555 lines
22 KiB
Python
555 lines
22 KiB
Python
"""Tavily web search class.
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"""
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import logging
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import asyncio
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import re
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from dataclasses import dataclass
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from typing import Optional, List, Dict
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from tavily import AsyncTavilyClient
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from modules.shared.configuration import APP_CONFIG
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from modules.aicore.aicoreBase import BaseConnectorAi
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from modules.datamodels.datamodelAi import AiModel, PriorityEnum, ProcessingModeEnum, OperationTypeEnum, AiModelCall, AiModelResponse, createOperationTypeRatings, AiCallPromptWebSearch, AiCallPromptWebCrawl
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from modules.datamodels.datamodelTools import CountryCodes
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logger = logging.getLogger(__name__)
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@dataclass
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class WebSearchResult:
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title: str
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url: str
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rawContent: Optional[str] = None
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@dataclass
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class WebCrawlResult:
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url: str
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content: str
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title: Optional[str] = None
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class ConnectorWeb(BaseConnectorAi):
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"""Tavily web search connector."""
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def __init__(self):
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super().__init__()
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self.client: Optional[AsyncTavilyClient] = None
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# Cached settings loaded at initialization time
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self.crawlTimeout: int = 30
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self.crawlMaxRetries: int = 3
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self.crawlRetryDelay: int = 2
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# Cached web search constraints (camelCase per project style)
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self.webSearchMinResults: int = 1
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self.webSearchMaxResults: int = 20
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# Initialize client if API key is available
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self._initializeClient()
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def _initializeClient(self):
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"""Initialize the Tavily client if API key is available."""
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try:
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apiKey = APP_CONFIG.get("Connector_AiTavily_API_SECRET")
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if apiKey:
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self.client = AsyncTavilyClient(api_key=apiKey)
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logger.info("Tavily client initialized successfully")
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else:
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logger.warning("Tavily API key not found, client not initialized")
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except Exception as e:
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logger.error(f"Failed to initialize Tavily client: {str(e)}")
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def getConnectorType(self) -> str:
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"""Get the connector type identifier."""
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return "tavily"
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def _convertIsoCodeToCountryName(self, isoCode: str) -> str:
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"""
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Convert ISO-2 country code to Tavily country name.
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Uses centralized CountryCodes mapping.
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"""
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return CountryCodes.getForTavily(isoCode)
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def _extractUrlsFromPrompt(self, prompt: str) -> List[str]:
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"""Extract URLs from a text prompt using regex."""
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if not prompt:
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return []
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# URL regex pattern - matches http/https URLs
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urlPattern = r'https?://(?:[-\w.])+(?:[:\d]+)?(?:/(?:[\w/_.])*(?:\?(?:[\w&=%.])*)?(?:#(?:[\w.])*)?)?'
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urls = re.findall(urlPattern, prompt)
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# Remove duplicates while preserving order
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seen = set()
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uniqueUrls = []
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for url in urls:
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if url not in seen:
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seen.add(url)
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uniqueUrls.append(url)
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return uniqueUrls
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def _normalizeUrl(self, url: str) -> str:
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"""
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Normalize URL for better deduplication.
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Removes common variations that represent the same content.
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"""
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if not url:
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return url
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# Remove trailing slashes
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url = url.rstrip('/')
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# Remove common query parameters that don't affect content
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import urllib.parse
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parsed = urllib.parse.urlparse(url)
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# Remove common tracking parameters
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queryParams = urllib.parse.parse_qs(parsed.query)
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filteredParams = {}
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for key, values in queryParams.items():
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# Keep important parameters, remove tracking ones
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if key.lower() not in ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
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'fbclid', 'gclid', 'ref', 'source', 'campaign']:
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filteredParams[key] = values
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# Rebuild query string
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filteredQuery = urllib.parse.urlencode(filteredParams, doseq=True)
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# Reconstruct URL
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normalized = urllib.parse.urlunparse((
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parsed.scheme,
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parsed.netloc,
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parsed.path,
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parsed.params,
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filteredQuery,
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parsed.fragment
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))
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return normalized
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def _calculateRelevanceScore(self, result: WebSearchResult, queryWords: set) -> float:
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"""
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Calculate relevance score for a search result.
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Higher score means more relevant to the query.
