353 lines
15 KiB
Python
353 lines
15 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, Response
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from backend import db
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from backend.models import Conversation, Message, ToolCall
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from backend.tools import registry, ToolExecutor
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from backend.utils.helpers import (
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get_or_create_default_user,
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record_token_usage,
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build_glm_messages,
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ok,
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err,
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to_dict,
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)
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from backend.services.glm_client import GLMClient
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class ChatService:
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"""Chat completion service with tool support"""
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MAX_ITERATIONS = 5
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def __init__(self, glm_client: GLMClient):
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self.glm_client = glm_client
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self.executor = ToolExecutor(registry=registry)
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def sync_response(self, conv: Conversation, tools_enabled: bool = True):
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"""Sync response with tool call support"""
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tools = registry.list_all() if tools_enabled else None
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messages = build_glm_messages(conv)
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# Clear tool call history for new request
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self.executor.clear_history()
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all_tool_calls = []
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all_tool_results = []
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for _ in range(self.MAX_ITERATIONS):
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try:
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resp = self.glm_client.call(
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model=conv.model,
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messages=messages,
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max_tokens=conv.max_tokens,
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temperature=conv.temperature,
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thinking_enabled=conv.thinking_enabled,
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tools=tools,
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)
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resp.raise_for_status()
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result = resp.json()
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except Exception as e:
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return err(500, f"upstream error: {e}")
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choice = result["choices"][0]
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message = choice["message"]
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# No tool calls - return final result
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if not message.get("tool_calls"):
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usage = result.get("usage", {})
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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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# Create message
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msg = Message(
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id=str(uuid.uuid4()),
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conversation_id=conv.id,
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role="assistant",
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content=message.get("content", ""),
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token_count=completion_tokens,
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thinking_content=message.get("reasoning_content", ""),
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)
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db.session.add(msg)
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# Create tool call records
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self._save_tool_calls(msg.id, all_tool_calls, all_tool_results)
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db.session.commit()
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user = get_or_create_default_user()
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record_token_usage(user.id, conv.model, prompt_tokens, completion_tokens)
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return ok({
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"message": self._message_to_dict(msg),
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"usage": {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": usage.get("total_tokens", 0)
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},
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})
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# Process tool calls
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tool_calls = message["tool_calls"]
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all_tool_calls.extend(tool_calls)
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messages.append(message)
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tool_results = self.executor.process_tool_calls(tool_calls)
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all_tool_results.extend(tool_results)
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messages.extend(tool_results)
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return err(500, "exceeded maximum tool call iterations")
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def stream_response(self, conv: Conversation, tools_enabled: bool = True):
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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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"""
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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_glm_messages(conv)
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# Clear tool call history for new request
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self.executor.clear_history()
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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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step_index = 0 # Track global step index for ordering
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for iteration in range(self.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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prompt_tokens = 0
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msg_id = str(uuid.uuid4())
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tool_calls_list = []
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# Send thinking_start event to clear previous thinking in frontend
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yield f"event: thinking_start\ndata: {{}}\n\n"
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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.glm_client.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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for line in resp.iter_lines():
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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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delta = chunk["choices"][0].get("delta", {})
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# Process thinking - send as process_step
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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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# Still send thinking event for backward compatibility
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yield f"event: thinking\ndata: {json.dumps({'content': reasoning}, ensure_ascii=False)}\n\n"
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# Process text
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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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yield f"event: message\ndata: {json.dumps({'content': text}, ensure_ascii=False)}\n\n"
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# Process tool calls
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tool_calls_list = self._process_tool_calls_delta(delta, tool_calls_list)
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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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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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# Tool calls exist - execute and continue
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if tool_calls_list:
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all_tool_calls.extend(tool_calls_list)
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# Send thinking as a complete step if exists
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if full_thinking:
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yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n"
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step_index += 1
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# Also send legacy tool_calls event for backward compatibility
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yield f"event: tool_calls\ndata: {json.dumps({'calls': tool_calls_list}, ensure_ascii=False)}\n\n"
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# Process each tool call one by one, send result immediately
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tool_results = []
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for tc in tool_calls_list:
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# Send tool call step
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yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'tool_call', 'id': tc['id'], 'name': tc['function']['name'], 'arguments': tc['function']['arguments']}, ensure_ascii=False)}\n\n"
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step_index += 1
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# Execute this single tool call
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single_result = self.executor.process_tool_calls([tc])
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tool_results.extend(single_result)
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# Send tool result step immediately
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tr = single_result[0]
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try:
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result_data = json.loads(tr["content"])
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skipped = result_data.get("skipped", False)
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except:
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skipped = False
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yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'tool_result', 'id': tr['tool_call_id'], 'name': tr['name'], 'content': tr['content'], 'skipped': skipped}, ensure_ascii=False)}\n\n"
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step_index += 1
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# Also send legacy tool_result event
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yield f"event: tool_result\ndata: {json.dumps({'id': tr['tool_call_id'], 'name': tr['name'], 'content': tr['content'], 'skipped': skipped}, ensure_ascii=False)}\n\n"
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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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continue
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# No tool calls - finish
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# Send thinking as a step if exists
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if full_thinking:
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yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n"
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step_index += 1
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with app.app_context():
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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=full_content,
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token_count=token_count,
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thinking_content=full_thinking,
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)
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db.session.add(msg)
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# Create tool call records
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self._save_tool_calls(msg_id, all_tool_calls, all_tool_results)
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db.session.commit()
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user = get_or_create_default_user()
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record_token_usage(user.id, conv_model, prompt_tokens, token_count)
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yield f"event: done\ndata: {json.dumps({'message_id': msg_id, 'token_count': token_count})}\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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return Response(
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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 _save_tool_calls(self, message_id: str, tool_calls: list, tool_results: list) -> None:
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"""Save tool calls to database"""
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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 execution_time if present
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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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execution_time = result_data.get("execution_time", 0)
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except:
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pass
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tool_call = ToolCall(
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message_id=message_id,
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call_id=tc.get("id", ""),
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call_index=i,
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tool_name=tc["function"]["name"],
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arguments=tc["function"]["arguments"],
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result=result_content,
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execution_time=execution_time,
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)
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db.session.add(tool_call)
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def _message_to_dict(self, msg: Message) -> dict:
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"""Convert message to dict with tool calls"""
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result = to_dict(msg, thinking_content=msg.thinking_content or None)
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# Add tool calls if any
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tool_calls = msg.tool_calls.all() if msg.tool_calls else []
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if tool_calls:
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result["tool_calls"] = []
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for tc in tool_calls:
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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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if tc.result:
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try:
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result_data = json.loads(tc.result)
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success = result_data.get("success", True)
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skipped = result_data.get("skipped", False)
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except:
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pass
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result["tool_calls"].append({
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"id": tc.call_id,
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"type": "function",
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"function": {
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"name": tc.tool_name,
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"arguments": tc.arguments,
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},
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"result": tc.result,
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"success": success,
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"skipped": skipped,
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"execution_time": tc.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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