logger.info(f"[TOKEN] total_usage: {total_usage}, actual_token_count: {actual_token_count}")
|
||||
# Execute agentic loop
|
||||
async for event in self.agentic_loop.execute(
|
||||
llm=llm,
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
temperature=conversation.temperature,
|
||||
max_tokens=max_tokens,
|
||||
thinking_enabled=thinking_enabled or conversation.thinking_enabled,
|
||||
context=ctx,
|
||||
tool_context=tool_context
|
||||
):
|
||||
yield event
|
||||
|
||||
# Save message after successful completion (only if we have content)
|
||||
if ctx._last_message_id and (ctx.full_content or ctx.all_tool_calls):
|
||||
self._save_message(
|
||||
conversation.id,
|
||||
msg_id,
|
||||
ctx._last_message_id,
|
||||
ctx.full_content,
|
||||
ctx.all_tool_calls,
|
||||
ctx.all_tool_results,
|
||||
ctx.all_steps,
|
||||
actual_token_count,
|
||||
total_usage
|
||||
ctx._last_token_count,
|
||||
ctx._last_usage
|
||||
)
|
||||
|
||||
yield _sse_event("done", {
|
||||
"message_id": msg_id,
|
||||
"token_count": actual_token_count,
|
||||
"usage": total_usage
|
||||
})
|
||||
return
|
||||
|
||||
# Max iterations exceeded - save message before error
|
||||
if ctx.full_content or ctx.all_tool_calls:
|
||||
msg_id = str(uuid.uuid4())
|
||||
self._save_message(
|
||||
conversation.id,
|
||||
msg_id,
|
||||
ctx.full_content,
|
||||
ctx.all_tool_calls,
|
||||
ctx.all_tool_results,
|
||||
ctx.all_steps,
|
||||
actual_token_count,
|
||||
total_usage
|
||||
)
|
||||
yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Stream error: {e}")
|
||||
logger.error(f"Stream error: {e}\n{traceback.format_exc()}")
|
||||
yield _sse_event("error", {"content": str(e)})
|
||||
|
||||
def _save_message(
|
||||
async def non_stream_response(
|
||||
self,
|
||||
conversation_id: str,
|
||||
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."""
|
||||
conversation,
|
||||
user_message: str,
|
||||
tools_enabled: bool = True,
|
||||
thinking_enabled: bool = False
|
||||
) -> Dict[str, Any]:
|
||||
"""Non-streaming response for simple requests."""
|
||||
try:
|
||||
messages = self.build_messages(conversation)
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": json.dumps({"text": user_message, "attachments": []})
|
||||
})
|
||||
|
||||
tools = [] if not tools_enabled else None
|
||||
llm, max_tokens = get_llm_client(conversation)
|
||||
model = conversation.model or llm.default_model or "gpt-4"
|
||||
|
||||
response = await llm.sync_call(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
temperature=conversation.temperature,
|
||||
max_tokens=max_tokens or 8192,
|
||||
thinking_enabled=thinking_enabled or conversation.thinking_enabled
|
||||
)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"content": response.content,
|
||||
"tool_calls": response.tool_calls,
|
||||
"usage": response.usage
|
||||
}
|
||||
|
||||
except httpx.HTTPStatusError as e:
|
||||
error_msg = f"HTTP {e.response.status_code}: {e.response.text[:200] if e.response else 'No response body'}"
|
||||
logger.error(f"Non-stream HTTP error: {error_msg}")
|
||||
return {"success": False, "error": error_msg}
|
||||
except httpx.TimeoutException as e:
|
||||
logger.error(f"Non-stream timeout: {e}")
|
||||
return {"success": False, "error": "Request timeout"}
|
||||
except Exception as e:
|
||||
logger.error(f"Non-stream error: {type(e).__name__}: {e}\n{traceback.format_exc()}")
|
||||
return {"success": False, "error": f"{type(e).__name__}: {str(e)}"}
|
||||
|
||||
def _save_message(self, conversation_id: str, 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 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
|
||||
|
||||
|
|
@ -520,12 +285,18 @@ class ChatService:
|
|||
)
|
||||
db.add(msg)
|
||||
db.commit()
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
db.rollback()
|
||||
raise
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
# Global chat service
|
||||
def _sse_event(event: str, data: dict) -> str:
|
||||
"""Format a Server-Sent Event string."""
