363 lines
16 KiB
Python
363 lines
16 KiB
Python
"""Chat completion service"""
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import json
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import uuid
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from flask import current_app, g, Response, request as flask_request
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from werkzeug.exceptions import ClientDisconnected
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from backend import db
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from backend.models import Conversation, Message
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from backend.tools import registry, ToolExecutor
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from backend.utils.helpers import (
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record_token_usage,
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build_messages,
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)
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from backend.services.llm_client import LLMClient
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from backend.config import MAX_ITERATIONS
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def _client_disconnected():
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"""Check if the client has disconnected."""
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try:
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stream = flask_request.input_stream
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# If input_stream is unavailable, assume still connected
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if stream is None:
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return False
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return stream.closed
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except Exception:
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return False
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class ChatService:
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"""Chat completion service with tool support"""
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def __init__(self, llm: LLMClient):
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self.llm = llm
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def stream_response(self, conv: Conversation, tools_enabled: bool = True, project_id: str = None):
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"""Stream response with tool call support
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Uses 'process_step' events to send thinking and tool calls in order,
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allowing them to be interleaved properly in the frontend.
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Args:
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conv: Conversation object
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tools_enabled: Whether to enable tools
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project_id: Project ID for workspace isolation
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"""
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conv_id = conv.id
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conv_model = conv.model
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app = current_app._get_current_object()
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tools = registry.list_all() if tools_enabled else None
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initial_messages = build_messages(conv, project_id)
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# Create per-request executor for thread-safe isolation.
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# Each request gets its own _call_history and _cache, eliminating
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# race conditions when multiple conversations stream concurrently.
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executor = ToolExecutor(registry=registry)
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# Build context for tool execution
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context = None
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if project_id:
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context = {"project_id": project_id}
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elif conv.project_id:
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context = {"project_id": conv.project_id}
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def generate():
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messages = list(initial_messages)
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all_tool_calls = []
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all_tool_results = []
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all_steps = [] # Collect all ordered steps for DB storage (thinking/text/tool_call/tool_result)
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step_index = 0 # Track global step index for ordering
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total_completion_tokens = 0 # Accumulated across all iterations
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prompt_tokens = 0 # Not accumulated — last iteration's value is sufficient
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# (each iteration re-sends the full context, so earlier
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# prompts are strict subsets of the final one)
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for iteration in range(MAX_ITERATIONS):
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full_content = ""
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full_thinking = ""
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token_count = 0
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msg_id = str(uuid.uuid4())
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tool_calls_list = []
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# Streaming step tracking — step ID is assigned on first chunk arrival.
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# thinking always precedes text in GLM's streaming order, so text gets step_index+1.
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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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with app.app_context():
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active_conv = db.session.get(Conversation, conv_id)
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resp = self.llm.call(
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model=active_conv.model,
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messages=messages,
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max_tokens=active_conv.max_tokens,
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temperature=active_conv.temperature,
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thinking_enabled=active_conv.thinking_enabled,
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tools=tools,
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stream=True,
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)
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resp.raise_for_status()
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# Stream LLM response chunk by chunk
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for line in resp.iter_lines():
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# Early exit if client has disconnected
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if _client_disconnected():
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resp.close()
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return
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if not line:
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continue
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line = line.decode("utf-8")
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if not line.startswith("data: "):
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continue
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data_str = line[6:]
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if data_str == "[DONE]":
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break
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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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continue
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# Extract usage first (present in last chunk when stream_options is set)
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usage = chunk.get("usage", {})
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if usage:
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token_count = usage.get("completion_tokens", 0)
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prompt_tokens = usage.get("prompt_tokens", 0)
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choices = chunk.get("choices", [])
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if not choices:
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continue
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delta = choices[0].get("delta", {})
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# Accumulate thinking content for this iteration
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reasoning = delta.get("reasoning_content", "")
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if reasoning:
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full_thinking += reasoning
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if thinking_step_id is None:
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thinking_step_id = f'step-{step_index}'
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thinking_step_idx = step_index
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yield f"event: process_step\ndata: {json.dumps({'id': thinking_step_id, 'index': thinking_step_idx, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n"
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# Accumulate text content for this iteration
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text = delta.get("content", "")
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if text:
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full_content += text
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if text_step_id is None:
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text_step_idx = step_index + (1 if thinking_step_id is not None else 0)
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text_step_id = f'step-{text_step_idx}'
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yield f"event: process_step\ndata: {json.dumps({'id': text_step_id, 'index': text_step_idx, 'type': 'text', 'content': full_content}, ensure_ascii=False)}\n\n"
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# Accumulate tool calls from streaming deltas
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tool_calls_list = self._process_tool_calls_delta(delta, tool_calls_list)
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except Exception as e:
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yield f"event: error\ndata: {json.dumps({'content': str(e)}, ensure_ascii=False)}\n\n"
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return
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# --- Finalize: save thinking/text steps to all_steps for DB storage ---
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# No need to yield to frontend — incremental process_step events already sent.
