Files
adolf/fast_tools.py
Alvis af181ba7ec Rename RealTimeSearchTool → WeatherTool, fetch Balashikha weather via SearXNG
WeatherTool queries SearXNG with a fixed 'weather Balashikha Moscow now'
query instead of passing the user message as-is. SearXNG has external
internet access and returns snippets with actual current conditions.
Direct wttr.in fetch not possible — deepagents container has no external
internet routing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-13 05:40:10 +00:00

123 lines
4.1 KiB
Python

"""
Fast Tools — pre-flight tools invoked by a classifier before the main LLM call.
Each FastTool has:
- matches(message) → bool : regex classifier that determines if this tool applies
- run(message) → str : async fetch that returns enrichment context
FastToolRunner holds a list of FastTools. The Router uses any_matches() to force
the tier to medium before LLM classification. run_agent_task() calls run_matching()
to build extra context that is injected into the system prompt.
To add a new fast tool:
1. Subclass FastTool, implement name/matches/run
2. Add an instance to the list passed to FastToolRunner in agent.py
"""
import asyncio
import re
from abc import ABC, abstractmethod
import httpx
class FastTool(ABC):
"""Base class for all pre-flight fast tools."""
@property
@abstractmethod
def name(self) -> str: ...
@abstractmethod
def matches(self, message: str) -> bool: ...
@abstractmethod
async def run(self, message: str) -> str: ...
class WeatherTool(FastTool):
"""
Fetches current weather for the user's location (Balashikha, Moscow region)
by querying SearXNG, which has external internet access.
Triggered by any weather-related query. The Router also forces medium tier
when this tool matches so the richer model handles the injected data.
"""
_PATTERN = re.compile(
r"\b(weather|forecast|temperature|rain(ing)?|snow(ing)?|humidity|wind\s*speed"
r"|холодно|тепло|погода|прогноз погоды"
r"|how (hot|cold|warm) is it|what.?s the (weather|temp)|dress for the weather)\b",
re.IGNORECASE,
)
# Fixed query — always fetch home location weather
_SEARCH_QUERY = "weather Balashikha Moscow now"
def __init__(self, searxng_url: str):
self._searxng_url = searxng_url
@property
def name(self) -> str:
return "weather"
def matches(self, message: str) -> bool:
return bool(self._PATTERN.search(message))
async def run(self, message: str) -> str:
"""Query SearXNG for Balashikha weather and return current conditions snippet."""
try:
async with httpx.AsyncClient(timeout=15) as client:
r = await client.get(
f"{self._searxng_url}/search",
params={"q": self._SEARCH_QUERY, "format": "json"},
)
r.raise_for_status()
items = r.json().get("results", [])[:5]
except Exception as e:
return f"[weather error: {e}]"
if not items:
return ""
# Prefer results whose snippets contain actual current conditions
lines = ["Current weather data for Balashikha, Moscow region:\n"]
for item in items:
snippet = item.get("content", "")
title = item.get("title", "")
url = item.get("url", "")
if snippet:
lines.append(f"[{title}]\n{snippet}\nSource: {url}\n")
return "\n".join(lines) if len(lines) > 1 else ""
class FastToolRunner:
"""
Classifier + executor for fast tools.
Used in two places:
- Router.route(): any_matches() forces medium tier before LLM classification
- run_agent_task(): run_matching() builds enrichment context in the pre-flight gather
"""
def __init__(self, tools: list[FastTool]):
self._tools = tools
def any_matches(self, message: str) -> bool:
"""True if any fast tool applies to this message."""
return any(t.matches(message) for t in self._tools)
def matching_names(self, message: str) -> list[str]:
"""Names of tools that match this message (for logging)."""
return [t.name for t in self._tools if t.matches(message)]
async def run_matching(self, message: str) -> str:
"""Run all matching tools concurrently and return combined context."""
matching = [t for t in self._tools if t.matches(message)]
if not matching:
return ""
results = await asyncio.gather(*[t.run(message) for t in matching])
parts = [r for r in results if r and not r.startswith("[")]
return "\n\n".join(parts)