怎样使用ChatGPT创建嵌入：ChatGPT嵌入教程

I. 嵌入概述

A. 嵌入的定义和作用

B. ChatGPT中的嵌入

II. 创建嵌入的方法

A. 深度神经网络模型

1. 使用大型数据集进行训练

2. 建立修改版本的模型

B. ChatGPT API与文本嵌入结合

1. 为电子商务业务构建个性化产品推荐聊天机器人

2. 使用OpenAI嵌入端点回答问题

III. ChatGPT嵌入的利用

A. 文本表示与语义捕捉

B. 信息检索与问答

IV. ChatGPT嵌入教程示例

A. 网络爬虫和数据转换

1. 建立网络爬虫

2. 数据转换为嵌入

B. 答案生成与查询

1. 使用嵌入划分语境块

2. 根据查询找到相应的语境块

V. ChatGPT嵌入的进一步发展

A. 嵌入技术的未来趋势

B. 嵌入在生成型人工智能中的利用

New Outline:

1. Introduction
2. What are Embeddings?
3. OpenAI API Endpoint for Embeddings
4. Creating Embeddings for Product and Customer Profile Datasets
5. Using Embeddings to Find Similarities
6. Creating a ChatGPT API Prompt
7. Generating ChatGPT Product Recommendations
8. Improvements
9. Troubleshooting
10. Next Steps

Introduction

Are you interested in building a chatbot that can provide personalized product recommendations to your customers based on their unique profiles? This step-by-step guide will show you how to build a chatbot using embeddings to match a user’s profile with relevant products from a company’s database. By following this guide, you will have the tools you need to create a customer-facing chatbot that can boost engagement and drive sales. While the guide uses a beauty e-commerce company as an example, the principles can be applied to any industry.

If you need tailored AI solutions for your business, consider consulting services for one-on-one collaboration and custom AI services.

What are Embeddings?

Embeddings, in the context of natural language processing (NLP), represent words, phrases, or even entire documents as dense vectors of numerical values. These vectors are designed to capture the semantic and syntactic relationships between different pieces of text data and are often high-dimensional. Embeddings are created using neural networks trained on large amounts of text data, where each word is mapped to a dense vector based on its meaning and context. For example, the words “car” and “vehicle” may be mapped to similar vectors because they have similar meanings and are used in similar contexts. In this guide, we will generate embeddings for each user profile and product in the database to calculate their similarity and find the best product matches for each user.

OpenAI API Endpoint for Embeddings

The OpenAI API provides two different endpoints for working with embeddings: search and similarity. The choice of which endpoint to use depends on your specific use case and task. The search endpoint is used to find documents or snippets that are similar to a given input text. It returns a list of search results, each with a document ID, score, and text. The score measures the similarity between the input text and the matched document. On the other hand, the similarity endpoint is used to measure the similarity between two snippets of text or documents. It returns a single score between 0 and 1, where 0 indicates complete dissimilarity and 1 indicates identical texts. In this guide, we will use the similarity endpoint to measure the similarity between a user’s chat message and the product database to provide relevant recommendations based on their profile.

Creating Embeddings for Product and Customer Profile Datasets

Before we dive into creating embeddings, we need to retrieve our OpenAI API keys. Visit the OpenAI website, log in to your account, and navigate to the API section to view and create your API keys. Once you have your API key, store it securely as we will need it later for authentication.

Now, let’s start by creating a product dataset. Import the necessary libraries, including OpenAI, pandas, and the OpenAI embeddings module. Set your API key and initialize the OpenAI API with your key. Then, create a product dataset using a list of dictionaries containing product information such as the product ID, name, brand, and description. Convert the list to a pandas DataFrame for easier manipulation. Create a combined column in the DataFrame by concatenating the brand, product, and description. Finally, generate embeddings for the combined column using the get_embedding function from the OpenAI embeddings module.

Similarly, create a customer profile dataset using a list of dictionaries representing the customer’s previous purchases or product interests. Convert the list to a pandas DataFrame and create a combined column. Generate embeddings for the combined column to represent the customer profile.

