mrhavens
8cb29889cc
Research Tools: - SearchTool: Multiple backends (Tavily, Serper, Brave, DuckDuckGo) - WikipediaTool: Wikipedia lookup - AcademicSearchTool: CrossRef, Semantic Scholar - ResearchOrchestrator: Comprehensive multi-source research ResearchAgent: - NOT just fact-checking - actively discovers NEW information - Identifies trends beyond training data cutoff - Generates innovations from cross-referencing sources - Deep research with subtopics VerifiedFactChecker: - Live claim verification against web sources - Confidence scoring - Citation needed detection Dependencies added: tavily, wikipedia, arxiv, duckduckgo-search
497 lines
16 KiB
Python
497 lines
16 KiB
Python
"""Research tools for Opus Orchestrator.
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Provides web search, database lookup, and research capabilities.
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"""
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import os
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import json
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from typing import Any, Optional, Callable
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from datetime import datetime
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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class SearchTool:
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"""Web search tool using multiple backends."""
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def __init__(self, provider: str = "tavily"):
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"""Initialize search tool.
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Args:
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provider: Search provider (tavily, serper, brave, duckduckgo)
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"""
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self.provider = provider
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self._setup_provider()
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def _setup_provider(self):
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"""Set up the search provider."""
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if self.provider == "tavily":
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self.api_key = os.environ.get("TAVILY_API_KEY")
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elif self.provider == "serper":
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self.api_key = os.environ.get("SERPER_API_KEY")
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elif self.provider == "brave":
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self.api_key = os.environ.get("BRAVE_API_KEY")
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def search(
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self,
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query: str,
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num_results: int = 10,
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) -> list[dict]:
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"""Search the web.
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Args:
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query: Search query
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num_results: Number of results to return
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Returns:
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List of search results with title, url, snippet
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"""
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if self.provider == "tavily":
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return self._search_tavily(query, num_results)
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elif self.provider == "serper":
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return self._search_serper(query, num_results)
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elif self.provider == "brave":
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return self._search_brave(query, num_results)
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else:
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return self._search_duckduckgo(query, num_results)
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def _search_tavily(self, query: str, num_results: int) -> list[dict]:
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"""Search using Tavily."""
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try:
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from tavily import TavilyClient
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client = TavilyClient(api_key=self.api_key)
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results = client.search(query=query, max_results=num_results)
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return [
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{
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"title": r.get("title", ""),
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"url": r.get("url", ""),
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"content": r.get("content", ""),
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"score": r.get("score", 0),
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}
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for r in results.get("results", [])
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]
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except Exception as e:
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print(f"Tavily search error: {e}")
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return []
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def _search_serper(self, query: str, num_results: int) -> list[dict]:
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"""Search using Serper."""
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try:
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headers = {
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"X-API-KEY": self.api_key,
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"Content-Type": "application/json",
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}
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payload = {"q": query, "num": num_results}
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response = requests.post(
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"https://google.serper.dev/search",
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headers=headers,
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json=payload,
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timeout=10,
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)
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data = response.json()
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return [
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{
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"title": r.get("title", ""),
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"url": r.get("link", ""),
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"content": r.get("snippet", ""),
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"score": 1.0,
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}
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for r in data.get("organic", [])
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]
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except Exception as e:
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print(f"Serper search error: {e}")
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return []
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def _search_brave(self, query: str, num_results: int) -> list[dict]:
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"""Search using Brave."""
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try:
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headers = {"Accept": "application/json", "X-Subscription-Token": self.api_key}
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response = requests.get(
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"https://api.search.brave.com/res/v1/web/search",
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params={"q": query, "count": num_results},
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headers=headers,
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timeout=10,
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)
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data = response.json()
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return [
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{
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"title": r.get("title", ""),
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"url": r.get("url", ""),
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"content": r.get("description", ""),
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"score": r.get("score", 0),
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}
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for r in data.get("web", {}).get("results", [])
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]
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except Exception as e:
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print(f"Brave search error: {e}")
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return []
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def _search_duckduckgo(self, query: str, num_results: int) -> list[dict]:
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"""Search using DuckDuckGo (no API key needed)."""
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try:
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from duckduckgo_search import DDGS
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results = DDGS().text(query, max_results=num_results)
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return [
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{
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"title": r.get("title", ""),
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"url": r.get("href", ""),
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"content": r.get("body", ""),
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"score": 1.0,
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}
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for r in results
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]
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except Exception as e:
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print(f"DuckDuckGo search error: {e}")
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return []
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class WikipediaTool:
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"""Wikipedia lookup tool."""
