490 lines
20 KiB
Python
490 lines
20 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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from dataclasses import dataclass
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from typing import Optional, List
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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.shared.timezoneUtils import get_utc_timestamp
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from modules.aicore.aicoreBase import BaseConnectorAi
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from modules.datamodels.datamodelAi import AiModel, ModelCapabilitiesEnum, PriorityEnum, ProcessingModeEnum, OperationTypeEnum
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from modules.datamodels.datamodelWeb import (
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WebSearchActionResult,
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WebSearchActionDocument,
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WebSearchDocumentData,
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WebSearchResultItem,
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WebCrawlActionResult,
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WebCrawlActionDocument,
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WebCrawlDocumentData,
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WebCrawlResultItem,
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WebScrapeActionResult,
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WebScrapeActionDocument,
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WebSearchDocumentData as WebScrapeDocumentData,
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WebScrapeResultItem,
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)
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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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raw_content: 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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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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def getConnectorType(self) -> str:
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"""Get the connector type identifier."""
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return "tavily"
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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",
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connectorType="tavily",
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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,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.WEB_SEARCH, ModelCapabilitiesEnum.INFORMATION_RETRIEVAL, ModelCapabilitiesEnum.URL_DISCOVERY],
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functionCall=self.search,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.WEB_RESEARCH],
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version="tavily-search",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived, searchDepth="basic", numRequests=1: numRequests * (1 if searchDepth == "basic" else 2) * 0.008
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),
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AiModel(
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name="tavily_extract",
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displayName="Tavily Extract",
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connectorType="tavily",
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maxTokens=0, # Web extraction 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=6,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.WEB_CRAWLING, ModelCapabilitiesEnum.CONTENT_EXTRACTION, ModelCapabilitiesEnum.TEXT_EXTRACTION],
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functionCall=self.crawl,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.WEB_RESEARCH],
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version="tavily-extract",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived, extractionDepth="basic", numSuccessfulUrls=1: (numSuccessfulUrls / 5) * (1 if extractionDepth == "basic" else 2) * 0.008
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),
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AiModel(
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name="tavily_crawl",
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displayName="Tavily Crawl",
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connectorType="tavily",
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maxTokens=0, # Web crawling 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=6,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.WEB_CRAWLING, ModelCapabilitiesEnum.CONTENT_EXTRACTION, ModelCapabilitiesEnum.MAPPING],
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functionCall=self.crawl,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.WEB_RESEARCH],
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version="tavily-crawl",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived, numPages=10, extractionDepth="basic", withInstructions=False, numSuccessfulExtractions=10: ((numPages / 10) * (2 if withInstructions else 1) + (numSuccessfulExtractions / 5) * (1 if extractionDepth == "basic" else 2)) * 0.008
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),
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AiModel(
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name="tavily_scrape",
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displayName="Tavily Scrape",
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connectorType="tavily",
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maxTokens=0, # Web scraping 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=6,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.WEB_SEARCH, ModelCapabilitiesEnum.WEB_CRAWLING, ModelCapabilitiesEnum.CONTENT_EXTRACTION, ModelCapabilitiesEnum.INFORMATION_RETRIEVAL],
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functionCall=self.scrape,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.WEB_RESEARCH],
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version="tavily-search-extract",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived, searchDepth="basic", numSuccessfulUrls=1, extractionDepth="basic": ((1 if searchDepth == "basic" else 2) + (numSuccessfulUrls / 5) * (1 if extractionDepth == "basic" else 2)) * 0.008
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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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api_key = APP_CONFIG.get("Connector_WebTavily_API_KEY_SECRET")
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if not api_key:
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raise ValueError("Tavily API key not configured. Please set Connector_WebTavily_API_KEY_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=api_key),
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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 methods returning ActionResults for the interface to consume
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async def search(self, request) -> "WebSearchActionResult":
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try:
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raw_results = await self._search(
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query=request.query,
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max_results=request.max_results,
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search_depth=request.search_depth,
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time_range=request.time_range,
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topic=request.topic,
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include_domains=request.include_domains,
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exclude_domains=request.exclude_domains,
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language=request.language,
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include_answer=request.include_answer,
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include_raw_content=request.include_raw_content,
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)
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except Exception as e:
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return WebSearchActionResult(success=False, error=str(e))
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result_items = [
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WebSearchResultItem(
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title=result.title,
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url=result.url,
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raw_content=getattr(result, 'raw_content', None)
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)
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for result in raw_results
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]
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document_data = WebSearchDocumentData(
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query=request.query,
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results=result_items,
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total_count=len(result_items),
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)
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document = WebSearchActionDocument(
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documentName=f"web_search_results_{get_utc_timestamp()}.json",
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documentData=document_data,
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mimeType="application/json",
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)
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return WebSearchActionResult(
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success=True, documents=[document], resultLabel="web_search_results"
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)
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async def crawl(self, request) -> "WebCrawlActionResult":
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try:
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raw_results = await self._crawl(
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[str(u) for u in request.urls],
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extract_depth=request.extract_depth,
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format=request.format,
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)
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except Exception as e:
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return WebCrawlActionResult(success=False, error=str(e))
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result_items = [
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WebCrawlResultItem(url=result.url, content=result.content)
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for result in raw_results
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]
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document_data = WebCrawlDocumentData(
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urls=[str(u) for u in request.urls],
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results=result_items,
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total_count=len(result_items),
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)
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document = WebCrawlActionDocument(
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documentName=f"web_crawl_results_{get_utc_timestamp()}.json",
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documentData=document_data,
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mimeType="application/json",
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)
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return WebCrawlActionResult(
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success=True, documents=[document], resultLabel="web_crawl_results"
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)
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async def scrape(self, request) -> "WebScrapeActionResult":
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try:
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search_results = await self._search(
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query=request.query,
