#!/usr/bin/env python
# coding: utf-8

# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/WeaviateIndexDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"/></a>
# 

# # Weaviate向量存储
# 

# 如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。
# 

# In[ ]:


get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')


# In[ ]:


get_ipython().system('pip install llama-index')


# #### 创建一个Weaviate客户端
# 

# In[ ]:


import os
import openai

os.environ["OPENAI_API_KEY"] = ""
openai.api_key = os.environ["OPENAI_API_KEY"]


# In[ ]:


import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))


# In[ ]:


import weaviate


# In[ ]:


# 云端
cluster_url = ""
api_key = ""

client = weaviate.connect_to_wcs(
    cluster_url=cluster_url,
    auth_credentials=weaviate.auth.AuthApiKey(api_key),
)

# 本地
# client = connect_to_local()


# #### 加载文档，构建VectorStoreIndex
# 

# In[ ]:


from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from IPython.display import Markdown, display


# 下载数据
# 

# In[ ]:


get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")


# In[ ]:


# 加载文档
documents = SimpleDirectoryReader("./data/paul_graham").load_data()


# In[ ]:


from llama_index.core import StorageContext

# 如果你想以后加载索引，请确保给它一个名称！
vector_store = WeaviateVectorStore(
    weaviate_client=client, index_name="LlamaIndex"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# 注意：你也可以选择手动定义一个索引名称。
# index_name = "test_prefix"
# vector_store = WeaviateVectorStore(weaviate_client=client, index_name=index_name)


# #### 查询索引
# 

# In[ ]:


# 将日志级别设置为DEBUG，以获得更详细的输出
query_engine = index.as_query_engine()
response = query_engine.query("作者在成长过程中做了什么？")


# In[ ]:


display(Markdown(f"<b>{response}</b>"))


# ## 加载索引
# 
# 在这里，我们使用与创建初始索引时相同的索引名称。这样可以阻止它被自动生成，并使我们能够轻松地重新连接到它。
# 

# In[ ]:


cluster_url = ""
api_key = ""

client = weaviate.connect_to_wcs(
    cluster_url=cluster_url,
    auth_credentials=weaviate.auth.AuthApiKey(api_key),
)

# 本地
# client = weaviate.connect_to_local()


# In[ ]:


vector_store = WeaviateVectorStore(
    weaviate_client=client, index_name="LlamaIndex"
)

loaded_index = VectorStoreIndex.from_vector_store(vector_store)


# In[ ]:


# 将日志级别设置为DEBUG，以获得更详细的输出
query_engine = loaded_index.as_query_engine()
response = query_engine.query("What happened at interleaf?")
display(Markdown(f"<b>{response}</b>"))


# ## 元数据过滤
# 
# 让我们插入一个虚拟文档，并尝试进行过滤，以便只返回该文档。
# 

# In[ ]:


from llama_index.core import Document

doc = Document.example()
print(doc.metadata)
print("-----")
print(doc.text[:100])


# In[ ]:


loaded_index.insert(doc)


# In[ ]:


from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="filename", value="README.md")]
)
query_engine = loaded_index.as_query_engine(filters=filters)
response = query_engine.query("What is the name of the file?")
display(Markdown(f"<b>{response}</b>"))


# # 完全删除索引
# 
# 您可以使用`delete_index`函数删除向量存储创建的索引。
# 

# In[ ]:


vector_store.delete_index()


# In[ ]:


vector_store.delete_index()  # 再次调用该函数不会有任何作用


# # 连接终止
# 

# 您必须确保关闭客户端连接：
# 

# In[ ]:


client.close()