Astra DB¶

DataStax Astra DB 是一个构建在Apache Cassandra上的无服务器向量数据库，通过易于使用的JSON API访问。

要运行这个笔记本，您需要在云中运行一个DataStax Astra DB实例（您可以在datastax.com免费获取一个）。

您应该确保已安装llama-index和astrapy：

In [ ]:

%pip install llama-index-vector-stores-astra-db

%pip install llama-index-vector-stores-astra-db

In [ ]:

!pip install llama-index
!pip install "astrapy>=0.6.0"

!pip install llama-index !pip install "astrapy>=0.6.0"

请提供数据库连接参数和密码：¶

In [ ]:

import os
import getpass

api_endpoint = input(
    "\nPlease enter your Database Endpoint URL (e.g. 'https://4bc...datastax.com'):"
)

token = getpass.getpass(
    "\nPlease enter your 'Database Administrator' Token (e.g. 'AstraCS:...'):"
)

os.environ["OPENAI_API_KEY"] = getpass.getpass(
    "\nPlease enter your OpenAI API Key (e.g. 'sk-...'):"
)

import os import getpass api_endpoint = input( "\nPlease enter your Database Endpoint URL (e.g. 'https://4bc...datastax.com'):" ) token = getpass.getpass( "\nPlease enter your 'Database Administrator' Token (e.g. 'AstraCS:...'):" ) os.environ["OPENAI_API_KEY"] = getpass.getpass( "\nPlease enter your OpenAI API Key (e.g. 'sk-...'):" )

导入所需的包依赖项：¶

In [ ]:

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    StorageContext,
)
from llama_index.vector_stores.astra_db import AstraDBVectorStore

from llama_index.core import ( VectorStoreIndex, SimpleDirectoryReader, StorageContext, ) from llama_index.vector_stores.astra_db import AstraDBVectorStore

加载一些示例数据：¶

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'

读取数据：¶

In [ ]:

# 加载文档documents = SimpleDirectoryReader("./data/paul_graham/").load_data()print(f"总文档数：{len(documents)}")print(f"第一个文档，id：{documents[0].doc_id}")print(f"第一个文档，哈希值：{documents[0].hash}")print(    "第一个文档，文本"    f"（{len(documents[0].text)} 个字符）：\n{'='*20}\n{documents[0].text[:360]} ...")

# 加载文档documents = SimpleDirectoryReader("./data/paul_graham/").load_data()print(f"总文档数：{len(documents)}")print(f"第一个文档，id：{documents[0].doc_id}")print(f"第一个文档，哈希值：{documents[0].hash}")print( "第一个文档，文本" f"（{len(documents[0].text)} 个字符）：\n{'='*20}\n{documents[0].text[:360]} ...")

创建Astra DB向量存储对象：¶

In [ ]:

astra_db_store = AstraDBVectorStore(
    token=token,
    api_endpoint=api_endpoint,
    collection_name="astra_v_table",
    embedding_dimension=1536,
)

astra_db_store = AstraDBVectorStore( token=token, api_endpoint=api_endpoint, collection_name="astra_v_table", embedding_dimension=1536, )

从文档中构建索引：¶

In [ ]:

storage_context = StorageContext.from_defaults(vector_store=astra_db_store)

index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

storage_context = StorageContext.from_defaults(vector_store=astra_db_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

使用索引进行查询：¶

In [ ]:

query_engine = index.as_query_engine()
response = query_engine.query("Why did the author choose to work on AI?")

print(response.response)

query_engine = index.as_query_engine() response = query_engine.query("Why did the author choose to work on AI?") print(response.response)