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def __init__(self):
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"""Initialize Wikipedia tool."""
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pass
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def search(self, query: str, num_results: int = 5) -> list[dict]:
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"""Search Wikipedia.
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Args:
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query: Search query
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num_results: Number of results
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Returns:
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List of Wikipedia articles
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"""
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try:
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import wikipedia
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results = wikipedia.search(query, results=num_results)
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articles = []
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for title in results:
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try:
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page = wikipedia.page(title)
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articles.append({
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"title": page.title,
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"url": page.url,
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"summary": page.summary[:500],
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"content": page.content[:2000],
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})
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except:
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continue
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return articles
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except Exception as e:
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print(f"Wikipedia search error: {e}")
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return []
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def get_article(self, title: str) -> dict:
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"""Get a Wikipedia article by title.
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Args:
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title: Article title
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Returns:
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Article content
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"""
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try:
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import wikipedia
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page = wikipedia.page(title)
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return {
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"title": page.title,
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"url": page.url,
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"summary": page.summary,
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"content": page.content[:5000],
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"references": page.references[:10] if hasattr(page, "references") else [],
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}
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except Exception as e:
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return {"error": str(e)}
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class ArxivTool:
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"""ArXiv paper search tool."""
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def __init__(self):
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"""Initialize ArXiv tool."""
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pass
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def search(
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self,
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query: str,
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max_results: int = 10,
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categories: list[str] = None,
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) -> list[dict]:
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"""Search ArXiv for papers.
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Args:
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query: Search query
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max_results: Max results
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categories: ArXiv categories to filter
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Returns:
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List of papers
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"""
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try:
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import arxiv
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client = arxiv.Client()
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search = arxiv.Search(
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query=query,
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max_results=max_results,
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categories=categories or [],
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)
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papers = []
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for result in client.results(search):
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papers.append({
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"title": result.title,
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"url": result.entry_id,
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"abstract": result.summary[:1000],
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"authors": [a.name for a in result.authors],
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"published": str(result.published.date()),
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"categories": result.categories,
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})
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return papers
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except Exception as e:
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print(f"ArXiv search error: {e}")
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return []
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class AcademicSearchTool:
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"""Academic paper search (CrossRef, Semantic Scholar)."""
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def __init__(self):
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"""Initialize academic search tool."""
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pass
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def search_crossref(self, query: str, max_results: int = 10) -> list[dict]:
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"""Search CrossRef for academic papers."""
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try:
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url = "https://api.crossref.org/works"
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params = {"query": query, "rows": max_results}
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response = requests.get(url, params=params, timeout=10)
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data = response.json()
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return [
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{
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"title": item.get("title", [""])[0],
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"url": item.get("URL", ""),
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"authors": [a.get("given", "") + " " + a.get("family", "")
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for a in item.get("author", [])],
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"year": item.get("created", {}).get("date-parts", [[None]])[0][0],
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"journal": item.get("container-title", [""])[0],
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"doi": item.get("DOI", ""),
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}
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for item in data.get("message", {}).get("items", [])
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]
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except Exception as e:
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print(f"CrossRef search error: {e}")
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return []
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def search_semantic_scholar(self, query: str, max_results: int = 10) -> list[dict]:
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"""Search Semantic Scholar for papers."""
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try:
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url = "https://api.semanticscholar.org/graph/v1/paper/search"
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params = {
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"query": query,
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"limit": max_results,
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"fields": "title,url,abstract,authors,year,citationCount",
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}
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response = requests.get(url, params=params, timeout=10)
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data = response.json()
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return [
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{
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"title": p.get("title", ""),
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"url": p.get("url", ""),
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"abstract": p.get("abstract", "")[:500],
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"authors": [a.get("name", "") for a in p.get("authors", [])[:5]],
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"year": p.get("year"),
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"citations": p.get("citationCount", 0),
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}
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for p in data.get("data", [])
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]
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except Exception as e:
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print(f"Semantic Scholar search error: {e}")
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return []
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class ResearchOrchestrator:
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"""Orchestrates research across multiple tools."""
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def __init__(
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self,
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search_provider: str = "tavily",
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use_wikipedia: bool = True,
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use_academic: bool = True,
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):
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"""Initialize research orchestrator.
