Glevel
7804122b7e
- Add solve_task.py: fast direct solver (1 LLM call per task, no deepagents overhead) - Add RetryOnRateLimitMiddleware: auto-retry on 429 from any tool - Fix double MCP load: __init__.py cleared, pipeline reuses agent.py journal tools - Fix proxy: add NO_PROXY for openrouter.ai, platform.brojs.ru, git.brojs.ru - Add utility scripts: get_task_ids.py, read_tasks.py - Update run_pipeline.py: TARGET_IDS support, unbuffered output Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
361 lines
13 KiB
Python
361 lines
13 KiB
Python
"""
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Быстрый решатель заданий BroJS.
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Схема: читаем задание (MCP) → 1 LLM-вызов → пушим на Gitea → сабмитим (MCP).
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Автономный — не импортирует src.agent, нет двойной загрузки MCP.
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Использование:
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python solve_task.py <full_task_id>
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"""
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import asyncio
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import base64
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import json
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import os
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import sys
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# Обходим локальный прокси
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os.environ["NO_PROXY"] = "openrouter.ai,platform.brojs.ru,git.brojs.ru," + os.environ.get("NO_PROXY", "")
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from dotenv import load_dotenv
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load_dotenv()
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import httpx
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain_openai import ChatOpenAI
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GITEA_BASE_URL = "https://git.brojs.ru"
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GITEA_OWNER = os.getenv("GITEA_OWNER", "glevelll")
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GITEA_TOKEN = os.getenv("GITEA_TOKEN", "")
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JOURNAL_TOKEN = os.getenv("JOURNAL_TOKEN", "")
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
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MCP_URL = "https://platform.brojs.ru/jrnl-bh/api/mcp"
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# ---------------------------------------------------------------------------
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# LLM
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# ---------------------------------------------------------------------------
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=OPENAI_API_KEY,
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temperature=0.0,
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max_tokens=4096,
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)
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# ---------------------------------------------------------------------------
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# MCP — один клиент на весь запуск
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# ---------------------------------------------------------------------------
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_mcp_tools: dict = {}
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async def _load_mcp(retries=5, pause=30):
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global _mcp_tools
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if _mcp_tools:
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return
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config = {
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"journal": {
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"transport": "streamable_http",
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"url": MCP_URL,
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"headers": {"Authorization": f"Bearer {JOURNAL_TOKEN}"},
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}
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}
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client = MultiServerMCPClient(config)
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for attempt in range(1, retries + 1):
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try:
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tools = await client.get_tools(server_name="journal")
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_mcp_tools = {t.name: t for t in tools}
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print(f" [mcp] Загружено {len(_mcp_tools)} инструментов")
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return
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except Exception as e:
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if "429" in str(e) and attempt < retries:
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print(f" [mcp] 429 при загрузке, жду {pause}с...")
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await asyncio.sleep(pause)
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else:
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raise
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async def mcp_call(name: str, args: dict, retries=5, pause=30):
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await _load_mcp()
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tool = _mcp_tools.get(name)
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if not tool:
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raise RuntimeError(f"MCP tool '{name}' not found. Available: {list(_mcp_tools.keys())}")
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for attempt in range(1, retries + 1):
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try:
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result = await tool.ainvoke(args)
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if isinstance(result, list):
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return next((x["text"] for x in result if x.get("type") == "text"), str(result))
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return str(result)
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except Exception as e:
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if "429" in str(e) and attempt < retries:
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print(f" [mcp] {name} → 429, жду {pause}с (попытка {attempt}/{retries})...")
