演示：将 Azure 表存储用作 Docstore¶
本指南向您展示如何使用我们基于 Azure 表存储构建的 AzureDocumentStore 和 AzureIndexStore 抽象。通过将节点放入文档存储，您可以在同一个底层文档存储上定义多个索引，而不是在索引之间复制数据。
如果您在 colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。
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%pip install matplotlib
%pip install llama-index
%pip install llama-index-embeddings-azure-openai
%pip install llama-index-llms-azure-openai
%pip install llama-index-storage-kvstore-azure
%pip install llama-index-storage-docstore-azure
%pip install llama-index-storage-index-store-azure
%pip install matplotlib %pip install llama-index %pip install llama-index-embeddings-azure-openai %pip install llama-index-llms-azure-openai %pip install llama-index-storage-kvstore-azure %pip install llama-index-storage-docstore-azure %pip install llama-index-storage-index-store-azure
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import nest_asyncio
nest_asyncio.apply()
import nest_asyncio nest_asyncio.apply()
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import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
logging.getLogger("azure.core.pipeline.policies.http_logging_policy").setLevel(
logging.WARNING
)
import logging import sys import os logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) logging.getLogger("azure.core.pipeline.policies.http_logging_policy").setLevel( logging.WARNING )
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from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
from llama_index.storage.kvstore.azure.base import ServiceMode
from llama_index.core import SimpleDirectoryReader, StorageContext from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex from llama_index.core import SummaryIndex from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding from llama_index.llms.azure_openai import AzureOpenAI from llama_index.core.response.notebook_utils import display_response from llama_index.core import Settings from llama_index.storage.kvstore.azure.base import ServiceMode
下载数据¶
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!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'
--2024-05-08 23:47:52-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 75042 (73K) [text/plain] Saving to: ‘data/paul_graham/paul_graham_essay.txt’ data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.01s 2024-05-08 23:47:52 (6.63 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]
加载文档¶
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reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
reader = SimpleDirectoryReader("./data/paul_graham/") documents = reader.load_data()
解析为节点¶
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from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
from llama_index.core.node_parser import SentenceSplitter nodes = SentenceSplitter().get_nodes_from_documents(documents)
添加到 Docstore¶
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from llama_index.storage.docstore.azure import AzureDocumentStore
from llama_index.storage.index_store.azure import AzureIndexStore
from llama_index.storage.docstore.azure import AzureDocumentStore from llama_index.storage.index_store.azure import AzureIndexStore
AzureDocumentStore 和 AzureIndexStore 类提供了几个 helper 方法，如 from_connection_string、from_account_and_key、from_sas_token、from_aad_token 等，以简化连接到我们的 Azure 表存储服务。
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storage_context = StorageContext.from_defaults(
docstore=AzureDocumentStore.from_account_and_key(
"",
"",
service_mode=ServiceMode.STORAGE,
),
index_store=AzureIndexStore.from_account_and_key(
"",
"",
service_mode=ServiceMode.STORAGE,
),
)
storage_context = StorageContext.from_defaults( docstore=AzureDocumentStore.from_account_and_key( "", "", service_mode=ServiceMode.STORAGE, ), index_store=AzureIndexStore.from_account_and_key( "", "", service_mode=ServiceMode.STORAGE, ), )
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storage_context.docstore.add_documents(nodes)
storage_context.docstore.add_documents(nodes)
如果我们导航到 Azure 表存储，现在应该能在表中看到我们的文档。
定义我们的模型¶
为了保持 Azure 的主题，我们来定义我们的 Azure OpenAI 嵌入和 LLM 模型。
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Settings.embed_model = AzureOpenAIEmbedding(
model="text-embedding-ada-002",
deployment_name="text-embedding-ada-002",
api_key="",
azure_endpoint="",
api_version="2024-03-01-preview",
)
Settings.llm = AzureOpenAI(
model="gpt-4",
deployment_name="gpt-4",
api_key="",
azure_endpoint="",
api_version="2024-03-01-preview",
)
Settings.embed_model = AzureOpenAIEmbedding( model="text-embedding-ada-002", deployment_name="text-embedding-ada-002", api_key="", azure_endpoint="", api_version="2024-03-01-preview", ) Settings.llm = AzureOpenAI( model="gpt-4", deployment_name="gpt-4", api_key="", azure_endpoint="", api_version="2024-03-01-preview", )
定义多个索引¶
每个索引都使用相同的底层节点。
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summary_index = SummaryIndex(nodes, storage_context=storage_context)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
我们现在应该能在 Azure 表存储中看到我们的 summary_index。
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vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
我们现在应该能在 Azure 表存储中看到我们的 vector_index 的条目。
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keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
keyword_table_index = SimpleKeywordTableIndex( nodes, storage_context=storage_context )
我们现在应该能在 Azure 表存储中看到我们的 keyword_table_index 的条目
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# NOTE: the docstore still has the same nodes
len(storage_context.docstore.docs)
# 注意：文档存储仍然包含相同的节点 len(storage_context.docstore.docs)
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22
测试保存和加载¶
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# NOTE: docstore and index_store are persisted in Azure Table Storage.
