使用 Chroma 进行嵌入搜索

本笔记本将引导您完成一个简单的流程，下载数据、对其进行嵌入，然后使用精选的向量数据库对其进行索引和搜索。这是客户在安全环境中存储和搜索我们的嵌入以支持生产用例（如聊天机器人、主题建模等）的常见需求。

什么是向量数据库

向量数据库是一种用于存储、管理和搜索嵌入向量的数据库。近年来，由于人工智能在解决涉及自然语言、图像识别和其他非结构化数据形式的用例方面越来越有效，因此使用嵌入将非结构化数据（文本、音频、视频等）编码为供机器学习模型使用的向量已呈爆炸式增长。向量数据库已成为企业交付和扩展这些用例的有效解决方案。

为什么使用向量数据库

向量数据库使企业能够利用我们在此仓库中共享的许多嵌入用例（例如，问答、聊天机器人和推荐服务），并在安全、可扩展的环境中使用它们。我们的许多客户在小规模范围内使用嵌入来解决他们的问题，但性能和安全性阻碍了他们投入生产——我们将向量数据库视为解决此问题的关键组成部分，在本指南中，我们将介绍嵌入文本数据、将其存储在向量数据库中以及使用它进行语义搜索的基础知识。

演示流程

演示流程如下：

• 设置：导入包并设置任何必需的变量
• 加载数据：加载数据集并使用 OpenAI 嵌入对其进行嵌入
• Chroma：
  • 设置：在这里我们将设置 Chroma 的 Python 客户端。有关更多详细信息，请访问此处
  • 索引数据：我们将为__标题__和__内容__创建带有向量的集合
  • 搜索数据：我们将运行几次搜索以确认其正常工作

运行完此笔记本后，您应该对如何设置和使用向量数据库有一个基本的了解，然后可以继续进行更多复杂的用例，利用我们的嵌入。

设置

导入所需的库并设置我们要使用的嵌入模型。

# Ensure the OpenAI library is installed
%pip install openai

# We'll need to install the Chroma client
%pip install chromadb

# Install wget to pull zip file
%pip install wget

# Install numpy for data manipulation
%pip install numpy

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