170 lines
6.8 KiB
Python
170 lines
6.8 KiB
Python
"""
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Tavily Research Provider
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AI-powered search implementation using Tavily API for high-quality research.
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"""
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import os
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from loguru import logger
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from models.subscription_models import APIProvider
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from services.research.tavily_service import TavilyService
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from .base_provider import ResearchProvider as BaseProvider
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class TavilyResearchProvider(BaseProvider):
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"""Tavily AI-powered search provider."""
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def __init__(self):
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self.api_key = os.getenv("TAVILY_API_KEY")
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if not self.api_key:
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raise RuntimeError("TAVILY_API_KEY not configured")
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self.tavily_service = TavilyService()
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logger.info("✅ Tavily Research Provider initialized")
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async def search(self, prompt, topic, industry, target_audience, config, user_id):
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"""Execute Tavily search and return standardized results."""
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# Build Tavily query
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query = f"{topic} {industry} {target_audience}"
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# Get Tavily-specific config options
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topic = config.tavily_topic or "general"
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search_depth = config.tavily_search_depth or "basic"
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logger.info(f"[Tavily Research] Executing search: {query}")
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# Execute Tavily search
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result = await self.tavily_service.search(
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query=query,
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topic=topic,
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search_depth=search_depth,
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max_results=min(config.max_sources, 20),
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include_domains=config.tavily_include_domains or None,
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exclude_domains=config.tavily_exclude_domains or None,
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include_answer=config.tavily_include_answer or False,
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include_raw_content=config.tavily_include_raw_content or False,
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include_images=config.tavily_include_images or False,
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include_image_descriptions=config.tavily_include_image_descriptions or False,
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time_range=config.tavily_time_range,
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start_date=config.tavily_start_date,
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end_date=config.tavily_end_date,
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country=config.tavily_country,
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chunks_per_source=config.tavily_chunks_per_source or 3,
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auto_parameters=config.tavily_auto_parameters or False
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)
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if not result.get("success"):
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raise RuntimeError(f"Tavily search failed: {result.get('error', 'Unknown error')}")
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# Transform to standardized format
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sources = self._transform_sources(result.get("results", []))
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content = self._aggregate_content(result.get("results", []))
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# Calculate cost (basic = 1 credit, advanced = 2 credits)
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cost = 0.001 if search_depth == "basic" else 0.002 # Estimate cost per search
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logger.info(f"[Tavily Research] Search completed: {len(sources)} sources, depth: {search_depth}")
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return {
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'sources': sources,
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'content': content,
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'search_type': search_depth,
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'provider': 'tavily',
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'search_queries': [query],
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'cost': {'total': cost},
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'answer': result.get("answer"), # If include_answer was requested
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'images': result.get("images", [])
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}
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def get_provider_enum(self):
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"""Return TAVILY provider enum for subscription tracking."""
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return APIProvider.TAVILY
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def estimate_tokens(self) -> int:
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"""Estimate token usage for Tavily (not token-based, but we estimate API calls)."""
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return 0 # Tavily is per-search, not token-based
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def _transform_sources(self, results):
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"""Transform Tavily results to ResearchSource format."""
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sources = []
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for idx, result in enumerate(results):
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source_type = self._determine_source_type(result.get("url", ""))
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sources.append({
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'title': result.get("title", ""),
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'url': result.get("url", ""),
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'excerpt': result.get("content", "")[:500], # First 500 chars
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'credibility_score': result.get("relevance_score", 0.5),
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'published_at': result.get("published_date"),
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'index': idx,
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'source_type': source_type,
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'content': result.get("content", ""),
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'raw_content': result.get("raw_content"), # If include_raw_content was requested
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'score': result.get("score", result.get("relevance_score", 0.5)),
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'favicon': result.get("favicon")
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})
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return sources
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def _determine_source_type(self, url):
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"""Determine source type from URL."""
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if not url:
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return 'web'
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url_lower = url.lower()
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if 'arxiv.org' in url_lower or 'research' in url_lower or '.edu' in url_lower:
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return 'academic'
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elif any(news in url_lower for news in ['cnn.com', 'bbc.com', 'reuters.com', 'theguardian.com', 'nytimes.com']):
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return 'news'
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elif 'linkedin.com' in url_lower:
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return 'expert'
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elif '.gov' in url_lower:
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return 'government'
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else:
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return 'web'
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def _aggregate_content(self, results):
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"""Aggregate content from Tavily results for LLM analysis."""
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content_parts = []
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for idx, result in enumerate(results):
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content = result.get("content", "")
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if content:
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content_parts.append(f"Source {idx + 1}: {content}")
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return "\n\n".join(content_parts)
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def track_tavily_usage(self, user_id: str, cost: float, search_depth: str):
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"""Track Tavily API usage after successful call."""
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from services.database import get_db
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from services.subscription import PricingService
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from sqlalchemy import text
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db = next(get_db())
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try:
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pricing_service = PricingService(db)
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current_period = pricing_service.get_current_billing_period(user_id)
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# Update tavily_calls and tavily_cost via SQL UPDATE
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update_query = text("""
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UPDATE usage_summaries
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SET tavily_calls = COALESCE(tavily_calls, 0) + 1,
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tavily_cost = COALESCE(tavily_cost, 0) + :cost,
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total_calls = COALESCE(total_calls, 0) + 1,
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total_cost = COALESCE(total_cost, 0) + :cost
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WHERE user_id = :user_id AND billing_period = :period
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""")
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db.execute(update_query, {
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'cost': cost,
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'user_id': user_id,
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'period': current_period
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})
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db.commit()
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logger.info(f"[Tavily] Tracked usage: user={user_id}, cost=${cost}, depth={search_depth}")
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except Exception as e:
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logger.error(f"[Tavily] Failed to track usage: {e}", exc_info=True)
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db.rollback()
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finally:
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db.close()
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