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 [ ]:

%pip install llama-index-vector-stores-weaviate

%pip install llama-index-vector-stores-weaviate

In [ ]:

!pip install llama-index

!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"]

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))

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

In [ ]:

import weaviate

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()

# 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

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 [ ]:

!mkdir -p 'data/paul_graham/'
!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'

!mkdir -p 'data/paul_graham/' !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()

# 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)

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？")

# 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>"))

display(Markdown(f"{response}"))

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()

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)

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>"))

# 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"{response}"))

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])

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

In [ ]:

loaded_index.insert(doc)

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>"))

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"{response}"))

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()

vector_store.delete_index()

In [ ]:

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

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()

client.close()