Using Embeddings to Find Similarities

Now that we have embeddings for the product dataset and the customer profile dataset, we can use them to calculate similarities and find the best product matches for each user. First, calculate the similarity between the user’s previous purchases and their chat message using the cosine similarity function. Sort the customer order DataFrame in descending order based on the similarity score to get the most similar products. Next, calculate the similarity between the user’s chat message and the products in the database using the cosine similarity function. Again, sort the product DataFrame in descending order based on the similarity score to find the most relevant products. Create new DataFrames with the top three similarity scores for both the previous purchases and the product database.

Creating a ChatGPT API Prompt

The next step is to construct a ChatGPT API prompt to initiate the conversation with the chatbot. The prompt consists of a series of message objects, where each object has a role (system, user, or assistant) and content (the content of the message). Start by defining the system message to set the behavior of the assistant. Then, add the user’s input message, followed by any additional instructions or preferences for the assistant. For example, you can ask the assistant to provide a detailed explanation of its recommendations or to be friendly in its responses. Finally, provide an example of the desired behavior by adding the assistant’s message and the top three product recommendations as assistant content.

Generating ChatGPT Product Recommendations

Now that we have our prompt ready, we can call the ChatGPT API using the prompt and receive the AI-generated response. Use the prompt and the openai.ChatCompletion.create function to generate the response from the model. Extract the response content and display it to the user. The response will contain the chatbot’s product recommendations based on the user’s profile and input question.

Improvements

While this guide provides a solid foundation for building a chatbot that recommends products based on a user’s profile and available product data, there are several areas that can be improved for more accurate and relevant recommendations.

• Product data: Use a more extensive dataset with detailed product information to improve the accuracy of the recommendations.
• Increase the number of previously purchased products: Include a larger set of previously purchased products to broaden the scope of the recommendations.
• Extract the product type from the user input: Extract the specific product type or category from the user’s input question to provide more relevant recommendations.

Troubleshooting

• AttributeError: module ‘openai’ has no attribute ‘ChatCompletion’: This error occurs when the version of your Python client library for the OpenAI API is lower than 0.27.0. Update the library to the latest version by running pip install openai --upgrade in your terminal.
• InvalidRequestError: This model’s maximum context length is 4096 tokens: This error indicates that the input message objects sent to the ChatGPT API exceed the maximum allowed length of 4096 tokens. To resolve this issue, shorten the length of your messages by reducing the number of tokens or removing unnecessary content.

Next Steps

1. Visit the GitHub repository for the source code and a Jupyter notebook with all the code from this guide.
2. Contact [email protected] for help getting started with the ChatGPT API or any other questions you may have.

问题1：ChatGPT中的嵌入是甚么？

答案：嵌入是指将文本表示为高维数值向量的技术。在ChatGPT中，嵌入用于将文本转化为这些向量，以捕捉语义和句法特点。嵌入模型通常是在大型数据集上训练的深度神经网络的改进版本。它们有助于将文本映照到向量空间中的特定位置，使得可以对其进行进一步处理和分析。

• ChatGPT使用嵌入来将文本表示成高维数值向量。
• 嵌入模型通常是训练好的深度神经网络的改进版本。
• 嵌入向量可以捕捉语义和句法特点，可以用于进一步处理和分析文本。

问题2：怎样使用ChatGPT API来构建产品推荐聊天机器人？

答案：使用ChatGPT API和文本嵌入技术，您可以为电子商务业务构建一个个性化的产品推荐聊天机器人。以下是构建进程的步骤：

1. 通过ChatGPT API与用户进行实时对话。
2. 将用户提供的信息作为输入，使用ChatGPT的文本嵌入功能将其转化为嵌入向量。
3. 使用嵌入向量与产品数据集中的向量进行类似度比较，找到最匹配的产品。
4. 根据找到的产品生成相应的推荐和建议。
5. 通过ChatGPT API将结果返回给用户。

通过以上步骤，您可以构建一个具有个性化产品推荐功能的聊天机器人，提供更准确和有针对性的产品推荐服务。

问题3：ChatGPT中的嵌入在ChatGPT-like模型中扮演甚么角色？

答案：在ChatGPT-like模型中，嵌入的角色非常重要。它们用于将文本转化为向量表示，从而使模型能够理解和处理文本数据。以下是嵌入在ChatGPT-like模型中的关键角色：

• 将文本数据转化为向量表示，可以进行进一步的处理和分析。
• 嵌入向量可以捕捉文本的语义和句法特点，有助于模型理解上下文。
• 嵌入向量可以用于计算文本之间的类似度，从而进行语义匹配和查找相关信息。