AnalyticDB¶
AnalyticDB for PostgreSQL 是一款面向在线海量数据分析的大规模并行处理(MPP)数据仓库服务。
运行此笔记本需要您在云端部署一个 AnalyticDB for PostgreSQL 实例(可通过阿里云通用购买页面获取)。
创建实例后,您需要通过API或在实例详情页面的"账号管理"功能创建管理员账户。
请确保已安装 llama-index
:
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%pip install llama-index-vector-stores-analyticdb
%pip install llama-index-vector-stores-analyticdb
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!pip install llama-index
!pip install llama-index
请提供参数:¶
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import os
import getpass
# alibaba cloud ram ak and sk:
alibaba_cloud_ak = ""
alibaba_cloud_sk = ""
# instance information:
region_id = "cn-hangzhou" # region id of the specific instance
instance_id = "gp-xxxx" # adb instance id
account = "test_account" # instance account name created by API or 'Account Management' at the instance detail web page
account_password = "" # instance account password
import os
import getpass
# alibaba cloud ram ak and sk:
alibaba_cloud_ak = ""
alibaba_cloud_sk = ""
# instance information:
region_id = "cn-hangzhou" # region id of the specific instance
instance_id = "gp-xxxx" # adb instance id
account = "test_account" # instance account name created by API or 'Account Management' at the instance detail web page
account_password = "" # instance account password
导入所需依赖包:¶
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from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
)
from llama_index.vector_stores.analyticdb import AnalyticDBVectorStore
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
)
from llama_index.vector_stores.analyticdb import AnalyticDBVectorStore
加载示例数据:¶
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!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/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/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
读取数据:¶
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# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(f"Total documents: {len(documents)}")
print(f"First document, id: {documents[0].doc_id}")
print(f"First document, hash: {documents[0].hash}")
print(
"First document, text"
f" ({len(documents[0].text)} characters):\n{'='*20}\n{documents[0].text[:360]} ..."
)
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(f"Total documents: {len(documents)}")
print(f"First document, id: {documents[0].doc_id}")
print(f"First document, hash: {documents[0].hash}")
print(
"First document, text"
f" ({len(documents[0].text)} characters):\n{'='*20}\n{documents[0].text[:360]} ..."
)
创建 AnalyticDB 向量存储对象:¶
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analytic_db_store = AnalyticDBVectorStore.from_params(
access_key_id=alibaba_cloud_ak,
access_key_secret=alibaba_cloud_sk,
region_id=region_id,
instance_id=instance_id,
account=account,
account_password=account_password,
namespace="llama",
collection="llama",
metrics="cosine",
embedding_dimension=1536,
)
analytic_db_store = AnalyticDBVectorStore.from_params(
access_key_id=alibaba_cloud_ak,
access_key_secret=alibaba_cloud_sk,
region_id=region_id,
instance_id=instance_id,
account=account,
account_password=account_password,
namespace="llama",
collection="llama",
metrics="cosine",
embedding_dimension=1536,
)
从文档构建索引:¶
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storage_context = StorageContext.from_defaults(vector_store=analytic_db_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
storage_context = StorageContext.from_defaults(vector_store=analytic_db_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
使用索引进行查询:¶
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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)
删除集合:¶
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analytic_db_store.delete_collection()
analytic_db_store.delete_collection()