vitya
ca669d96e1
Copies source (no node_modules, dist, .tasks, .wiki, __pycache__) for: - projects-meta-mcp v2.25.0 (TypeScript/Node) - wiki-graph v0.3.1 (TypeScript/Node) - interns-mcp v0.3.3 (Python/FastMCP) .gitignore: exclude lib build artefacts (node_modules, dist, .venv, __pycache__, *.pyc) bootstrap.ps1: add MCP build step — npm install+build for TS servers, venv+pip for Python Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
"""OpenAI-compatible HTTP client, persistent per-endpoint."""
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from __future__ import annotations
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from typing import Any
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from openai import OpenAI
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# Bound every LLM call. Without this the SDK default (600s) lets a stalled
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# endpoint hang the worker thread that ran the tool; enough such threads exhaust
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# FastMCP's pool and wedge the whole stdio server. 90s is well above a healthy
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# deepseek-v4-flash response yet short enough to fail fast and free the thread.
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DEFAULT_REQUEST_TIMEOUT = 90.0
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def _resolve_timeout(endpoint_cfg: dict[str, Any]) -> float:
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"""Per-endpoint request timeout in seconds; falls back to the bounded default."""
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return float(endpoint_cfg.get("request_timeout", DEFAULT_REQUEST_TIMEOUT))
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def make_client(endpoint_cfg: dict[str, Any], api_key: str) -> OpenAI:
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"""Create a persistent OpenAI client for an endpoint."""
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kwargs: dict[str, Any] = {
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"base_url": endpoint_cfg["base_url"],
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"api_key": api_key,
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"timeout": _resolve_timeout(endpoint_cfg),
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}
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defaults = endpoint_cfg.get("request_defaults", {})
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if "extra_body" in defaults:
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kwargs["default_query"] = {}
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return OpenAI(**kwargs)
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class ClientPool:
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"""Manages persistent HTTP clients per endpoint."""
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def __init__(self) -> None:
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self._clients: dict[str, OpenAI] = {}
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def get(self, name: str, endpoint_cfg: dict[str, Any], api_key: str) -> OpenAI:
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if name not in self._clients:
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self._clients[name] = make_client(endpoint_cfg, api_key)
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return self._clients[name] |