pip install langchain-moorcheh

from langchain_moorcheh import MoorchehVectorStore
from moorcheh_sdk import MoorchehClient

import logging
import os
from uuid import uuid4
import asyncio
from typing import Any, List, Optional, Literal, Tuple, Type, TypeVar, Sequence
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from google.colab import userdata

MOORCHEH_API_KEY = os.environ['MOORCHEH_API_KEY']

namespace = "your_namespace_name"
namespace_type = "text" # 或 vector
store = MoorchehVectorStore(
            api_key=MOORCHEH_API_KEY,
            namespace=namespace,
            namespace_type=namespace_type
        )

document_1 = Document(
    page_content="冲泡了一杯新鲜的埃塞俄比亚咖啡，并搭配了一个温热的羊角面包。",
    metadata={"source": "blog"},
)

document_2 = Document(
    page_content="明天的天气将是晴朗，有微风，最高气温将达到 78°F。",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="正在尝试使用 LangChain 构建一个 AI 驱动的笔记助手！",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="当地面包店向社区食品银行捐赠了 500 条面包。",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="昨晚的音乐会绝对令人难忘——多么精彩的表演！",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="查看我们的最新文章：在家办公时提高生产力的 5 种方法。",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="掌握自制披萨面团的终极指南。",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph 让多智能体工作流变得简单多了——真的令人印象深刻！",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="由于主要出口国意外削减供应，今日油价上涨了 3%。",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="我真希望这篇帖子不会消失在数字虚空中……",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]

uuids = [str(uuid4()) for _ in range(len(documents))]

store.add_documents(documents=documents, ids=uuids)

store.delete(ids=["chunk_id_here"])

query = "给我一个所提供文档的简要摘要"
answer = store.generative_answer(query, ai_model = "anthropic.claude-sonnet-4-6")
print(answer)