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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, 'index': thinking_step_idx,
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'type': 'thinking', 'content': full_thinking,
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})
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step_index += 1
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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, 'index': text_step_idx,
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'type': 'text', 'content': full_content,
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})
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step_index += 1
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# --- Branch: tool calls vs final ---
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if tool_calls_list:
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all_tool_calls.extend(tool_calls_list)
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# Execute each tool call, emit tool_call + tool_result as paired steps
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tool_results = []
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for tc in tool_calls_list:
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# Emit tool_call step (before execution)
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call_step = {
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'id': f'step-{step_index}',
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'index': step_index,
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'type': 'tool_call',
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'id_ref': tc['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 f"event: process_step\ndata: {json.dumps(call_step, ensure_ascii=False)}\n\n"
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step_index += 1
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# Execute the tool
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with app.app_context():
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single_result = executor.process_tool_calls([tc], context)
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tool_results.extend(single_result)
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# Emit tool_result step (after execution)
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tr = single_result[0]
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try:
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result_content = json.loads(tr["content"])
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skipped = result_content.get("skipped", False)
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except:
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skipped = False
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result_step = {
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'id': f'step-{step_index}',
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'index': step_index,
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'type': 'tool_result',
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'id_ref': tr['tool_call_id'],
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'name': tr['name'],
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'content': tr['content'],
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'skipped': skipped,
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}
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all_steps.append(result_step)
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yield f"event: process_step\ndata: {json.dumps(result_step, ensure_ascii=False)}\n\n"
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step_index += 1
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# Append assistant message + tool results for the next iteration
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messages.append({
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"role": "assistant",
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"content": full_content or None,
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"tool_calls": tool_calls_list
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})
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messages.extend(tool_results)
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all_tool_results.extend(tool_results)
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total_completion_tokens += token_count
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continue
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# --- No tool calls: final iteration — save message to DB ---
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suggested_title = None
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# prompt_tokens already holds the last iteration's value (set during streaming)
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total_completion_tokens += token_count
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with app.app_context():
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# Build content JSON with ordered steps array for DB storage.
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# 'steps' is the single source of truth for rendering order.
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content_json = {
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"text": full_content,
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}
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if all_tool_calls:
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content_json["tool_calls"] = self._build_tool_calls_json(all_tool_calls, all_tool_results)
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# Store ordered steps — the single source of truth for rendering order
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content_json["steps"] = all_steps
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msg = Message(
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id=msg_id,
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conversation_id=conv_id,
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role="assistant",
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content=json.dumps(content_json, ensure_ascii=False),
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token_count=total_completion_tokens,
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)
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db.session.add(msg)
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db.session.commit()
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# Auto-generate title from first user message if needed
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conv = db.session.get(Conversation, conv_id)
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# Record token usage (get user_id from conv, not g —
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# app.app_context() creates a new context where g.current_user is lost)
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if conv:
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record_token_usage(conv.user_id, conv_model, prompt_tokens, total_completion_tokens)
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if conv and (not conv.title or conv.title == "新对话"):
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user_msg = Message.query.filter_by(
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conversation_id=conv_id, role="user"
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).order_by(Message.created_at.asc()).first()
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if user_msg and user_msg.content:
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try:
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content_data = json.loads(user_msg.content)
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title_text = content_data.get("text", "")[:30]
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except (json.JSONDecodeError, TypeError):
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title_text = user_msg.content.strip()[:30]
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if title_text:
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suggested_title = title_text
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else:
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suggested_title = "新对话"
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db.session.refresh(conv)
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conv.title = suggested_title
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db.session.commit()
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else:
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suggested_title = None
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yield f"event: done\ndata: {json.dumps({'message_id': msg_id, 'token_count': total_completion_tokens, 'suggested_title': suggested_title}, ensure_ascii=False)}\n\n"
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return
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yield f"event: error\ndata: {json.dumps({'content': 'exceeded maximum tool call iterations'}, ensure_ascii=False)}\n\n"
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def safe_generate():
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"""Wrapper that catches client disconnection during yield."""
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try:
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yield from generate()
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except (ClientDisconnected, BrokenPipeError, ConnectionResetError):
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pass # Client aborted, silently stop
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return Response(
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safe_generate(),
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mimetype="text/event-stream",
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headers={
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"Cache-Control": "no-cache, no-store, must-revalidate",
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"X-Accel-Buffering": "no",
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"Connection": "keep-alive",
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"Transfer-Encoding": "chunked",
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}
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)
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def _build_tool_calls_json(self, tool_calls: list, tool_results: list) -> list:
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"""Build tool calls JSON structure - matches streaming format"""
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result = []
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for i, tc in enumerate(tool_calls):
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result_content = tool_results[i]["content"] if i < len(tool_results) else None
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# Parse result to extract success/skipped status
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success = True
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skipped = False
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execution_time = 0
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if result_content:
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try:
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result_data = json.loads(result_content)
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success = result_data.get("success", True)
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skipped = result_data.get("skipped", False)
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execution_time = result_data.get("execution_time", 0)
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except:
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pass
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# Keep same structure as streaming format
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result.append({
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"id": tc.get("id", ""),
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"type": tc.get("type", "function"),
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"function": {
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"],
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},
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"result": result_content,
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"success": success,
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"skipped": skipped,
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"execution_time": execution_time,
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})
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return result
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def _process_tool_calls_delta(self, delta: dict, tool_calls_list: list) -> list:
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"""Process tool calls from streaming delta"""
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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": tc.get("type", "function"),
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"function": {"name": "", "arguments": ""}
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})
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if tc.get("id"):
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tool_calls_list[idx]["id"] = tc["id"]
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if tc.get("function"):
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if tc["function"].get("name"):
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tool_calls_list[idx]["function"]["name"] = tc["function"]["name"]
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if tc["function"].get("arguments"):
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tool_calls_list[idx]["function"]["arguments"] += tc["function"]["arguments"]
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return tool_calls_list
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