503 lines
21 KiB
Plaintext
503 lines
21 KiB
Plaintext
# cockpit_chat.dspy - Product-grade LLM conversation with context
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# POST: action=send_message, iteration_id, message_text, model_id, file_paths
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# GET: action=list_messages, iteration_id, task_id
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import aiohttp
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action = (params_kw or {}).get('action', 'list_messages')
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dbname = get_module_dbname('pipeline-sdlc')
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DEFAULT_SYSTEM_PROMPT = """你是一个专业的软件开发 Agent，名为「开发产线驾驶舱」。你的职责是帮助用户完成软件开发生命周期的各个环节：需求分析、设计、编码、测试、部署。
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对话规则：
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1. 简洁专业，用中文回复
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2. 当用户描述需求时，先理解并复述确认，然后给出分析和建议
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3. 如果用户提到了项目/迭代，主动关联上下文
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4. 可以建议启动开发产线来推进工作
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5. 对于代码相关问题，给出具体的代码示例
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6. 记住对话历史，保持上下文连贯
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当前你可以帮助用户完成：
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- 创建和管理项目、迭代
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- 数据表设计、CRUD 设计、API 设计
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- 代码生成、规范检查、自动修复
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- 测试用例生成、功能测试、Bug 管理
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- 环境部署和验证"""
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async def _load_agent_settings(sor, uid):
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"""Load user's agent settings, return defaults if not set."""
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recs = await sor.sqlExe(
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"SELECT default_llm_id, system_prompt, temperature, max_context_messages FROM sd_agent_settings WHERE user_id=${uid}$",
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{"uid": uid}
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)
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if recs:
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r = recs[0]
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return {
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'llm_id': getattr(r, 'default_llm_id', None),
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'system_prompt': getattr(r, 'system_prompt', None) or DEFAULT_SYSTEM_PROMPT,
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'temperature': float(getattr(r, 'temperature', 0.7) or 0.7),
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'max_context': int(getattr(r, 'max_context_messages', 30) or 30),
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}
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return {
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'llm_id': None,
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'system_prompt': DEFAULT_SYSTEM_PROMPT,
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'temperature': 0.7,
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'max_context': 30,
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}
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async def _load_context(sor, uid):
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"""Load session context: current project/iteration."""
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settings = await _load_agent_settings(sor, uid)
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recs = await sor.sqlExe(
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"SELECT current_project_id, current_iteration_id FROM sd_agent_settings WHERE user_id=${uid}$",
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{"uid": uid}
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)
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ctx = {'project_id': '', 'iteration_id': '', 'project_name': '', 'iteration_name': ''}
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if recs:
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r = recs[0]
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ctx['project_id'] = getattr(r, 'current_project_id', '') or ''
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ctx['iteration_id'] = getattr(r, 'current_iteration_id', '') or ''
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if ctx['project_id']:
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projs = await sor.sqlExe(
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"SELECT name FROM sd_projects WHERE id=${pid}$", {"pid": ctx['project_id']}
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)
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if projs:
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ctx['project_name'] = getattr(projs[0], 'name', '')
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if ctx['iteration_id']:
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iters = await sor.sqlExe(
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"SELECT iteration_name FROM sd_iterations WHERE id=${iid}$", {"iid": ctx['iteration_id']}
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)
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if iters:
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ctx['iteration_name'] = getattr(iters[0], 'iteration_name', '')
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return ctx
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async def _save_context(sor, uid, project_id, iteration_id):
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"""Save session context."""
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existing = await sor.sqlExe(
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"SELECT id FROM sd_agent_settings WHERE user_id=${uid}$", {"uid": uid}
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)
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if existing:
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await sor.sqlExe(
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"UPDATE sd_agent_settings SET current_project_id=${pid}$, current_iteration_id=${iid}$ WHERE user_id=${uid}$",
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{"pid": project_id or '', "iid": iteration_id or '', "uid": uid}
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)
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else:
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await sor.C('sd_agent_settings', {
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'id': getID(), 'user_id': uid,
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'current_project_id': project_id or '',
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'current_iteration_id': iteration_id or '',
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})
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async def _select_model(sor, preferred_llm_id, has_files):
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"""Select best model: prefer user choice, then multimodal if files, else first active text."""
