import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel('gemini-1.5-pro')

def analyze_large_document(document_path: str, query: str) -> str:
    """大規模なドキュメントを特定のクエリで分析します。"""
    with open(document_path, 'r') as f:
        content = f.read()

    prompt = f"""
    Document:
    {content}

    Query: {query}

    Please analyze the document and answer the query comprehensively.
    """

    response = model.generate_content(prompt)
    return response.text

def create_structured_context(documents: list) -> str:
    """複数のドキュメントから構造化されたコンテキストを作成します。"""
    context_parts = []

    for i, doc in enumerate(documents):
        context_parts.append(f"""
=== DOCUMENT {i+1}: {doc['title']} ===
Source: {doc['source']}
Date: {doc['date']}
Content:
{doc['content']}
=== END DOCUMENT {i+1} ===
""")

    return "\n\n".join(context_parts)

def build_hierarchical_context(data: dict) -> str:
    """複雑なデータ構造に対して階層的なコンテキストを構築します。"""
    template = """
# Project Overview
{overview}

## Architecture
{architecture}

## Components
{components}

## Dependencies
{dependencies}

## Recent Changes
{changes}
"""
    return template.format(**data)

def count_tokens(text: str) -> int:
    """Geminiのトークナイザーを使用してテキスト内のトークンを数えます。"""
    model = genai.GenerativeModel('gemini-1.5-pro')
    return model.count_tokens(text).total_tokens

def check_context_fit(documents: list, max_tokens: int = 1000000) -> bool:
    """ドキュメントがコンテキストウィンドウ内に収まるか確認します。"""
    total = sum(count_tokens(doc) for doc in documents)
    return total < max_tokens

def compress_context(content: str, target_ratio: float = 0.5) -> str:
    """重要な情報を保持しながらコンテンツを圧縮します。"""
    prompt = f"""
    Summarize the following content, preserving:
    - Key facts and figures
    - Important decisions and conclusions
    - Technical specifications
    - Action items

    Target compression: {int(target_ratio * 100)}% of original

    Content:
    {content}
    """

    response = model.generate_content(prompt)
    return response.text

def analyze_codebase(repo_path: str) -> str:
    """コードベース全体を一度に分析します。"""
    files = collect_code_files(repo_path)

    context = "# CODEBASE ANALYSIS\n\n"
    for file_path, content in files.items():
        context += f"## File: {file_path}\n```\n{content}\n```\n\n"

    prompt = f"""
    {context}

    Analyze this codebase and provide:
    1. Architecture overview
    2. Main components and their responsibilities
    3. Code quality assessment
    4. Potential improvements
    5. Security considerations
    """

    response = model.generate_content(prompt)
    return response.text

def compare_documents(docs: list, comparison_criteria: list) -> str:
    """指定された基準に基づいて複数のドキュメントを比較します。"""
    context = create_structured_context(docs)

    criteria_str = "\n".join(f"- {c}" for c in comparison_criteria)

    prompt = f"""
    {context}

    Compare all documents above based on these criteria:
    {criteria_str}

    Provide a detailed comparison table and analysis.
    """

    response = model.generate_content(prompt)
    return response.text

import hashlib
from functools import lru_cache

\@lru_cache(maxsize=100)
def cached_analysis(content_hash: str, query: str) -> str:
    """コンテンツハッシュに基づいて分析結果をキャッシュします。"""
    # ハッシュでコンテンツを取得して分析
    pass

def analyze_with_cache(content: str, query: str) -> str:
    """キャッシュを使用してコンテンツを分析します。"""
    content_hash = hashlib.md5(content.encode()).hexdigest()
    return cached_analysis(content_hash, query)

def stream_analysis(context: str, query: str):
    """長い出力に対して分析結果をストリーミングします。"""
    prompt = f"{context}\n\nQuery: {query}"

    response = model.generate_content(
        prompt,
        stream=True
    )

    for chunk in response:
        yield chunk.text