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"""
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score = 0.0
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# Title relevance (most important)
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titleWords = set(result.title.lower().split())
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titleMatches = len(queryWords.intersection(titleWords))
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score += titleMatches * 3.0 # Weight title matches heavily
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# URL relevance
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urlWords = set(result.url.lower().split('/'))
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urlMatches = len(queryWords.intersection(urlWords))
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score += urlMatches * 1.5
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# Content relevance (if available)
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if hasattr(result, 'rawContent') and result.rawContent:
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contentWords = set(result.rawContent.lower().split())
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contentMatches = len(queryWords.intersection(contentWords))
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score += contentMatches * 0.1 # Lower weight for content matches
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# Domain authority bonus (simple heuristic)
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domain = result.url.split('/')[2] if '/' in result.url else result.url
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if any(authDomain in domain.lower() for authDomain in
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['wikipedia.org', 'github.com', 'stackoverflow.com', 'reddit.com', 'medium.com']):
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score += 1.0
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# Penalty for very long URLs (often less relevant)
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if len(result.url) > 100:
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score -= 0.5
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return score
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def _intelligentUrlFiltering(self, searchResults: List[WebSearchResult], query: str, maxResults: int) -> List[WebSearchResult]:
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"""
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Intelligent URL filtering with de-duplication and relevance scoring.
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Args:
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searchResults: Raw search results from Tavily
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query: Original search query for relevance scoring
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maxResults: Maximum number of results to return
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Returns:
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Filtered and deduplicated list of search results
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"""
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if not searchResults:
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return []
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# Step 1: Basic de-duplication by URL
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seenUrls = set()
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uniqueResults = []
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for result in searchResults:
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# Normalize URL for better deduplication
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normalizedUrl = self._normalizeUrl(result.url)
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if normalizedUrl not in seenUrls:
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seenUrls.add(normalizedUrl)
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uniqueResults.append(result)
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logger.info(f"After basic deduplication: {len(uniqueResults)} unique URLs from {len(searchResults)} original")
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# Step 2: Relevance scoring and filtering
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scoredResults = []
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queryWords = set(query.lower().split())
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for result in uniqueResults:
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score = self._calculateRelevanceScore(result, queryWords)
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scoredResults.append((score, result))
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# Step 3: Sort by relevance score (higher is better)
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scoredResults.sort(key=lambda x: x[0], reverse=True)
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# Step 4: Take top results
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filteredResults = [result for score, result in scoredResults[:maxResults]]
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logger.info(f"After intelligent filtering: {len(filteredResults)} results selected from {len(uniqueResults)} unique")
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return filteredResults
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def getModels(self) -> List[AiModel]:
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"""Get all available Tavily models."""
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return [
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AiModel(
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name="tavily-search",
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displayName="Tavily Search & Research",
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connectorType="tavily",
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apiUrl="https://api.tavily.com",
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temperature=0.0, # Web search doesn't use temperature
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maxTokens=0, # Web search doesn't use tokens
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=8, # Good speed for search and extract
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qualityRating=9, # Excellent quality for web research
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# capabilities removed (not used in business logic)
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functionCall=self._routeWebOperation,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.WEB_SEARCH, 9),
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(OperationTypeEnum.WEB_CRAWL, 8)
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),
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version="tavily-search",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.008 # Simple flat rate
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)
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]
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@classmethod
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async def create(cls):
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apiKey = APP_CONFIG.get("Connector_AiTavily_API_SECRET")
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if not apiKey:
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raise ValueError("Tavily API key not configured. Please set Connector_AiTavily_API_SECRET in config.ini")
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# Load and cache web crawl related configuration
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crawlTimeout = int(APP_CONFIG.get("Web_Crawl_TIMEOUT", "30"))
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crawlMaxRetries = int(APP_CONFIG.get("Web_Crawl_MAX_RETRIES", "3"))
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crawlRetryDelay = int(APP_CONFIG.get("Web_Crawl_RETRY_DELAY", "2"))
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return cls(
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client=AsyncTavilyClient(api_key=apiKey),
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crawlTimeout=crawlTimeout,
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crawlMaxRetries=crawlMaxRetries,
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crawlRetryDelay=crawlRetryDelay,
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webSearchMinResults=int(APP_CONFIG.get("Web_Search_MIN_RESULTS", "1")),
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webSearchMaxResults=int(APP_CONFIG.get("Web_Search_MAX_RESULTS", "20")),
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)
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# Standardized method using AiModelCall/AiModelResponse pattern
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def _cleanUrl(self, url: str) -> str:
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"""Clean URL by removing extra text that might be appended."""
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import re
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# Extract just the URL part, removing any extra text after it
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urlMatch = re.match(r'(https?://[^\s,]+)', url)
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if urlMatch:
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return urlMatch.group(1)
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return url
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async def _search(
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self,
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query: str,
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maxResults: int,
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searchDepth: str | None = None,
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timeRange: str | None = None,
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topic: str | None = None,
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includeDomains: list[str] | None = None,
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excludeDomains: list[str] | None = None,
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language: str | None = None,
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country: str | None = None,
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includeAnswer: bool | None = None,
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includeRawContent: bool | None = None,
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) -> list[WebSearchResult]:
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"""Calls the Tavily API to perform a web search."""