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# If user has preferred model, use it (match by id or name)
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if preferred_llm_id:
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recs = await sor.sqlExe(
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"SELECT id, name, provider, model_id, api_base, api_key, capabilities FROM llm WHERE (id=${lid}$ OR name=${lid}$) AND status='active'",
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{"lid": preferred_llm_id}
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)
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if recs:
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return recs[0]
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# Auto-select based on file presence
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if has_files:
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recs = await sor.sqlExe(
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"SELECT id, name, provider, model_id, api_base, api_key, capabilities FROM llm WHERE status='active' AND capabilities LIKE '%multimodal%' LIMIT 1",
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{}
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)
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if recs:
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return recs[0]
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# Fallback: first active model
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recs = await sor.sqlExe(
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"SELECT id, name, provider, model_id, api_base, api_key, capabilities FROM llm WHERE status='active' LIMIT 1",
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{}
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)
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if recs:
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return recs[0]
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return None
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async def _build_context(sor, iteration_id, task_id, max_msgs, system_prompt):
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"""Build LLM messages array with Hermes-style context."""
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messages = [{"role": "system", "content": system_prompt}]
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# Project/iteration context
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if iteration_id:
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iters = await sor.sqlExe(
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"SELECT i.iteration_name, i.iteration_type, i.status, i.scope, p.name as project_name, p.description, p.tech_stack "
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"FROM sd_iterations i LEFT JOIN sd_projects p ON i.project_id=p.id WHERE i.id=${iid}$",
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{"iid": iteration_id}
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)
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if iters:
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it = iters[0]
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ctx_parts = ["## 当前上下文"]
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ctx_parts.append(f"项目: {getattr(it, 'project_name', '未知')}")
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ctx_parts.append(f"迭代: {getattr(it, 'iteration_name', '未知')}")
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ctx_parts.append(f"类型: {getattr(it, 'iteration_type', '')}")
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ctx_parts.append(f"状态: {getattr(it, 'status', '')}")
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desc = getattr(it, 'description', '')
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if desc:
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ctx_parts.append(f"项目描述: {desc[:500]}")
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stack = getattr(it, 'tech_stack', '')
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if stack:
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ctx_parts.append(f"技术栈: {stack[:300]}")
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scope = getattr(it, 'scope', '')
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if scope:
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ctx_parts.append(f"迭代范围: {scope[:500]}")
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messages.append({"role": "system", "content": "\n".join(ctx_parts)})
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# Task context
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if task_id:
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tasks = await sor.sqlExe(
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"SELECT id, status, pipeline_id FROM pipeline_tasks WHERE id=${tid}$",
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{"tid": task_id}
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)
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if tasks:
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t = tasks[0]
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messages.append({"role": "system", "content": f"关联 Pipeline 任务: {t.id}, 状态: {getattr(t, 'status', 'unknown')}"})
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# Conversation history
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where = []
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params = {}
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if task_id:
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where.append("task_id=${tid}$")
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params["tid"] = task_id
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if iteration_id:
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where.append("iteration_id=${iid}$")
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params["iid"] = iteration_id
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if where:
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sql = f"SELECT role, content FROM sd_conversations WHERE {' OR '.join(where)} ORDER BY created_at DESC LIMIT ${max_msgs}$"
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params["max_msgs"] = max_msgs
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history = await sor.sqlExe(sql, params)
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# Reverse to chronological order
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for h in reversed(history):
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role = getattr(h, 'role', 'user')
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content = getattr(h, 'content', '')
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if role in ('user', 'agent'):
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messages.append({"role": "user" if role == "user" else "assistant", "content": content})
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return messages
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async def _call_llm(model_info, messages, temperature):
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"""Call LLM API directly using model config from llm table."""