|
||||
return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
|
||||
|
||||
|
||||
# ============== Global Singleton ==============
|
||||
|
||||
chat_service = ChatService()
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
import json
|
||||
import httpx
|
||||
import logging
|
||||
import traceback
|
||||
from typing import Dict, Any, Optional, List, AsyncGenerator
|
||||
|
||||
from luxx.config import config
|
||||
|
|
@ -172,7 +173,7 @@ class LLMClient:
|
|||
logger.error(f"HTTP error: {status_code}")
|
||||
yield f"event: error\ndata: {json.dumps({'content': f'HTTP {status_code}: Request failed'})}\n\n"
|
||||
except Exception as e:
|
||||
logger.error(f"Exception: {type(e).__name__}: {str(e)}")
|
||||
logger.error(f"Exception: {type(e).__name__}: {str(e)}\n{traceback.format_exc()}")
|
||||
yield f"event: error\ndata: {json.dumps({'content': str(e)})}\n\n"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,37 @@
|
|||
"""ProcessResult - Result of processing an SSE line."""
|
||||
|
||||
|
||||
class ProcessResult:
|
||||
"""Result of processing an SSE line.
|
||||
|
||||
Attributes:
|
||||
events: List of SSE event strings to yield
|
||||
has_error: Whether an error occurred
|
||||
error_content: Error message if any
|
||||
has_content: Whether content was received
|
||||
has_tool_calls: Whether tool calls were received
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.events: list = []
|
||||
self.has_error: bool = False
|
||||
self.error_content: str = ""
|
||||
self.has_content: bool = False
|
||||
self.has_tool_calls: bool = False
|
||||
|
||||
def add_event(self, event: str):
|
||||
"""Add an event to the result."""
|
||||
self.events.append(event)
|
||||
|
||||
def set_error(self, content: str):
|
||||
"""Set error state."""
|
||||
self.has_error = True
|
||||
self.error_content = content
|
||||
|
||||
def set_content(self):
|
||||
"""Mark that content was received."""
|
||||
self.has_content = True
|
||||
|
||||
def set_tool_calls(self):
|
||||
"""Mark that tool calls were received."""
|
||||
self.has_tool_calls = True
|
||||
|
|
@ -0,0 +1,185 @@
|
|||
"""StreamContext - Manages streaming state transitions during LLM response.
|
||||
|
||||
Tracks steps in order:
|
||||
- thinking: Model reasoning content
|
||||
- text: Model response text
|
||||
- tool_call: Tool invocation request
|
||||
- tool_result: Tool execution result
|
||||
|
||||
Each step has unique id and index for frontend rendering.
|
||||
"""
|
||||
import json
|
||||
from typing import List, Dict, Optional
|
||||
|
||||
|
||||
def _sse_event(event: str, data: dict) -> str:
|
||||
"""Format a Server-Sent Event string."""
|
||||
return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
|
||||
|
||||
|
||||
class StreamContext:
|
||||
"""Manages streaming state transitions during LLM response."""
|
||||
|
||||
def __init__(self):
|
||||
self.step_index = 0
|
||||
self.current_step_id = None
|
||||
self.current_step_idx = None
|
||||
self.current_step_type = None
|
||||
self.full_content = ""
|
||||
self.full_thinking = ""
|
||||
self.all_steps = []
|
||||
self.all_tool_calls = []
|
||||
self.all_tool_results = []
|
||||
self.tool_calls_list = []
|
||||
self._last_message_id = None
|
||||
self._last_token_count = 0
|
||||
self._last_usage = None
|
||||
|
||||
def reset(self):
|
||||
"""Reset state for new iteration."""
|
||||
self.current_step_id = None
|
||||
self.current_step_idx = None
|
||||
self.current_step_type = None
|
||||
self.full_content = ""
|
||||
self.full_thinking = ""
|
||||
self.tool_calls_list = []
|
||||
|
||||
def start_step(self, step_type: str) -> str:
|
||||
"""Start a new step with unique ID."""
|
||||
self.current_step_idx = self.step_index
|
||||
self.current_step_id = f"step-{self.step_index}"
|
||||
self.current_step_type = step_type
|
||||
self.step_index += 1
|
||||
return self.current_step_id
|
||||
|
||||
def finalize_step(self):
|
||||
"""Save current step to all_steps."""
|
||||
if self.current_step_id is None:
|
||||
return
|
||||
|
||||
content = self.full_content if self.current_step_type == "text" else self.full_thinking
|
||||
self.all_steps.append({
|
||||
"id": self.current_step_id,
|
||||
"index": self.current_step_idx,
|
||||
"type": self.current_step_type,
|
||||
"content": content
|
||||
})
|
||||
|
||||
def handle_thinking(self, delta: Dict) -> Optional[str]:
|
||||
"""Handle reasoning delta from LLM."""
