import requests

def query_legal_research(query, api_key):
    """Query CaseText's research API"""
    endpoint = "https://api.casetext.com/v2/research"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "query": query,
        "jurisdiction": "federal",
        "courts": ["supreme-court", "circuit-appeals"],
        "date_range": {"from": "2020-01-01", "to": "2026-03-01"}
    }
    response = requests.post(endpoint, json=payload, headers=headers)
    return response.json()

# Example usage
results = query_legal_research(
    "Fourth Amendment search and seizure digital evidence",
    api_key="your_api_key"
)

# Building a custom legal research assistant
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

class LegalResearchAssistant:
    def __init__(self, legal_documents):
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200
        )
        self.embeddings = OpenAIEmbeddings()

    def build_knowledge_base(self, documents):
        """Index legal documents for semantic search"""
        texts = self.text_splitter.split_documents(documents)
        vectorstore = Chroma.from_documents(
            texts,
            self.embeddings,
            collection_name="legal_precedents"
        )
        return vectorstore

    def query(self, question, vectorstore, top_k=5):
        """Semantic search across legal documents"""
        from langchain.chains import RetrievalQA
        from langchain.llms import OpenAI

        qa = RetrievalQA.from_chain_type(
            llm=OpenAI(),
            chain_type="stuff",
            retriever=vectorstore.as_retriever(search_kwargs={"k": top_k})
        )
        return qa.run(question)

from functools import lru_cache
import hashlib

@lru_cache(maxsize=1000)
def cached_research_query(query_hash, *args):
    """Cache research results to reduce API costs"""
    # Query implementation here
    pass

def get_query_hash(query, filters):
    """Generate consistent hash for caching"""
    combined = f"{query}:{filters}"
    return hashlib.md5(combined.encode()).hexdigest()

def hybrid_legal_search(query, vectorstore, keyword_index):
    """Combine semantic and keyword search for better results"""
    # Get semantic matches
    semantic_results = vectorstore.similarity_search(query, k=10)

    # Get keyword matches
    keyword_results = keyword_index.search(query, limit=10)

    # Merge and rerank results
    combined = rerank_results(semantic_results, keyword_results)
    return combined

import anthropic
import json
from datetime import datetime

class LegalResearchAssistant:
    def __init__(self, api_key: str):
        self.client = anthropic.Anthropic(api_key=api_key)
        self.research_history = []

    def analyze_legal_question(
        self,
        question: str,
        jurisdiction: str = "federal",
        practice_area: str = "general"
    ) -> dict:
        """Analyze legal question and suggest research strategy."""

        system_prompt = f"""You are a legal research specialist.
Jurisdiction: {jurisdiction}
Practice Area: {practice_area}

When given a legal question:
1. Identify key legal concepts
2. Suggest relevant statutes and regulations
3. Recommend case law to review
4. Outline a research strategy
Format response as JSON."""

        message = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=2048,
            system=system_prompt,
            messages=[{
                "role": "user",
                "content": f"Legal question: {question}"
            }]
        )

        try:
            result = json.loads(message.content[0].text)
        except json.JSONDecodeError:
            result = {"analysis": message.content[0].text}

        self.research_history.append({
            "question": question,
            "timestamp": datetime.now().isoformat(),
            "result": result
        })

        return result

    def synthesize_findings(self, findings: list, case_name: str) -> dict:
        """Synthesize research findings into coherent brief."""

        findings_text = json.dumps(findings, indent=2)

        message = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=3000,
            system="You are a legal brief writer. Synthesize research into a structured brief.",
            messages=[{
                "role": "user",
                "content": f"""Case: {case_name}
Research findings:
{findings_text}

Create a brief with:
1. Issue statement
2. Rule explanation
3. Application
4. Conclusion
5. Relevant citations"""
            }]
        )

        return {
            "case": case_name,
            "brief": message.content[0].text,
            "generated_at": datetime.now().isoformat()
        }

    def cite_check(self, citation: str) -> dict:
        """Verify citation format and provide context."""

        message = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=500,
            messages=[{
                "role": "user",
                "content": f"""Verify and analyze this legal citation: {citation}
Provide:
- Proper citation format
- Case name and year
- Court level
- Legal relevance"""
            }]
        )

        return {"citation": citation, "analysis": message.content[0].text}

    def export_research(self, format: str = "pdf") -> str:
        """Export research history in specified format."""
        if format == "json":
            return json.dumps(self.research_history, indent=2)
        elif format == "markdown":
            return self._format_as_markdown()
        else:
            return json.dumps(self.research_history)

    def _format_as_markdown(self) -> str:
        """Format research history as markdown."""
        md = "# Legal Research Report\n\n"
        for entry in self.research_history:
            md += f"## {entry['question']}\n"
            md += f"_Generated: {entry['timestamp']}_\n\n"
            md += f"{json.dumps(entry['result'], indent=2)}\n\n"
        return md

# Usage example
assistant = LegalResearchAssistant(api_key="your-api-key")

# Analyze a legal question
analysis = assistant.analyze_legal_question(
    question="Can employers require genetic testing as condition of employment?",
    jurisdiction="federal",
    practice_area="employment law"
)
print("Legal Analysis:", analysis)

# Synthesize findings into brief
brief = assistant.synthesize_findings(
    findings=analysis.get("case_law", []),
    case_name="Doe v. MyCompany"
)
print("\nGenerated Brief:", brief["brief"])

# Export research
markdown_output = assistant.export_research(format="markdown")
print("\nExported Research:\n", markdown_output)

class DocumentCentricLegalResearch:
    """Research workflow centered on document analysis."""

    def __init__(self):
        self.client = anthropic.Anthropic()

    def extract_legal_issues_from_document(self, document_text: str) -> list:
        """Identify legal issues within a document."""

        message = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1500,
            messages=[{
                "role": "user",
                "content": f"""Analyze this legal document and identify:
1. Primary legal issues
2. Applicable statutes
3. Relevant case law areas
4. Potential conflicts or gaps

Document:
{document_text}"""
            }]
        )

        return self._parse_legal_issues(message.content[0].text)

    def compare_contracts(self, contract1: str, contract2: str) -> dict:
        """Compare two contracts and identify differences."""

        message = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=2000,
            messages=[{
                "role": "user",
                "content": f"""Compare these two contracts:

Contract 1:
{contract1}

Contract 2:
{contract2}

Identify:
1. Key differences in terms
2. Favorable vs unfavorable provisions
3. Missing clauses
4. Legal risk areas"""
            }]
        )

        return {
            "comparison": message.content[0].text,
            "risk_level": self._assess_risk_level(message.content[0].text)
        }

    def _parse_legal_issues(self, text: str) -> list:
        """Parse identified legal issues from response."""
        # Implementation would extract structured data from Claude's response
        return text.split("\n")

    def _assess_risk_level(self, text: str) -> str:
        """Quick risk assessment based on findings."""
        if "severe" in text.lower() or "critical" in text.lower():
            return "high"
        elif "moderate" in text.lower():
            return "medium"
        else:
            return "low"

def research_multi_jurisdiction(
    query: str,
    jurisdictions: list = ["federal", "ca", "ny", "tx"]
) -> dict:
    """Research a legal question across multiple jurisdictions."""

    results = {}
    client = anthropic.Anthropic()

    for jurisdiction in jurisdictions:
        message = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1500,
            messages=[{
                "role": "user",
                "content": f"""Research this question in {jurisdiction} law:
{query}

Provide:
1. Applicable statutes
2. Key cases
3. Current legal status"""
            }]
        )

        results[jurisdiction] = message.content[0].text

    return results