# 基于語義相似度的動態(tài)分塊
def semantic_chunking(text, model, threshold=0.8):
    sentences = split_into_sentences(text)
    embeddings = model.encode(sentences)
    
    chunks = []
    current_chunk = [sentences[0]]
    
    for i in range(1, len(sentences)):
        similarity = cosine_similarity(
            embeddings[i-1], embeddings[i]
        )
        if similarity < threshold:
            chunks.append(' '.join(current_chunk))
            current_chunk = [sentences[i]]
        else:
            current_chunk.append(sentences[i])
    
    return chunks

def enhance_chunk_with_context(chunk, document, llm):
    prompt = f"""
    Document: {document}
    Chunk: {chunk}
    
    Generate a brief context that explains how this chunk 
    relates to the overall document.
    """
    context = llm.generate(prompt)
    return f"Context: {context}\n\nContent: {chunk}"

class HybridRetriever:
    def __init__(self, vector_store, bm25_index):
        self.vector_store = vector_store
        self.bm25_index = bm25_index
        
    def retrieve(self, query, k=10, alpha=0.5):
        # 向量檢索
        vector_results = self.vector_store.similarity_search(
            query, k=k*2
        )
        
        # BM25檢索
        bm25_results = self.bm25_index.search(
            query, k=k*2
        )
        
        # 融合得分
        combined = self.reciprocal_rank_fusion(
            vector_results, bm25_results, alpha
        )
        
        return combined[:k]

from sentence_transformers import CrossEncoder

class Reranker:
    def __init__(self, model_name='cross-encoder/ms-marco-MiniLM-L-6-v2'):
        self.model = CrossEncoder(model_name)
    
    def rerank(self, query, documents, top_k=5):
        pairs = [[query, doc.content] for doc in documents]
        scores = self.model.predict(pairs)
        
        # 按分?jǐn)?shù)排序
        ranked_docs = sorted(
            zip(documents, scores), 
            key=lambda x: x[1], 
            reverse=True
        )
        
        return [doc for doc, _ in ranked_docs[:top_k]]

def expand_query(query, llm):
    prompt = f"""
    Original query: {query}
    
    Generate 3 alternative phrasings that capture 
    the same intent but use different keywords:
    """
    alternatives = llm.generate(prompt)
    return [query] + alternatives

def hyde_search(query, llm, retriever):
    # 生成假設(shè)答案
    hypothetical_answer = llm.generate(
        f"Answer this question: {query}"
    )
    
    # 使用假設(shè)答案檢索
    return retriever.search(hypothetical_answer)

class IncrementalIndexer:
    def __init__(self, vector_store, doc_store):
        self.vector_store = vector_store
        self.doc_store = doc_store
        self.checksums = {}
    
    def update(self, documents):
        for doc in documents:
            checksum = hashlib.md5(doc.content).hexdigest()
            
            if doc.id notin self.checksums or \
               self.checksums[doc.id] != checksum:
                # 文檔已更改，需要更新
                self.reindex_document(doc)
                self.checksums[doc.id] = checksum

def filter_low_confidence_chunks(chunks, threshold=0.7):
    return [
        chunk for chunk in chunks 
        if chunk.similarity_score > threshold
    ]

GROUNDED_PROMPT = """
You must answer ONLY based on the provided context.
If the context doesn't contain enough information, 
say "I don't have enough information to answer."

Context: {context}
Question: {question}
Answer:
"""

class FactChecker:
    def __init__(self, source_documents):
        self.sources = source_documents
        
    def verify_claim(self, claim):
        # 檢查聲明是否有文檔支持
        supporting_docs = self.find_supporting_evidence(claim)
        confidence = len(supporting_docs) / len(self.sources)
        
        return {
            'claim': claim,
            'supported': confidence > 0.5,
            'confidence': confidence,
            'evidence': supporting_docs
        }

class RAGAgent:
    def __init__(self, tools, memory, planner):
        self.tools = tools  # 包括檢索、計(jì)算、API調(diào)用等
        self.memory = memory  # 對話歷史和用戶偏好
        self.planner = planner  # 任務(wù)規(guī)劃器
        
    asyncdef process_query(self, query):
        # 任務(wù)分解
        subtasks = self.planner.decompose(query)
        
        results = []
        for task in subtasks:
            # 工具選擇
            tool = self.select_tool(task)
            # 執(zhí)行任務(wù)
            result = await tool.execute(task)
            # 更新記憶
            self.memory.update(task, result)
            results.append(result)
            
        # 綜合答案
        return self.synthesize_answer(results)