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api_base = model_info.api_base.rstrip('/')
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api_key = model_info.api_key or ''
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model_id = model_info.model_id
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debug(f'_call_llm: base={api_base} model={model_id} key_len={len(api_key)} key_prefix={api_key[:10]}')
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": model_id,
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"messages": messages,
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"temperature": temperature,
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}
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timeout = aiohttp.ClientTimeout(total=120)
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async with aiohttp.ClientSession(timeout=timeout) as session:
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url = f"{api_base}/chat/completions"
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debug(f'_call_llm: POST {url}')
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async with session.post(url, headers=headers, json=payload) as resp:
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if resp.status != 200:
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text = await resp.text()
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debug(f'_call_llm: FAIL status={resp.status} body={text[:200]}')
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raise ValueError(f"LLM API error {resp.status}: {text[:300]}")
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data = await resp.json()
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content = data["choices"][0]["message"]["content"]
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debug(f'_call_llm: OK reply_len={len(content)}')
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if len(content) > 8000:
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content = content[:8000] + "\n\n...(内容过长已截断)"
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return content
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INTENT_PROMPT = """你是一个开发产线意图分类器。分析用户输入，返回 JSON。
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意图类型：
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- new_project: 创建新项目
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- select_project: 切换到已有项目
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- new_iteration: 在当前项目下创建新迭代
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- new_task: 提交开发任务（需关联项目/迭代）
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- add_bug: 报告Bug
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- query: 查询当前状态
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- chat: 开发相关的一般对话
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- out_of_scope: 完全无关软件开发
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当前上下文：项目={ctx}，迭代={iter}
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返回纯JSON（不要markdown包裹）：
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{"intent":"...","confidence":0.8,"project_name":"...","iteration_name":"...","title":"...","description":"...","missing_info":"..."}"""
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async def _classify_intent(model_info, message, ctx, history_msgs):
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"""Classify user intent using LLM."""
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ctx_str = ctx.get('project_name', '') or '无'
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iter_str = ctx.get('iteration_name', '') or '无'
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prompt = INTENT_PROMPT.replace('{ctx}', ctx_str).replace('{iter}', iter_str)
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msgs = [{"role": "system", "content": prompt}]
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for h in history_msgs[-4:]:
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msgs.append(h)
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msgs.append({"role": "user", "content": message})
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raw = await _call_llm(model_info, msgs, 0.2)
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raw = raw.strip()
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if raw.startswith('```'):
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raw = raw.split('\n', 1)[1].rsplit('```', 1)[0]
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try:
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return json.loads(raw)
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except Exception:
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return {"intent": "chat", "confidence": 0.5, "missing_info": ""}
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async def _find_project(sor, name, org_id):
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"""Find project by name."""
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if not name: return None
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recs = await sor.sqlExe(
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"SELECT id, name FROM sd_projects WHERE name=${name}$ AND org_id=${oid}$",
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{"name": name, "oid": org_id})
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return recs[0] if recs else None
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SCOPE_GUIDE = """我可以帮你：
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📁 项目管理 — "创建电商平台项目" / "切换到XXX项目"
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🔄 迭代管理 — "创建Sprint3" / "查看迭代进度"
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📝 提交任务 — "设计用户表结构" / "实现登录API"
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🐛 Bug管理 — "登录页报500" / "我的Bug列表"
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📊 查询 — "当前项目进度" / "有哪些迭代"
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请描述你的需求。"""
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# ==================== ACTION HANDLERS ====================
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if action == 'send_message':
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iteration_id = (params_kw or {}).get('iteration_id', '')
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message_text = (params_kw or {}).get('message_text', '').strip()
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user_model_id = (params_kw or {}).get('model_id', '')
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file_paths_raw = (params_kw or {}).get('file_paths', '[]')
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debug(f'send_message: model_id={user_model_id} iteration_id={iteration_id} msg_len={len(message_text)}')