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# Make sure maxResults is within the allowed range (use cached values)
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minResults = self.webSearchMinResults
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maxAllowedResults = self.webSearchMaxResults
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if maxResults < minResults or maxResults > maxAllowedResults:
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raise ValueError(f"maxResults must be between {minResults} and {maxAllowedResults}")
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# Perform actual API call
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# Build kwargs only for provided options to avoid API rejections
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kwargs: dict = {"query": query, "max_results": maxResults}
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if searchDepth is not None:
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kwargs["search_depth"] = searchDepth
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if timeRange is not None:
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kwargs["time_range"] = timeRange
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if topic is not None:
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kwargs["topic"] = topic
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if includeDomains is not None and len(includeDomains) > 0:
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kwargs["include_domains"] = includeDomains
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if excludeDomains is not None:
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kwargs["exclude_domains"] = excludeDomains
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if language is not None:
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kwargs["language"] = language
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if country is not None:
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kwargs["country"] = country
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if includeAnswer is not None:
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kwargs["include_answer"] = includeAnswer
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if includeRawContent is not None:
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kwargs["include_raw_content"] = includeRawContent
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logger.debug(f"Tavily.search kwargs: {kwargs}")
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# Ensure client is initialized
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if self.client is None:
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self._initializeClient()
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if self.client is None:
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raise ValueError("Tavily client not initialized. Please check API key configuration.")
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response = await self.client.search(**kwargs)
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return [
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WebSearchResult(
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title=result["title"],
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url=self._cleanUrl(result["url"]),
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rawContent=result.get("raw_content")
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)
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for result in response["results"]
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]
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async def _crawl(
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self,
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urls: list,
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extractDepth: str | None = None,
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format: str | None = None,
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) -> list[WebCrawlResult]:
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"""Calls the Tavily API to extract text content from URLs with retry logic."""
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maxRetries = self.crawlMaxRetries
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retryDelay = self.crawlRetryDelay
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timeout = self.crawlTimeout
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logger.debug(f"Starting crawl of {len(urls)} URLs: {urls}")
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logger.debug(f"Crawl settings: extractDepth={extractDepth}, format={format}, timeout={timeout}s")
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for attempt in range(maxRetries + 1):
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try:
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logger.debug(f"Crawl attempt {attempt + 1}/{maxRetries + 1}")
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# Use asyncio.wait_for for timeout
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# Build kwargs for extract
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kwargsExtract: dict = {"urls": urls}
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kwargsExtract["extract_depth"] = extractDepth or "advanced"
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kwargsExtract["format"] = format or "markdown" # Use markdown to get HTML structure
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logger.debug(f"Sending request to Tavily with kwargs: {kwargsExtract}")
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# Ensure client is initialized
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if self.client is None:
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self._initializeClient()
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if self.client is None:
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raise ValueError("Tavily client not initialized. Please check API key configuration.")
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response = await asyncio.wait_for(
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self.client.extract(**kwargsExtract),
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timeout=timeout
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)
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logger.debug(f"Tavily response received: {list(response.keys())}")
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# Debug: Log what Tavily actually returns
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if "results" in response and response["results"]:
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logger.debug(f"Tavily returned {len(response['results'])} results")
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logger.debug(f"First result keys: {list(response['results'][0].keys())}")
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logger.debug(f"First result has raw_content: {'raw_content' in response['results'][0]}")
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# Log each result
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for i, result in enumerate(response["results"]):
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logger.debug(f"Result {i+1}: URL={result.get('url', 'N/A')}, content_length={len(result.get('raw_content', result.get('content', '')))}")
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else:
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logger.warning(f"Tavily returned no results in response: {response}")
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results = [
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WebCrawlResult(
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url=result["url"],
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content=result.get("raw_content", result.get("content", "")), # Try raw_content first, fallback to content
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title=result.get("title", "") # Extract title if available
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)
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for result in response["results"]
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]
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logger.debug(f"Crawl successful: extracted {len(results)} results")
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return results
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except asyncio.TimeoutError:
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logger.warning(f"Crawl attempt {attempt + 1} timed out after {timeout} seconds for URLs: {urls}")
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if attempt < maxRetries:
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logger.info(f"Retrying in {retryDelay} seconds...")
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await asyncio.sleep(retryDelay)
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else:
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raise Exception(f"Crawl failed after {maxRetries + 1} attempts due to timeout")
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except Exception as e:
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logger.warning(f"Crawl attempt {attempt + 1} failed for URLs {urls}: {str(e)}")
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logger.debug(f"Full error details: {type(e).__name__}: {str(e)}")
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# Check if it's a validation error and log more details
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if "validation" in str(e).lower():
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logger.debug(f"URL validation failed. Checking URL format:")
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for i, url in enumerate(urls):
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logger.debug(f" URL {i+1}: '{url}' (length: {len(url)})")
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# Check for common URL issues
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if ' ' in url:
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logger.debug(f" WARNING: URL contains spaces!")
