339 lines
12 KiB
Plaintext
339 lines
12 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 _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
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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=${lid}$ 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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# ==================== 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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task_id = (params_kw or {}).get('task_id', '')
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message_text = (params_kw or {}).get('message_text', '').strip()
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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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if not message_text:
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return json.dumps({"error": "message_text is required"}, ensure_ascii=False)
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# Parse file paths
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has_files = False
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try:
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fps = json.loads(file_paths_raw)
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has_files = len(fps) > 0
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except Exception:
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pass
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uid = await get_user()
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async with DBPools().sqlorContext(dbname) as sor:
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# 1. Save user message
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msg_id = getID()
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await sor.C('sd_conversations', {
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'id': msg_id,
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'iteration_id': iteration_id or '',
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'task_id': task_id or '',
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'step_name': '',
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'role': 'user',
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'content': message_text,
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'attachments': file_paths_raw,
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'msg_type': 'text',
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'org_id': '0',
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'created_by': uid
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})
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# 2. Load agent settings
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settings = await _load_agent_settings(sor, uid)
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# 3. Select model (preferred → auto by file type → first active)
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selected_llm_id = model_id or settings['llm_id']
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model_info = await _select_model(sor, selected_llm_id, has_files)
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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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# 4. Build context
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messages = await _build_context(
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sor, iteration_id, task_id,
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settings['max_context'], settings['system_prompt']
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)
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# Append current user message
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messages.append({"role": "user", "content": message_text})
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# 5. Call LLM
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try:
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agent_reply = await _call_llm(model_info, messages, settings['temperature'])
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except Exception as e:
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agent_reply = f"抱歉，模型调用失败: {str(e)[:200]}"
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# 6. Save agent response
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agent_msg_id = getID()
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await sor.C('sd_conversations', {
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'id': agent_msg_id,
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'iteration_id': iteration_id or '',
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'task_id': task_id or '',
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'step_name': '',
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'role': 'agent',
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'content': agent_reply,
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'attachments': '[]',
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'msg_type': 'text',
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'org_id': '0',
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'created_by': 'system'
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})
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return json.dumps({
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"success": True,
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"message_id": msg_id,
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"agent_reply": agent_reply,
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"model_used": model_info.name,
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}, ensure_ascii=False)
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else:
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# list_messages - return conversation as Bricks widget JSON
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iteration_id = (params_kw or {}).get('iteration_id', '')
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task_id = (params_kw or {}).get('task_id', '')
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msgs = []
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if iteration_id or task_id:
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async with DBPools().sqlorContext(dbname) as sor:
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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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sql = f"SELECT role, content, msg_type, created_at FROM sd_conversations WHERE {' OR '.join(where)} ORDER BY created_at ASC LIMIT 50"
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msgs = await sor.sqlExe(sql, params)
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msg_widgets = []
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for m in msgs:
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role = m.role if hasattr(m, 'role') else ''
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content = m.content if hasattr(m, 'content') else ''
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if role == 'agent':
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bg = '#e8f0fe'
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align = 'flex-start'
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label = 'Agent'
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label_color = '#3b82f6'
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elif role == 'user':
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bg = '#dbeafe'
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align = 'flex-end'
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label = '\u4f60'
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label_color = '#2563eb'
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else:
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bg = '#f1f5f9'
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align = 'center'
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label = '\u7cfb\u7edf'
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label_color = '#94a3b8'
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msg_widgets.append({
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"widgettype": "VBox",
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"options": {
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"width": "85%",
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"alignSelf": align,
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"bgcolor": bg,
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"borderRadius": "12px",
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"padding": "12px 16px",
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"marginBottom": "10px",
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"gap": "4px"
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},
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"subwidgets": [
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{"widgettype": "Text", "options": {
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"text": label, "cfontsize": 0.75,
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"color": label_color, "fontWeight": "bold"
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}},
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{"widgettype": "Text", "options": {
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"text": content, "cfontsize": 0.95,
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"color": "#1e293b", "whiteSpace": "pre-wrap"
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}}
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]
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})
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if not msg_widgets:
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msg_widgets.append({
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"widgettype": "Text",
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"options": {
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"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",
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"cfontsize": 0.9, "color": "#94a3b8", "padding": "20px"
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}
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})
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return {
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"widgettype": "VBox",
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"options": {"width": "100%", "padding": "4px"},
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"subwidgets": msg_widgets
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}
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