140 lines
4.3 KiB
Python
140 lines
4.3 KiB
Python
"""OpenAI-compatible LLM API client
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Supports any provider that follows the OpenAI chat completions API format:
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- Zhipu GLM (open.bigmodel.cn)
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- DeepSeek (api.deepseek.com)
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- OpenAI, Moonshot, Qwen, etc.
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"""
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import os
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import re
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import time
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import requests
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from typing import Optional, List
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def _resolve_env_vars(value: str) -> str:
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"""Replace ${VAR} or $VAR with environment variable values."""
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if not isinstance(value, str):
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return value
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def _replace(m):
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var = m.group(1) or m.group(2)
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return os.environ.get(var, m.group(0))
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return re.sub(r'\$\{(\w+)\}|\$(\w+)', _replace, value)
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def _detect_provider(api_url: str) -> str:
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"""Detect provider from api_url, returns provider name."""
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if "deepseek" in api_url:
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return "deepseek"
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elif "bigmodel" in api_url:
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return "glm"
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else:
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return "openai"
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class LLMClient:
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"""OpenAI-compatible LLM API client.
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Each model must have its own api_url and api_key configured in MODEL_CONFIG.
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"""
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def __init__(self, model_config: dict):
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"""Initialize with per-model config lookup.
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Args:
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model_config: {model_id: {"api_url": ..., "api_key": ...}}
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"""
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self.model_config = model_config
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def _get_credentials(self, model: str):
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"""Get api_url and api_key for a model, with env-var expansion."""
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cfg = self.model_config.get(model)
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if not cfg:
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raise ValueError(f"Unknown model: '{model}', not found in config")
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api_url = _resolve_env_vars(cfg.get("api_url", ""))
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api_key = _resolve_env_vars(cfg.get("api_key", ""))
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if not api_url:
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raise ValueError(f"Model '{model}' has no api_url configured")
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if not api_key:
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raise ValueError(f"Model '{model}' has no api_key configured")
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return api_url, api_key
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def _build_body(self, model, messages, max_tokens, temperature, thinking_enabled, tools, stream, api_url):
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"""Build request body with provider-specific parameter adaptation."""
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provider = _detect_provider(api_url)
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body = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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}
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# --- Provider-specific: max_tokens ---
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if provider == "deepseek":
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body["max_tokens"] = min(max_tokens, 8192)
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elif provider == "glm":
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body["max_tokens"] = min(max_tokens, 65536)
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else:
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body["max_tokens"] = max_tokens
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# --- Provider-specific: thinking ---
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if thinking_enabled:
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if provider == "glm":
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body["thinking"] = {"type": "enabled"}
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elif provider == "deepseek":
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pass # deepseek-reasoner has built-in reasoning, no extra param
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# --- Provider-specific: tools ---
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if tools:
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body["tools"] = tools
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body["tool_choice"] = "auto"
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# --- Provider-specific: stream ---
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if stream:
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body["stream"] = True
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if provider == "glm":
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body["stream_options"] = {"include_usage": True}
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elif provider == "deepseek":
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pass # DeepSeek does not support stream_options
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return body
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def call(
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self,
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model: str,
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messages: List[dict],
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max_tokens: int = 65536,
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temperature: float = 1.0,
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thinking_enabled: bool = False,
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tools: Optional[List[dict]] = None,
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stream: bool = False,
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timeout: int = 120,
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max_retries: int = 3,
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):
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"""Call LLM API with retry on rate limit (429)"""
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api_url, api_key = self._get_credentials(model)
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body = self._build_body(
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model, messages, max_tokens, temperature,
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thinking_enabled, tools, stream, api_url,
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)
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for attempt in range(max_retries + 1):
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resp = requests.post(
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api_url,
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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},
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json=body,
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stream=stream,
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timeout=timeout,
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)
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if resp.status_code == 429 and attempt < max_retries:
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wait = 2 ** attempt
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time.sleep(wait)
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continue
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return resp
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return resp |