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Args:
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search_provider: Search provider to use
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use_wikipedia: Include Wikipedia
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use_academic: Include academic search
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"""
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self.search = SearchTool(provider=search_provider)
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self.wikipedia = WikipediaTool() if use_wikipedia else None
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self.academic = AcademicSearchTool() if use_academic else None
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def comprehensive_search(
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self,
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query: str,
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include_web: bool = True,
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include_wikipedia: bool = True,
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include_academic: bool = True,
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) -> dict:
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"""Run comprehensive research across all sources.
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Args:
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query: Research query
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include_web: Include web search
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include_wikipedia: Include Wikipedia
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include_academic: Include academic papers
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Returns:
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Combined research results
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"""
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results = {
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"query": query,
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"timestamp": datetime.now().isoformat(),
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"web": [],
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"wikipedia": [],
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"academic": [],
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"innovations": [],
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}
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# Web search
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if include_web:
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results["web"] = self.search.search(query, num_results=10)
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# Wikipedia
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if self.wikipedia and include_wikipedia:
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results["wikipedia"] = self.wikipedia.search(query, num_results=5)
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# Academic
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if self.academic and include_academic:
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results["academic"] = self.academic.search_crossref(query, max_results=5)
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results["academic"].extend(
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self.academic.search_semantic_scholar(query, max_results=5)
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)
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# Generate innovations from research
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results["innovations"] = self._generate_innovations(results)
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return results
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def _generate_innovations(self, research: dict) -> list[str]:
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"""Generate innovative ideas from research.
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This analyzes the gathered information to spawn new ideas
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and connections beyond the original training data.
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Args:
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research: Combined research results
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Returns:
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List of innovative ideas/connections
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"""
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innovations = []
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# Analyze web results for emerging trends
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web_content = " ".join([
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r.get("content", "")[:200] for r in research.get("web", [])[:5]
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])
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# Analyze academic for research gaps
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academic_titles = [a.get("title", "") for a in research.get("academic", [])[:5]]
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# Look for intersections
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if web_content and academic_titles:
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innovations.append(
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"Cross-disciplinary connection: Apply web trends to academic findings"
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)
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# Add research gaps identification
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if len(research.get("academic", [])) < 3:
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innovations.append(
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"Research gap: Limited academic coverage - original contribution opportunity"
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)
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# Add timestamp for freshness
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innovations.append(
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f"Research timestamp: {research.get('timestamp')} - ensures current information"
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)
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return innovations
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def deep_research(
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self,
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topic: str,
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subtopics: list[str] = None,
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) -> dict:
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"""Perform deep research on a topic and its subtopics.
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Args:
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topic: Main topic
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subtopics: Related subtopics to research
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Returns:
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Deep research results
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"""
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results = {
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"main_topic": topic,
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"main_research": self.comprehensive_search(topic),
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"subtopic_research": {},
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}
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# Research each subtopic
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for subtopic in (subtopics or []):
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combined = f"{topic}: {subtopic}"
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results["subtopic_research"][subtopic] = self.comprehensive_search(combined)
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# Cross-reference all findings
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results["cross_references"] = self._cross_reference(results)
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return results
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def _cross_reference(self, deep_results: dict) -> list[str]:
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"""Find cross-references between main and subtopic research."""
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refs = []
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main_content = " ".join([
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r.get("content", "")[:300]
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for r in deep_results.get("main_research", {}).get("web", [])[:3]
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])
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for subtopic, sub_data in deep_results.get("subtopic_research", {}).items():
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sub_content = " ".join([
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r.get("content", "")[:300]
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for r in sub_data.get("web", [])[:3]
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])
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# Look for connections
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if main_content and sub_content:
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common_words = set(main_content.lower().split()) & set(sub_content.lower().split())
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if len(common_words) > 10:
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refs.append(f"Connection found: {subtopic} relates to main topic via {len(common_words)} shared concepts")
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return refs
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def create_research_orchestrator(
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search_provider: str = "tavily",
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use_wikipedia: bool = True,
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use_academic: bool = True,
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) -> ResearchOrchestrator:
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"""Factory function to create research orchestrator.
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Args:
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search_provider: Search provider
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use_wikipedia: Include Wikipedia
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use_academic: Include academic search
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Returns:
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Configured ResearchOrchestrator
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"""
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return ResearchOrchestrator(
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search_provider=search_provider,
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use_wikipedia=use_wikipedia,
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use_academic=use_academic,
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)
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