# NOTE: This call is only needed to persist the in-memory `SimpleVectorStore`, created by `VectorStoreIndex`, to disk.
storage_context.persist()
# 注意：文档存储和索引存储持久化在 Azure 表存储中。 # 注意：此调用仅用于将由 `VectorStoreIndex` 创建的内存中的 `SimpleVectorStore` 持久化到磁盘。 storage_context.persist()
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# note down index IDs
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
# 记下索引 IDs list_id = summary_index.index_id vector_id = vector_index.index_id keyword_id = keyword_table_index.index_id
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from llama_index.core import load_index_from_storage
# re-create storage context
storage_context = StorageContext.from_defaults(
persist_dir="./storage",
docstore=AzureDocumentStore.from_account_and_key(
"",
"",
service_mode=ServiceMode.STORAGE,
),
index_store=AzureIndexStore.from_account_and_key(
"",
"",
service_mode=ServiceMode.STORAGE,
),
)
# load indices
summary_index = load_index_from_storage(
storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
storage_context=storage_context, index_id=vector_id
)
keyword_table_index = load_index_from_storage(
storage_context=storage_context, index_id=keyword_id
)
from llama_index.core import load_index_from_storage # 重新创建存储上下文 storage_context = StorageContext.from_defaults( persist_dir="./storage", docstore=AzureDocumentStore.from_account_and_key( "", "", service_mode=ServiceMode.STORAGE, ), index_store=AzureIndexStore.from_account_and_key( "", "", service_mode=ServiceMode.STORAGE, ), ) # 加载索引 summary_index = load_index_from_storage( storage_context=storage_context, index_id=list_id ) vector_index = load_index_from_storage( storage_context=storage_context, index_id=vector_id ) keyword_table_index = load_index_from_storage( storage_context=storage_context, index_id=keyword_id )
INFO:llama_index.core.indices.loading:Loading indices with ids: ['cc88721d-b03e-4ecf-8a3d-8eba23af2f12'] Loading indices with ids: ['cc88721d-b03e-4ecf-8a3d-8eba23af2f12'] INFO:llama_index.core.indices.loading:Loading indices with ids: ['399b94e3-8661-4aef-9962-739952206466'] Loading indices with ids: ['399b94e3-8661-4aef-9962-739952206466'] INFO:llama_index.core.indices.loading:Loading indices with ids: ['f69b0db4-25c2-419a-bcab-75e4c35db96b'] Loading indices with ids: ['f69b0db4-25c2-419a-bcab-75e4c35db96b']
测试一些查询¶
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query_engine = summary_index.as_query_engine()
list_response = query_engine.query("What is a summary of this document?")
query_engine = summary_index.as_query_engine() list_response = query_engine.query("What is a summary of this document?")
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display_response(list_response)
display_response(list_response)
最终响应： 这份文档是 Paul Graham 对其多方面职业生涯的广泛反思，涵盖了他从最初的编程和艺术探索，到通过创建 Y Combinator (YC) 在创业生态系统中的重要作用。Graham 讲述了他早期对计算机的迷恋，这带来了他在编程，特别是 Lisp 方面的显著贡献，以及他通过创办 Viaweb（首批在线商店构建器之一）意外进入创业领域的旅程。这次冒险不仅标志着电子商务的一个关键时刻，也为 Graham 更深入地参与科技创业世界奠定了基础。
叙述深入探讨了 Y Combinator 的创立过程，重点介绍了其通过批次模式和夏季创始人计划（Summer Founders Program）来资助和支持初创企业的创新方法。该计划旨在通过提供种子资金和指导来培育新的初创企业。格雷厄姆分享了关于 YC 挑战和成功的见解，包括其在资助 Reddit 和 Twitch 等知名初创企业方面的作用，并讨论了促使他最终退出 YC 转而追求其他兴趣（包括重拾写作和编程）的个人成长和感悟。
query_engine = vector_index.as_query_engine() vector_response = query_engine.query("What did the author do growing up?")
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query_engine = vector_index.as_query_engine()
vector_response = query_engine.query("What did the author do growing up?")
display_response(vector_response)
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display_response(vector_response)
最终响应： 作者在成长过程中，除了上学，还进行写作和编程。最初，他写短篇小说，他形容这些小说缺乏情节，但充满了情感强烈的角色。他最初的编程尝试是在 IBM 1401 上进行的，使用早期版本的 Fortran，由于当时技术的限制，他遇到了挑战。后来，随着微型计算机的出现，作者的编程活动得以扩展，他编写了简单的游戏、预测模型火箭飞行的程序，以及一个他父亲使用的文字处理器。query_engine = keyword_table_index.as_query_engine() keyword_response = query_engine.query( "What did the author do after his time at YC?" )
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query_engine = keyword_table_index.as_query_engine()
keyword_response = query_engine.query(
"What did the author do after his time at YC?"
)
display_response(keyword_response)
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display_response(keyword_response)
最终响应： 在离开 Y Combinator (YC) 后，作者决定追求绘画，想看看如果他全身心投入，能达到什么水平。他把接下来大部分时间花在绘画上，取得了他所希望的水平，虽然不如预期高，但比以前好了。然而，到了十一月，他对绘画失去了兴趣，便停了下来。随后，他恢复了散文写作，在接下来的几个月里写了不少新文章，其中一些并非关于初创公司。2015 年 3 月，他又开始研究 Lisp，专注于它作为一门通过自身编写解释器来定义的语言的核心。返回顶部