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if not message_text:
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return json.dumps({"error": "message_text is required"}, ensure_ascii=False)
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uid = await get_user()
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org_id = await get_userorgid() or '0'
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async with DBPools().sqlorContext(dbname) as sor:
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# 1. Load context and model
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ctx = await _load_context(sor, uid)
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settings = await _load_agent_settings(sor, uid)
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selected_llm_id = user_model_id or settings['llm_id']
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model_info = await _select_model(sor, selected_llm_id, False)
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if not model_info:
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return json.dumps({"error": "No active LLM model configured"}, ensure_ascii=False)
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# 2. Classify intent
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history = await sor.sqlExe(
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"SELECT role, content FROM sd_conversations WHERE iteration_id=${iid}$ OR iteration_id='' ORDER BY created_at DESC LIMIT 4",
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{"iid": iteration_id or ctx.get('iteration_id', '')}
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)
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history_msgs = []
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for h in reversed(history):
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role = 'user' if getattr(h, 'role', '') == 'user' else 'assistant'
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history_msgs.append({"role": role, "content": getattr(h, 'content', '')})
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intent = await _classify_intent(model_info, message_text, ctx, history_msgs)
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debug(f'intent: {intent}')
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# 3. Route by intent
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intent_type = intent.get('intent', 'chat')
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confidence = intent.get('confidence', 0.5)
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if intent_type == 'out_of_scope' or (intent_type == 'chat' and confidence < 0.6 and not ctx['project_id']):
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agent_reply = SCOPE_GUIDE
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elif intent.get('missing_info') and confidence < 0.7:
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agent_reply = f"让我确认一下：{intent.get('missing_info', '请提供更多信息')}"
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elif intent_type == 'new_project':
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pname = intent.get('project_name', '') or message_text[:50]
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proj = await _find_project(sor, pname, org_id)
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if proj:
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agent_reply = f"项目「{pname}」已存在。已切换到该项目。"
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await _save_context(sor, uid, proj.id, '')
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else:
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pid = getID()
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await sor.C('sd_projects', {
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'id': pid, 'name': pname, 'description': intent.get('description', ''),
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'project_type': 'software', 'org_id': org_id, 'created_by': uid, 'status': 'active'
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})
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# Auto-create default iteration
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iid = getID()
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await sor.C('sd_iterations', {
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'id': iid, 'project_id': pid, 'iteration_name': '默认迭代',
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'iteration_type': 'sprint', 'org_id': org_id, 'created_by': uid, 'status': 'active'
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})
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await _save_context(sor, uid, pid, iid)
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agent_reply = f"✅ 项目「{pname}」已创建，默认迭代已就绪。现在可以提交任务了。"
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elif intent_type == 'select_project':
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pname = intent.get('project_name', '')
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proj = await _find_project(sor, pname, org_id)
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if proj:
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await _save_context(sor, uid, proj.id, ctx['iteration_id'])
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agent_reply = f"已切换到项目「{pname}」。"
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else:
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agent_reply = f"未找到项目「{pname}」。请先创建项目后再切换。"
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elif intent_type == 'new_task':
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pid = ctx['project_id']
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iid = intent.get('iteration_name', '') or ctx['iteration_id']
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if not pid:
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agent_reply = "请先指定项目。「创建XXX项目」或「切换到XXX项目」"
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elif not iid:
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agent_reply = "请指定迭代。「创建Sprint1」或「切换到XXX迭代」"
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else:
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title = intent.get('title', '') or message_text[:100]
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task_params = {'description': intent.get('description', ''), 'input_text': message_text}
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try:
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result = await pipeline_submit(org_id, 'sdlc_general', uid, title, task_params)
