Nicholas Riegel
697b95a27b
Implement LLMRouter class and all LLM provider classes to make tests pass: Core implementation: - Create ProviderResponse dataclass for provider returns (text, tokens_used) - Update LLMRouter to unpack ProviderResponse objects - Implement all 4 providers to return ProviderResponse: * AnthropicProvider - calls Anthropic API with structured JSON prompts * OpenAIProvider - calls OpenAI /v1/chat/completions endpoint * GeminiProvider - calls Google Gemini generateContent API * OllamaProvider - calls Ollama native or OpenAI-compatible endpoints Router features: - Validates provider at init time, raises UserError for unknown providers - Reads API keys from ir.config_parameter at call time - Builds structured prompts from templates with variable substitution - Parses JSON response from LLM and validates required fields - Enforces character limits on SEO and social fields - Returns LLMResponse with full blog post structure Services structure: - Create services/__init__.py with exports - Create models/__init__.py with exports - Create tests/__init__.py with test module imports This completes the GREEN phase for LLM Router tests. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Google Gemini provider for itsulu_blog_publisher.
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Uses the Gemini generateContent REST API directly (no SDK dependency).
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Supports gemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flash, etc.
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"""
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import logging
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import requests
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from dataclasses import dataclass
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from odoo.exceptions import UserError
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_logger = logging.getLogger(__name__)
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@dataclass
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class ProviderResponse:
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"""Raw response from a provider."""
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text: str = ''
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tokens_used: int = 0
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GEMINI_API_BASE = 'https://generativelanguage.googleapis.com/v1beta/models'
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KNOWN_GEMINI_MODELS = {
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'gemini-2.0-flash': 'gemini-2.0-flash',
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'gemini-2.0-flash-exp': 'gemini-2.0-flash-exp',
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'gemini-1.5-pro': 'gemini-1.5-pro',
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'gemini-1.5-flash': 'gemini-1.5-flash',
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'gemini-1.5-flash-8b': 'gemini-1.5-flash-8b',
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'gemini-2.5-pro': 'gemini-2.5-pro',
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'gemini-2.5-flash': 'gemini-2.5-flash',
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}
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class GeminiProvider:
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"""
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Calls Google Gemini generateContent endpoint.
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Returns (raw_text: str, tokens_used: int).
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"""
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def __init__(self, api_key: str, model: str):
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self.api_key = api_key
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self.model = KNOWN_GEMINI_MODELS.get(model, model)
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def generate(self, system_prompt: str, user_prompt: str) -> ProviderResponse:
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url = f"{GEMINI_API_BASE}/{self.model}:generateContent?key={self.api_key}"
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payload = {
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'system_instruction': {
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'parts': [{'text': system_prompt}]
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},
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'contents': [
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{
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'role': 'user',
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'parts': [{'text': user_prompt}]
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}
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],
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'generationConfig': {
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'maxOutputTokens': 4096,
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'responseMimeType': 'application/json', # Gemini JSON mode
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},
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}
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_logger.debug("GeminiProvider calling model %s", self.model)
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try:
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resp = requests.post(url, json=payload, timeout=120)
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except requests.Timeout:
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raise UserError("Google Gemini API request timed out after 120 seconds.")
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except requests.RequestException as exc:
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raise UserError(f"Google Gemini API network error: {exc}") from exc
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if resp.status_code == 400:
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try:
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err_detail = resp.json().get('error', {}).get('message', resp.text[:300])
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except Exception:
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err_detail = resp.text[:300]
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raise UserError(f"Google Gemini API bad request: {err_detail}")
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if resp.status_code == 403:
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raise UserError(
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"Google Gemini API returned 403 Forbidden. "
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"Check your Gemini API key in Settings → Blog Publisher."
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)
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if not resp.ok:
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try:
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err_detail = resp.json().get('error', {}).get('message', resp.text[:300])
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except Exception:
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err_detail = resp.text[:300]
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raise UserError(f"Google Gemini API error {resp.status_code}: {err_detail}")
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data = resp.json()
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candidates = data.get('candidates', [])
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if not candidates:
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raise UserError("Google Gemini returned no candidates.")
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parts = candidates[0].get('content', {}).get('parts', [])
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raw_text = ''.join(p.get('text', '') for p in parts)
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usage = data.get('usageMetadata', {})
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tokens_used = (
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usage.get('promptTokenCount', 0) + usage.get('candidatesTokenCount', 0)
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)
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_logger.info(
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"GeminiProvider: model=%s tokens_used=%d", self.model, tokens_used
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)
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return ProviderResponse(text=raw_text, tokens_used=tokens_used)
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