Grok DeepSearch：xAI深度研究

import jsonimport timefrom dataclasses import dataclass, fieldfrom typing import List, Dict, Optionalfrom enum import Enum class DataSource(Enum):    X_PLATFORM = "x_platform"    WEB_SEARCH = "web_search" class CredibilityLevel(Enum):    HIGH = "high"    MEDIUM = "medium"    LOW = "low"    DISPUTED = "disputed" @dataclassclass SourceItem:    url: str    title: str    source_type: DataSource    publish_time: float    credibility: CredibilityLevel    snippet: str @dataclassclass ReasoningStep:    step_id: int    action: str    query: str    findings: List[SourceItem] = field(default_factory=list)    intermediate_conclusion: str = ""    confidence: float = 0.0 class DeepSearchSimulator:    def __init__(self, max_steps: int = 5):        self.max_steps = max_steps        self.reasoning_chain: List[ReasoningStep] = []        self.all_sources: List[SourceItem] = []     def decompose_query(self, query: str) -> List[str]:        keywords = query.split()        sub_queries = []        if len(keywords) > 3:            mid = len(keywords) // 2            sub_queries.append(" ".join(keywords[:mid]))            sub_queries.append(" ".join(keywords[mid:]))        sub_queries.append(query)        return sub_queries     def search_x_platform(self, query: str) -> List[SourceItem]:        results = [            SourceItem(                url=f"https://x.com/search/result_{i}",                title=f"X Post about: {query} (result {i})",                source_type=DataSource.X_PLATFORM,                publish_time=time.time() - i * 60,                credibility=CredibilityLevel.MEDIUM if i > 0 else CredibilityLevel.HIGH,                snippet=f"Relevant tweet discussing {query} with real-time insights..."            )            for i in range(3)        ]        return results     def search_web(self, query: str) -> List[SourceItem]:        results = [            SourceItem(                url=f"https://example.com/article_{i}",                title=f"Web Article: {query} (result {i})",                source_type=DataSource.WEB_SEARCH,                publish_time=time.time() - i * 3600,                credibility=CredibilityLevel.HIGH,                snippet=f"Authoritative article analyzing {query} in depth..."            )            for i in range(3)        ]        return results     def assess_credibility(self, claim: str, sources: List[SourceItem]) -> CredibilityLevel:        high_count = sum(1 for s in sources if s.credibility == CredibilityLevel.HIGH)        if high_count >= 2:            return CredibilityLevel.HIGH        elif high_count >= 1:            return CredibilityLevel.MEDIUM        return CredibilityLevel.LOW     def merge_results(self, x_results: List[SourceItem], web_results: List[SourceItem]) -> List[SourceItem]:        merged = []        seen_urls = set()        for item in sorted(x_results + web_results, key=lambda s: s.publish_time, reverse=True):            if item.url not in seen_urls:                merged.append(item)                seen_urls.add(item.url)        return merged     def deep_search(self, query: str) -> Dict:        sub_queries = self.decompose_query(query)        for step_id, sub_q in enumerate(sub_queries[:self.max_steps]):            step = ReasoningStep(step_id=step_id + 1, action="search", query=sub_q)            x_results = self.search_x_platform(sub_q)            web_results = self.search_web(sub_q)            step.findings = self.merge_results(x_results, web_results)            self.all_sources.extend(step.findings)            step.intermediate_conclusion = f"Step {step_id + 1}: Found {len(step.findings)} sources for '{sub_q}'"            step.confidence = len([s for s in step.findings if s.credibility == CredibilityLevel.HIGH]) / max(len(step.findings), 1)            self.reasoning_chain.append(step)        final_sources = self.merge_results(            [s for s in self.all_sources if s.source_type == DataSource.X_PLATFORM],            [s for s in self.all_sources if s.source_type == DataSource.WEB_SEARCH]        )        overall_confidence = sum(s.confidence for s in self.reasoning_chain) / len(self.reasoning_chain)        return {            "query": query,            "reasoning_chain": [                {"step": s.step_id, "query": s.query, "conclusion": s.intermediate_conclusion, "confidence": round(s.confidence, 2)}                for s in self.reasoning_chain            ],            "final_sources_count": len(final_sources),            "overall_confidence": round(overall_confidence, 2),            "top_sources": [{"url": s.url, "title": s.title, "credibility": s.credibility.value} for s in final_sources[:5]]        } simulator = DeepSearchSimulator(max_steps=3)result = simulator.deep_search("Grok DeepSearch real-time X platform data integration")print(json.dumps(result, indent=2))