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rd = json.loads(result)
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if rd.get('success'):
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task_id = rd.get('task_id', '')
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await _save_context(sor, uid, pid, iid)
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agent_reply = f"✅ 任务「{title}」已提交（{task_id}），产线开始执行。"
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else:
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agent_reply = f"任务提交失败：{rd.get('message', '未知错误')}"
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except Exception as e:
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agent_reply = f"任务提交失败：{str(e)[:200]}"
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elif intent_type == 'add_bug':
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pid = ctx['project_id']
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iid = ctx['iteration_id']
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if not pid:
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agent_reply = "请先指定项目后再报告Bug。"
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else:
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bid = getID()
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await sor.C('sd_bugs', {
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'id': bid, 'iteration_id': iid or '', 'title': intent.get('title', '') or message_text[:100],
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'description': intent.get('description', ''), 'severity': 'major', 'priority': 'P1',
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'status': 'open', 'reporter_type': 'human', 'reporter_id': uid, 'created_at': curDateString()
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})
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agent_reply = f"🐛 Bug已记录（{bid}）：{message_text[:100]}"
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elif intent_type == 'query':
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ctx_info = []
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if ctx['project_name']:
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ctx_info.append(f"当前项目：{ctx['project_name']}")
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if ctx['iteration_name']:
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ctx_info.append(f"当前迭代：{ctx['iteration_name']}")
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if ctx_info:
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agent_reply = '\n'.join(ctx_info) + '\n\n请描述具体想查询什么（如：任务列表、Bug列表等）'
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else:
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agent_reply = "当前未选择项目。请先「创建XXX项目」或「切换到XXX项目」。"
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else:
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# chat: general conversation
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messages = await _build_context(sor, iteration_id or ctx['iteration_id'], '',
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settings['max_context'], settings['system_prompt'])
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messages.append({"role": "user", "content": message_text})
|
||
try:
|
||
agent_reply = await _call_llm(model_info, messages, settings['temperature'])
|
||
except Exception as e:
|
||
agent_reply = f"抱歉，模型调用失败: {str(e)[:200]}"
|
||
|
||
# 4. Save conversation
|
||
msg_id = getID()
|
||
await sor.C('sd_conversations', {
|
||
'id': msg_id, 'iteration_id': iteration_id or ctx.get('iteration_id', ''),
|
||
'task_id': '', 'step_name': '', 'role': 'user', 'content': message_text,
|
||
'attachments': file_paths_raw, 'msg_type': 'text', 'org_id': org_id, 'created_by': uid
|
||
})
|
||
agent_msg_id = getID()
|
||
await sor.C('sd_conversations', {
|
||
'id': agent_msg_id, 'iteration_id': iteration_id or ctx.get('iteration_id', ''),
|
||
'task_id': '', 'step_name': '', 'role': 'agent', 'content': agent_reply,
|
||
'attachments': '[]', 'msg_type': 'text', 'org_id': org_id, 'created_by': 'system'
|
||
})
|
||
|
||
return json.dumps({
|
||
"success": True, "message_id": msg_id, "agent_reply": agent_reply,
|
||
"model_used": model_info.name, "intent": intent_type,
|
||
"context": {"project_name": ctx.get('project_name', ''), "iteration_name": ctx.get('iteration_name', '')}
|
||
}, ensure_ascii=False)
|
||
|
||
|
||
else:
|
||
# list_messages - return conversation as Bricks widget JSON
|
||
iteration_id = (params_kw or {}).get('iteration_id', '')
|
||
task_id = (params_kw or {}).get('task_id', '')
|
||
|
||
msgs = []
|
||
if iteration_id or task_id:
|
||
async with DBPools().sqlorContext(dbname) as sor:
|
||
where = []
|
||
params = {}
|
||
if task_id:
|
||
where.append("task_id=${tid}$")
|
||
params["tid"] = task_id
|
||
if iteration_id:
|
||
where.append("iteration_id=${iid}$")
|
||
params["iid"] = iteration_id
|
||
|
||
sql = f"SELECT role, content, msg_type, created_at FROM sd_conversations WHERE {' OR '.join(where)} ORDER BY created_at ASC LIMIT 50"
|
||
msgs = await sor.sqlExe(sql, params)
|
||
|
||
msg_widgets = []
|
||
for m in msgs:
|
||
role = m.role if hasattr(m, 'role') else ''
|
||
content = m.content if hasattr(m, 'content') else ''
|
||
|
||
if role == 'agent':
|
||
bg = '#e8f0fe'
|
||
align = 'flex-start'
|
||
label = 'Agent'
|
||
label_color = '#3b82f6'
|
||
elif role == 'user':
|
||
bg = '#dbeafe'
|
||
align = 'flex-end'
|
||
label = '\u4f60'
|
||
label_color = '#2563eb'
|
||
else:
|
||
bg = '#f1f5f9'
|
||
align = 'center'
|
||
label = '\u7cfb\u7edf'
|
||
label_color = '#94a3b8'
|
||
|
||
msg_widgets.append({
|
||
"widgettype": "VBox",
|
||
"options": {
|
||
"width": "85%",
|
||
"alignSelf": align,
|
||
"bgcolor": bg,
|
||
"borderRadius": "12px",
|
||
"padding": "12px 16px",
|
||
"marginBottom": "10px",
|
||
"gap": "4px"
|
||
},
|
||
"subwidgets": [
|
||
{"widgettype": "Text", "options": {
|
||
"text": label, "cfontsize": 0.75,
|
||
"color": label_color, "fontWeight": "bold"
|
||
}},
|
||
{"widgettype": "Text", "options": {
|
||
"text": content, "cfontsize": 0.95,
|
||
"color": "#1e293b", "whiteSpace": "pre-wrap"
|
||
}}
|
||
]
|
||
})
|
||
|
||
if not msg_widgets:
|
||
msg_widgets.append({
|
||
"widgettype": "Text",
|
||
"options": {
|
||
"text": "\u6682\u65e0\u5bf9\u8bdd\u8bb0\u5f55\u3002\u9009\u62e9\u4e00\u4e2a\u8fed\u4ee3\u540e\uff0c\u5728\u4e0b\u65b9\u8f93\u5165\u6846\u4e2d\u5f00\u59cb\u5bf9\u8bdd\u3002",
|
||
"cfontsize": 0.9, "color": "#94a3b8", "padding": "20px"
|
||
}
|
||
})
|
||
|
||
return {
|
||
"widgettype": "VBox",
|
||
"options": {"width": "100%", "padding": "4px"},
|
||
"subwidgets": msg_widgets
|
||
}
|