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if not url.startswith(('http://', 'https://')):
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logger.debug(f" WARNING: URL doesn't start with http/https!")
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if len(url) > 2000:
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logger.debug(f" WARNING: URL is very long ({len(url)} chars)")
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if attempt < maxRetries:
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logger.info(f"Retrying in {retryDelay} seconds...")
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await asyncio.sleep(retryDelay)
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else:
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raise Exception(f"Crawl failed after {maxRetries + 1} attempts: {str(e)}")
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async def _routeWebOperation(self, modelCall: AiModelCall) -> "AiModelResponse":
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"""
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Route web operation based on operation type.
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Args:
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modelCall: AiModelCall with messages and options
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Returns:
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AiModelResponse based on operation type
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"""
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operationType = modelCall.options.operationType
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if operationType == OperationTypeEnum.WEB_SEARCH:
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return await self.webSearch(modelCall)
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elif operationType == OperationTypeEnum.WEB_CRAWL:
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return await self.webCrawl(modelCall)
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else:
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# Unsupported operation type
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return AiModelResponse(
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content="",
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success=False,
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error=f"Unsupported operation type: {operationType}"
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)
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async def webSearch(self, modelCall: AiModelCall) -> "AiModelResponse":
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"""
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WEB_SEARCH operation - returns list of URLs using Tavily search.
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Args:
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modelCall: AiModelCall with AiCallPromptWebSearch as prompt
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Returns:
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AiModelResponse with JSON list of URLs
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"""
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try:
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# Extract parameters
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promptContent = modelCall.messages[0]["content"] if modelCall.messages else ""
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import json
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promptData = json.loads(promptContent)
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# Create Pydantic model
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webSearchPrompt = AiCallPromptWebSearch(**promptData)
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# Convert ISO country code to country name for Tavily
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countryName = webSearchPrompt.country
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if countryName:
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countryName = self._convertIsoCodeToCountryName(countryName)
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# Perform search
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searchResults = await self._search(
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query=webSearchPrompt.instruction,
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maxResults=webSearchPrompt.maxNumberPages,
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timeRange=webSearchPrompt.timeRange,
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country=countryName,
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language=webSearchPrompt.language,
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includeAnswer=False,
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includeRawContent=False
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)
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# Extract URLs from results
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urls = [result.url for result in searchResults]
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# Return as JSON array
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import json
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return AiModelResponse(
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content=json.dumps(urls, indent=2),
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success=True,
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metadata={"total_urls": len(urls), "operation": "WEB_SEARCH"}
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)
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except Exception as e:
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logger.error(f"Error in Tavily web search: {str(e)}")
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return AiModelResponse(
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content="[]",
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success=False,
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error=str(e)
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)
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async def webCrawl(self, modelCall: AiModelCall) -> "AiModelResponse":
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"""
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WEB_CRAWL operation - crawls one URL using Tavily.
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Args:
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modelCall: AiModelCall with AiCallPromptWebCrawl as prompt
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Returns:
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AiModelResponse with crawl results as JSON
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"""
|
|
try:
|
|
# Extract parameters
|
|
promptContent = modelCall.messages[0]["content"] if modelCall.messages else ""
|
|
import json
|
|
promptData = json.loads(promptContent)
|
|
|
|
# Create Pydantic model
|
|
webCrawlPrompt = AiCallPromptWebCrawl(**promptData)
|
|
|
|
# Perform crawl for ONE URL
|
|
# Note: _crawl expects a list, so we wrap the single URL in a list
|
|
crawlResults = await self._crawl(
|
|
urls=[webCrawlPrompt.url],
|
|
extractDepth="advanced" if webCrawlPrompt.maxDepth > 2 else "basic",
|
|
format="markdown"
|
|
)
|
|
|
|
# Format result for single URL - consistent with Perplexity format
|
|
if crawlResults and len(crawlResults) > 0:
|
|
firstResult = crawlResults[0]
|
|
resultData = {
|
|
"url": firstResult.url,
|
|
"title": firstResult.title if firstResult.title else "Content",
|
|
"content": firstResult.content
|
|
}
|
|
else:
|
|
resultData = {"url": webCrawlPrompt.url, "title": "", "content": "", "error": "No content extracted"}
|
|
|
|
# Return as JSON - same format as Perplexity
|
|
import json
|
|
return AiModelResponse(
|
|
content=json.dumps(resultData, indent=2),
|
|
success=True,
|
|
metadata={"operation": "WEB_CRAWL", "url": webCrawlPrompt.url}
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in Tavily web crawl: {str(e)}")
|
|
import json
|
|
errorResult = {"error": str(e), "url": ""}
|
|
return AiModelResponse(
|
|
content=json.dumps(errorResult, indent=2),
|
|
success=False,
|
|
error=str(e)
|
|
)
|