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max_results=request.max_results,
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search_depth=request.search_depth,
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time_range=request.time_range,
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topic=request.topic,
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include_domains=request.include_domains,
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exclude_domains=request.exclude_domains,
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language=request.language,
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include_answer=request.include_answer,
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include_raw_content=request.include_raw_content,
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)
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except Exception as e:
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return WebScrapeActionResult(success=False, error=str(e))
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try:
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urls = [result.url for result in search_results]
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crawl_results = await self._crawl(
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urls,
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extract_depth=request.extract_depth,
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format=request.format,
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)
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except Exception as e:
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return WebScrapeActionResult(success=False, error=str(e))
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result_items = [
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WebScrapeResultItem(url=result.url, content=result.content)
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for result in crawl_results
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]
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document_data = WebScrapeDocumentData(
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query=request.query,
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results=result_items,
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total_count=len(result_items),
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)
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document = WebScrapeActionDocument(
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documentName=f"web_scrape_results_{get_utc_timestamp()}.json",
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documentData=document_data,
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mimeType="application/json",
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)
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return WebScrapeActionResult(
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success=True, documents=[document], resultLabel="web_scrape_results"
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)
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async def _search_urls_raw(self,
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*,
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query: str,
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max_results: int,
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search_depth: str | None = None,
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time_range: str | None = None,
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topic: str | None = None,
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include_domains: list[str] | None = None,
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exclude_domains: list[str] | None = None,
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language: str | None = None,
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include_answer: bool | None = None,
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include_raw_content: bool | None = None,
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) -> list["WebSearchResult"]:
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return await self._search(
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query=query,
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max_results=max_results,
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search_depth=search_depth,
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time_range=time_range,
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topic=topic,
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include_domains=include_domains,
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exclude_domains=exclude_domains,
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language=language,
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include_answer=include_answer,
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include_raw_content=include_raw_content,
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)
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async def _crawl_urls_raw(self,
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*,
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urls: list[str],
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extract_depth: str | None = None,
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format: str | None = None,
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) -> list["WebCrawlResult"]:
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return await self._crawl(urls, extract_depth=extract_depth, format=format)
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async def _scrape_raw(self,
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*,
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query: str,
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max_results: int,
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search_depth: str | None = None,
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time_range: str | None = None,
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topic: str | None = None,
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include_domains: list[str] | None = None,
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exclude_domains: list[str] | None = None,
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language: str | None = None,
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include_answer: bool | None = None,
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include_raw_content: bool | None = None,
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extract_depth: str | None = None,
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format: str | None = None,
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) -> list["WebCrawlResult"]:
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search_results = await self._search(
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query=query,
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max_results=max_results,
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search_depth=search_depth,
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time_range=time_range,
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topic=topic,
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include_domains=include_domains,
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exclude_domains=exclude_domains,
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language=language,
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include_answer=include_answer,
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include_raw_content=include_raw_content,
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)
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urls = [result.url for result in search_results]
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return await self._crawl(urls, extract_depth=extract_depth, format=format)
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def _clean_url(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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url_match = re.match(r'(https?://[^\s,]+)', url)
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if url_match:
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return url_match.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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max_results: int,
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search_depth: str | None = None,
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time_range: str | None = None,
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topic: str | None = None,
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include_domains: list[str] | None = None,
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exclude_domains: list[str] | None = None,
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language: str | None = None,
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country: str | None = None,
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include_answer: bool | None = None,
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include_raw_content: 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 max_results 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 max_results < minResults or max_results > maxAllowedResults:
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raise ValueError(f"max_results 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": max_results}
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if search_depth is not None:
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kwargs["search_depth"] = search_depth
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if time_range is not None:
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kwargs["time_range"] = time_range
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if topic is not None:
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kwargs["topic"] = topic
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if include_domains is not None and len(include_domains) > 0:
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kwargs["include_domains"] = include_domains
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if exclude_domains is not None:
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kwargs["exclude_domains"] = exclude_domains
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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 include_answer is not None:
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kwargs["include_answer"] = include_answer
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if include_raw_content is not None:
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kwargs["include_raw_content"] = include_raw_content
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logger.debug(f"Tavily.search kwargs: {kwargs}")
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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._clean_url(result["url"]),
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raw_content=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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extract_depth: 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: extract_depth={extract_depth}, 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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kwargs_extract: dict = {"urls": urls}
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kwargs_extract["extract_depth"] = extract_depth or "advanced"
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kwargs_extract["format"] = format or "markdown" # Use markdown to get HTML structure
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logger.debug(f"Sending request to Tavily with kwargs: {kwargs_extract}")
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response = await asyncio.wait_for(
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self.client.extract(**kwargs_extract),
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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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)
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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:
|
|
logger.debug(f" WARNING: URL is very long ({len(url)} chars)")
|
|
|
|
if attempt < maxRetries:
|
|
logger.info(f"Retrying in {retryDelay} seconds...")
|
|
await asyncio.sleep(retryDelay)
|
|
else:
|
|
raise Exception(f"Crawl failed after {maxRetries + 1} attempts: {str(e)}")
|