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@arizeai/phoenix-mcp

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by Arize-ai
tavily.py1.45 kB
import os import requests from dotenv import load_dotenv from llama_index.core.schema import Document async def get_docs_from_tavily_search(sub_query: str, visited_urls: set[str]): load_dotenv() api_key = os.getenv("TAVILY_API_KEY") base_url = "https://api.tavily.com/search" headers = { "Content-Type": "application/json", } data = { "query": sub_query, "api_key": api_key, "include_raw_content": True, } docs = [] print(f"\n> Searching Tavily for sub query: {sub_query}\n") response = requests.post(base_url, headers=headers, json=data) if response.status_code == 200: search_results = response.json().get("results", []) for search_result in search_results: url = search_result.get("url") if not search_result.get("raw_content"): continue if url not in visited_urls: visited_urls.add(url) docs.append( Document( text=search_result.get("raw_content"), metadata={ "source": url, "title": search_result.get("title"), }, ) ) print(f"\n> Found {len(docs)} docs from Tavily search on {sub_query}\n") return docs, visited_urls else: response.raise_for_status()

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