|
||||
reasoning = delta.get("reasoning_content", "")
|
||||
if not reasoning:
|
||||
return None
|
||||
|
||||
if not self.full_thinking:
|
||||
self.start_step("thinking")
|
||||
|
||||
self.full_thinking += reasoning
|
||||
return _sse_event("process_step", {
|
||||
"step": {
|
||||
"id": self.current_step_id,
|
||||
"index": self.current_step_idx,
|
||||
"type": "thinking",
|
||||
"content": self.full_thinking
|
||||
}
|
||||
})
|
||||
|
||||
def handle_text(self, delta: Dict) -> Optional[str]:
|
||||
"""Handle content delta from LLM."""
|
||||
content = delta.get("content", "")
|
||||
if not content:
|
||||
return None
|
||||
|
||||
if not self.full_content:
|
||||
self.start_step("text")
|
||||
|
||||
self.full_content += content
|
||||
return _sse_event("process_step", {
|
||||
"step": {
|
||||
"id": self.current_step_id,
|
||||
"index": self.current_step_idx,
|
||||
"type": "text",
|
||||
"content": self.full_content
|
||||
}
|
||||
})
|
||||
|
||||
def accumulate_tool_call(self, tc_delta: Dict):
|
||||
"""Accumulate tool call delta."""
|
||||
idx = tc_delta.get("index", 0)
|
||||
if idx >= len(self.tool_calls_list):
|
||||
self.tool_calls_list.append({
|
||||
"id": tc_delta.get("id", ""),
|
||||
"type": "function",
|
||||
"function": {"name": "", "arguments": ""}
|
||||
})
|
||||
|
||||
func = tc_delta.get("function", {})
|
||||
if func.get("name"):
|
||||
self.tool_calls_list[idx]["function"]["name"] += func["name"]
|
||||
if func.get("arguments"):
|
||||
self.tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
|
||||
|
||||
def emit_tool_calls(self) -> List[str]:
|
||||
"""Emit tool call steps, return SSE events."""
|
||||
events = []
|
||||
for tc in self.tool_calls_list:
|
||||
step_id = f"step-{self.step_index}"
|
||||
self.step_index += 1
|
||||
|
||||
step = {
|
||||
"id": step_id,
|
||||
"index": self.step_index - 1,
|
||||
"type": "tool_call",
|
||||
"id_ref": tc.get("id", ""),
|
||||
"name": tc["function"]["name"],
|
||||
"arguments": tc["function"]["arguments"]
|
||||
}
|
||||
self.all_steps.append(step)
|
||||
self.all_tool_calls.append(tc)
|
||||
events.append(_sse_event("process_step", {"step": step}))
|
||||
|
||||
return events
|
||||
|
||||
def emit_tool_result(self, result: Dict, ref_step_id: str) -> tuple:
|
||||
"""Emit tool result step, return (step, event)."""
|
||||
step_id = f"step-{self.step_index}"
|
||||
self.step_index += 1
|
||||
|
||||
content = result.get("content", "")
|
||||
success = True
|
||||
try:
|
||||
parsed = json.loads(content)
|
||||
if isinstance(parsed, dict):
|
||||
success = parsed.get("success", True)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
step = {
|
||||
"id": step_id,
|
||||
"index": self.step_index - 1,
|
||||
"type": "tool_result",
|
||||
"id_ref": ref_step_id,
|
||||
"name": result.get("name", ""),
|
||||
"content": content,
|
||||
"success": success
|
||||
}
|
||||
self.all_steps.append(step)
|
||||
self.all_tool_results.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": result.get("tool_call_id", ""),
|
||||
"content": content
|
||||
})
|
||||
|
||||
return step, _sse_event("process_step", {"step": step})
|
||||
|
||||
def set_completion(self, msg_id: str, token_count: int, usage: dict):
|
||||
"""Set completion info for saving."""
|
||||
self._last_message_id = msg_id
|
||||
self._last_token_count = token_count
|
||||
self._last_usage = usage
|
||||
|
||||
def reset_completion(self):
|
||||
"""Reset completion info."""
|
||||
self._last_message_id = None
|
||||
self._last_token_count = 0
|
||||
self._last_usage = None
|
||||
Loading…
Reference in New Issue