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await asyncio.sleep(pause)
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else:
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raise
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# ---------------------------------------------------------------------------
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# Gitea
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# ---------------------------------------------------------------------------
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def _gh():
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return {"Authorization": f"token {GITEA_TOKEN}", "Content-Type": "application/json"}
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def gitea_create_repo(name: str) -> str:
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with httpx.Client(timeout=30) as c:
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r = c.post(f"{GITEA_BASE_URL}/api/v1/user/repos", headers=_gh(),
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json={"name": name, "private": False, "auto_init": False})
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if r.status_code == 409:
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return f"{GITEA_BASE_URL}/{GITEA_OWNER}/{name}"
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r.raise_for_status()
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return r.json().get("html_url", f"{GITEA_BASE_URL}/{GITEA_OWNER}/{name}")
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def gitea_write(repo: str, path: str, content: str, msg: str):
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encoded = base64.b64encode(content.encode()).decode()
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url = f"{GITEA_BASE_URL}/api/v1/repos/{GITEA_OWNER}/{repo}/contents/{path}"
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with httpx.Client(timeout=30) as c:
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r = c.get(url, headers=_gh())
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if r.status_code == 200:
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sha = r.json().get("sha", "")
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c.put(url, headers=_gh(), json={"message": msg, "content": encoded, "sha": sha}).raise_for_status()
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else:
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c.post(url, headers=_gh(), json={"message": msg, "content": encoded}).raise_for_status()
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# ---------------------------------------------------------------------------
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# LLM: генерация кода
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# ---------------------------------------------------------------------------
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_PROMPT = '''\
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Ты — Python-разработчик. Напиши решение для учебного задания по LLM/AI.
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Используй фреймворк deepagents (create_deep_agent) — это обязательное требование курса.
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## Задание
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{task_text}
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## ОБЯЗАТЕЛЬНЫЕ ТЕХНИЧЕСКИЕ ПАТТЕРНЫ
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### LLM — всегда OpenRouter:
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```python
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import os
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from langchain_openai import ChatOpenAI
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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```
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### Базовый агент (deepagents) — ОБЯЗАТЕЛЬНАЯ основа:
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```python
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import asyncio, os
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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llm = ChatOpenAI(model="openai/gpt-oss-20b:free", base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPENAI_API_KEY"))
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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@tool
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def my_tool(query: str) -> str:
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"""Tool description."""
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return f"result for {{query}}"
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agent = create_deep_agent(
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model=llm,
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tools=[my_tool],
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backend=backend,
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system_prompt="You are a helpful agent.",
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)
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async def main():
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result = await agent.ainvoke(
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{{"messages": [HumanMessage(content="Your task here")]}},
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{{"configurable": {{"thread_id": "session-1"}}}},
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)
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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requirements.txt: deepagents, langchain-openai>=0.3.0, langchain>=1.2.10, langgraph>=0.2.0
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### RAG с Qdrant (для RAG-заданий):
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```python
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from langchain_openai import OpenAIEmbeddings
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from langchain_qdrant import QdrantVectorStore
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from qdrant_client import QdrantClient
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from qdrant_client.models import Distance, VectorParams
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from langchain_core.documents import Document
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embeddings = OpenAIEmbeddings(model="text-embedding-3-small", base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPENAI_API_KEY"))
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client = QdrantClient(":memory:")
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client.create_collection("knowledge", vectors_config=VectorParams(size=1536, distance=Distance.COSINE))
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vector_store = QdrantVectorStore(client=client, collection_name="knowledge", embedding=embeddings)
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@tool
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def search_knowledge(query: str) -> str:
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"""Search the knowledge base."""
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docs = vector_store.similarity_search(query, k=3)
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return "\\n".join(d.page_content for d in docs) if docs else "No results."
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@tool
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def add_to_knowledge(content: str, title: str = "doc") -> str:
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"""Add content to knowledge base."""
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vector_store.add_documents([Document(page_content=content, metadata={{"title": title}})])
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return f"Added: {{title}}"
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```
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requirements.txt добавить: langchain-qdrant, qdrant-client
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### Планирующий агент (для planning-заданий):
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```python
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from langgraph.graph import StateGraph, START, END
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from typing import TypedDict, Annotated
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from langgraph.graph.message import add_messages
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class PlanState(TypedDict):
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messages: Annotated[list, add_messages]
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plan: list[str]
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current_step: int
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def planner_node(state):
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# LLM создаёт план
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...
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def executor_node(state):
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# LLM выполняет шаг плана
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...
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```
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### Самокорректирующийся агент:
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```python
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# Агент проверяет свой вывод и исправляет если нужно
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@tool
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def validate_output(output: str) -> str:
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"""Validate the output and return issues if any."""
