向量存储

# 文档处理时写入
docs_to_add = [
    LCDocument(
        page_content="分块内容...",
        metadata={
            "user_id": "user_123",
            "knowledge_base_id": "kb_456",
            "document_id": "doc_789",
            "chunk_index": 0,
            "source": "example.pdf",
            "page": 1,
            "created_at": "2025-01-01T00:00:00",
        }
    )
]
chunk_ids = vectorstore.add_documents(docs_to_add)

# 向量相似度搜索 + 元数据过滤
results = vectorstore.similarity_search_with_score(
    query="如何配置 Zeus?",
    k=5,
    filter={
        "user_id": "user_123",
        "knowledge_base_id": "kb_456",
    },
)

# 删除文档的所有分块
vectorstore.delete(filter={"document_id": "doc_789"})

# 删除知识库的所有数据
vectorstore.delete(filter={"knowledge_base_id": "kb_456"})