add: main.py

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2026-06-27 13:45:16 +00:00
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import os
import asyncio
import argparse
from typing import List, Union, Annotated
from pydantic import BaseModel, Field, Literal
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
# ----------------- Pydantic models -----------------
class HttpOkEvent(BaseModel):
kind: Literal["ok"] = Field(..., description="Event kind: ok")
status: Literal[200] = Field(..., description="HTTP status code")
path: str = Field(..., description="Request path")
duration_ms: int = Field(..., description="Duration in milliseconds")
class HttpErrorEvent(BaseModel):
kind: Literal["error"] = Field(..., description="Event kind: error")
status: int = Field(..., description="HTTP status code (4xx/5xx)")
path: str = Field(..., description="Request path")
error_message: str = Field(..., description="Error message")
ApiEvent = Annotated[Union[HttpOkEvent, HttpErrorEvent], Field(discriminator="kind")]
# ----------------- LLM -----------------
llm = ChatOpenAI(
model="openai/gpt-oss-20b:free",
base_url="https://openrouter.ai/api/v1",
api_key=os.getenv("OPENAI_API_KEY"),
temperature=0.0,
)
# ----------------- Backend -----------------
backend = CompositeBackend([
LocalShellBackend(workspace_dir="./workspace"),
FilesystemBackend(),
])
# ----------------- Tools -----------------
@tool
def parse_block(block: str) -> ApiEvent:
"""Parse a single log block into a typed ApiEvent using LLM structured output."""
# Use the LLM to produce structured output
result = llm.with_structured_output(ApiEvent).invoke({"messages": [HumanMessage(content=block)]})
# The LLM returns an ApiEvent instance directly
return result
@tool
def parse_log(log_text: str) -> str:
"""Parse the entire log text and return a formatted table of events."""
# Split into blocks (empty lines or '---' separators)
raw_blocks = [b.strip() for b in log_text.split("\n") if b.strip()]
events: List[ApiEvent] = []
for block in raw_blocks:
try:
event = parse_block(block)
events.append(event)
except Exception as e:
# If parsing fails, skip the block but keep a note
events.append(
HttpErrorEvent(
kind="error",
status=0,
path="<unknown>",
error_message=f"Failed to parse block: {e}",
)
)
# Build table
header = f"{'kind':<6} | {'path':<20} | {'status':<6} | {'detail':<30}"
lines = [header, "-" * len(header)]
for ev in events:
if isinstance(ev, HttpOkEvent):
detail = f"duration {ev.duration_ms}ms"
else:
detail = ev.error_message
line = f"{ev.kind:<6} | {ev.path:<20} | {ev.status:<6} | {detail:<30}"
lines.append(line)
return "\n".join(lines)
# ----------------- Agent -----------------
agent = create_deep_agent(
model=llm,
tools=[parse_block, parse_log],
backend=backend,
system_prompt="You are a log parsing agent. Use the provided tools to parse the log and return a formatted table.",
)
# ----------------- CLI -----------------
async def run_agent(log_text: str):
result = await agent.ainvoke(
{"messages": [HumanMessage(content=log_text)]},
{"configurable": {"thread_id": "log-session"}},
)
# The tool output is in the last message
print(result["messages"][-1].content)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Parse raw log into structured events.")
parser.add_argument("--file", type=str, help="Path to a log file. If omitted, uses default sample.")
args = parser.parse_args()
if args.file:
with open(args.file, "r", encoding="utf-8") as f:
log = f.read()
else:
# Default sample log
log = """
2023-10-01 12:00:00 INFO /api/users 200 123ms
2023-10-01 12:00:01 ERROR /api/orders 404 Not Found
2023-10-01 12:00:02 INFO /api/products 200 98ms
2023-10-01 12:00:03 ERROR /api/payments 500 Internal Server Error
"""
asyncio.run(run_agent(log))