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issues = []
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if len(output) < 10:
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issues.append("Output too short")
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return "OK" if not issues else f"Issues: {{', '.join(issues)}}"
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```
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### Структурированный вывод (Pydantic):
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```python
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from pydantic import BaseModel, Field
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from langchain_core.output_parsers import PydanticOutputParser
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class MyOutput(BaseModel):
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field1: str = Field(description="...")
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field2: int = Field(description="...")
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parser = PydanticOutputParser(pydantic_object=MyOutput)
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```
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## Требования
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- Полный рабочий код без заглушек (no pass, TODO, ...)
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- ОБЯЗАТЕЛЬНО использовать create_deep_agent из deepagents
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- requirements.txt: deepagents, langchain>=1.2.10, langchain-openai>=0.3.0, langgraph>=0.2.0 + нужные доп. зависимости
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## Ответ — ТОЛЬКО JSON без markdown:
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{{"main_py": "...", "requirements_txt": "...", "extra_files": {{}}}}
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extra_files — только если нужны доп. файлы, иначе пустой объект.
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'''
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async def generate(task_text: str, retries=5) -> dict:
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prompt = _PROMPT.format(task_text=task_text)
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for attempt in range(1, retries + 1):
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try:
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print(f" [llm] Генерирую решение (попытка {attempt})...")
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resp = await llm.ainvoke(prompt)
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raw = resp.content.strip()
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if raw.startswith("```"):
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raw = raw.split("```")[1]
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if raw.startswith("json"):
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raw = raw[4:]
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return json.loads(raw.strip())
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except json.JSONDecodeError as e:
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print(f" [llm] JSON parse error: {e}. Повтор...")
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if attempt == retries:
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raise
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except Exception as e:
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if "429" in str(e) and attempt < retries:
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wait = 90 * attempt
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print(f" [llm] 429, жду {wait}с (попытка {attempt}/{retries})...")
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await asyncio.sleep(wait)
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else:
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raise
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# ---------------------------------------------------------------------------
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# Основная логика
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# ---------------------------------------------------------------------------
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async def solve(task_id: str):
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print(f"\n{'='*60}")
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print(f"Задание: {task_id}")
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print('='*60)
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# 1. Читаем текст задания
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print("[1/5] Читаем текст задания...")
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task_text = await mcp_call("task_text", {"taskId": task_id})
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print(f" Получено {len(task_text)} символов")
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# 2. Генерируем код
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print("[2/5] Генерируем код (1 LLM-вызов)...")
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solution = await generate(task_text)
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main_py = solution.get("main_py", "")
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requirements = solution.get("requirements_txt", "")
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extra = solution.get("extra_files", {})
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print(f" main.py: {len(main_py)} символов, requirements.txt: {len(requirements)} символов")
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# 3. Создаём репо
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repo = f"task-{task_id}"
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print(f"[3/5] Создаём репозиторий {repo}...")
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repo_url = gitea_create_repo(repo)
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print(f" {repo_url}")
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# 4. Пушим файлы
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print("[4/5] Пушим файлы...")
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gitea_write(repo, "main.py", main_py, "add main.py")
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print(" main.py ✓")
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gitea_write(repo, "requirements.txt", requirements, "add requirements.txt")
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print(" requirements.txt ✓")
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for fname, fcontent in extra.items():
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gitea_write(repo, fname, fcontent, f"add {fname}")
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print(f" {fname} ✓")
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# 5. Сабмитим
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print("[5/5] Сабмитим...")
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await mcp_call("task_update_answer", {
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"taskId": task_id, "answerType": "link", "content": repo_url,
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})
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print(" task_update_answer ✓")
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await asyncio.sleep(3)
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await mcp_call("task_submit", {"taskId": task_id, "confirmSubmit": True})
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print(" task_submit ✓")
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print(f"\n✅ Готово! Репозиторий: {repo_url}")
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return repo_url
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async def main():
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if len(sys.argv) < 2:
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print("Использование: python solve_task.py <task_id>")
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sys.exit(1)
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await solve(sys.argv[1])
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if __name__ == "__main__":
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asyncio.run(main())
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