116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
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"""
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Google Gemini adapter — calls the Generative Language REST API directly.
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"""
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import time
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from typing import Optional, Dict, Any
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from markitect.prompts.execution.llm_adapter import LLMAdapter
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from markitect.prompts.execution.models import RunConfig, LLMResponse
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from markitect.llm.config import resolve_api_key, find_project_root
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from markitect.llm._http import post_json
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from markitect.llm.exceptions import LLMConfigurationError
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_DEFAULT_MODEL = "gemini-2.5-flash"
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_API_BASE = "https://generativelanguage.googleapis.com/v1beta"
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class GeminiAdapter(LLMAdapter):
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"""LLM adapter that calls the Google Generative Language API.
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Supports the free tier of Gemini models via a Google AI Studio API key.
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"""
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def __init__(
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self,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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system_prompt: Optional[str] = None,
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**_kwargs: Any,
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):
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self._model = model or _DEFAULT_MODEL
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self._system_prompt = system_prompt
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root = find_project_root()
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key_file_paths = [root / "apikey-geminifree.txt"] if root else []
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self._api_key = resolve_api_key(
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explicit=api_key,
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env_var="GEMINI_API_KEY",
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key_file_paths=key_file_paths,
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)
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if not self._api_key:
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raise LLMConfigurationError(
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"No Gemini API key found. Set GEMINI_API_KEY or create "
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"apikey-geminifree.txt in the project root.",
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context={"provider": "gemini"},
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)
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# ── LLMAdapter interface ────────────────────────────────────────
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def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
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model = self._model
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# Build Gemini request
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contents: list[Dict[str, Any]] = []
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if self._system_prompt:
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contents.append({
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"role": "user",
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"parts": [{"text": self._system_prompt}],
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})
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contents.append({
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"role": "model",
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"parts": [{"text": "Understood."}],
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})
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contents.append({
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"role": "user",
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"parts": [{"text": prompt}],
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})
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payload: Dict[str, Any] = {
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"contents": contents,
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"generationConfig": {
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"temperature": config.temperature,
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"maxOutputTokens": config.max_tokens,
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},
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}
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url = f"{_API_BASE}/models/{model}:generateContent?key={self._api_key}"
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start = time.time()
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data = post_json(url, payload, timeout=config.timeout_seconds)
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latency = time.time() - start
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# Parse Gemini response
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candidates = data.get("candidates", [])
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if not candidates:
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content = ""
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finish_reason = "error"
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else:
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parts = candidates[0].get("content", {}).get("parts", [])
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content = "".join(p.get("text", "") for p in parts)
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finish_reason = candidates[0].get("finishReason", "STOP").lower()
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usage_meta = data.get("usageMetadata", {})
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return LLMResponse(
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content=content,
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model=model,
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usage={
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"prompt_tokens": usage_meta.get("promptTokenCount", 0),
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"completion_tokens": usage_meta.get("candidatesTokenCount", 0),
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"total_tokens": usage_meta.get("totalTokenCount", 0),
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},
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finish_reason=finish_reason,
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metadata={
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"provider": "gemini",
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"latency_seconds": round(latency, 3),
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},
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)
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def validate_config(self, config: RunConfig) -> bool:
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if not self._api_key:
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return False
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if not (0.0 <= config.temperature <= 2.0):
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return False
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return True
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