Wenxin intelligent body mounted commodity link cash practical tutorials

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Based on the Wenshin Intelligent Body platform, combined with the latest DeepSeek The book recommendation assistant developed by the model is able to make intelligent commodity recommendations based on the content of user conversations, realize accurate conversion and transaction realization, and build a closed loop of business.

This tutorial will provide an in-depth analysis of the development practices of the DeepSeek Book Recommendation Assistant, and help readers master the new method of realizing text-centric intelligences.

I. Creative Conceptualization

1. Why DeepSeek in combination with commodity mounting?

The DeepSeek model has a powerful chain of thought, which can provide insights into the user's needs, personality and age group during the dialog with the user, so as to recommend more accurate books for the user according to the actual situation. Even users who are not good at writing prompts can use DeepSeek's chain of thought to effectively plan intelligent body tasks.

2. Model selection strategy

In terms of model selection, developers can choose according to the application scenarios and needs of the intelligences:

✅ Models with chains of thought, e.g. DeepSeek-R1

• Applicable Scenarios: For scenarios that require deep thinking, such as code assistance, text creation, scientific research and analysis, strategy planning, and homework problem solving.
• dominance: enables deeper thinking and is usually superior to the quick-thinking model in terms of response effectiveness.

✅ Models that do not have a chain of thought, such as the Bunshin Big Model series

• Applicable Scenarios: For scenarios that require a quick response, such as customer service Q&A, content summarization, and real-time chat.
• dominance: Quick to answer and able to summarize content or give a response quickly.

II. Intelligent Body Configuration Details

1. Steps for rapid creation of intelligences

1. Visit the official website of the Wenxin Intelligent Body Platform (https://agents.baidu.com/center) and click the [Create Intelligent Body] button on the left navigation bar.

Click on Create Intelligence

2. Fill in the intelligent body'sName and settings. For example, the setting could read, "Your main task is to help users recommend appropriate books. You need to recommend books that match the user's taste according to their interests and needs. You can do this by asking the user what type of books they prefer, or by referring to their previous reading history. Please keep a friendly and professional tone when replying, and add emoji emoticons as appropriate to liven up the conversation." After filling out the form, click the [Create Now] button.

Fill in the name and settings

Create an Intelligent Body Now

3. After completing the above steps, the basic configuration of the smart body can be quickly generated, including the introduction, prompt words, opening text and questions, as well as other basic configuration items. After that, the developer can adjust the smart body configuration according to the actual needs.

2. Detailed description of the configuration of the smart body

1) Intelligent body name: DeepSeek book recommendation assistant

• request: The name should clearly and visually reflect the theme or function of the smartbody, front-loading DeepSeek and highlighting the model highlights.
• goal: Reduce user awareness costs and quickly attract user interest.

2) Synopsis: This is an intelligent assistant that specializes in recommending books for users.

• request: A succinct and accurate description of the core functions of the intelligences, with the possibility of expanding the profile as appropriate.
• goal: To enable users to quickly understand the specialized areas and functions of intelligences.

3) Persona and reply logic:

**# Role specification**

As a Book Recommendation Assistant, your main responsibility is to recommend appropriate books for users. You need to recommend books that match users' tastes based on their interests and needs. You can make book recommendations by asking users about their preferred book genres, or by referring to their previous reading history.

**# Thinking about the specification**

1. proactively ask users about their interests or preferences, such as their favorite book categories. If the user has not provided explicit information, you can first make random recommendations and request relevant information from the user at the end of the response. If the user has expressed a clear preference, make a recommendation directly.
2. If the user does not have a clear preference, you can first make random recommendations, or dig out the types of books they may be interested in through dialog exchanges with the user.
3. When recommending books, we need to consider the reading level and interest level of the users and recommend books that suit their reading ability and interest points. 4.
4. If users feedback that they are not interested in the recommended books, we need to reanalyze their interests and try to recommend other types of books.

**# Replies to specification**

1. maintain a friendly and professional tone when replying, and add emoji expressions in the reply to create an active dialog atmosphere. 2. provide books in the reply.
2. Provide a brief description of the book, the reason for the recommendation, and other valuable additional information in the reply to help users better understand the content of the book. 3.
3. if the user is not interested in the recommended book, you can further ask the user about his/her interest and preference in the reply. 4.
4. at the end of each reply, you can ask the user if they need further help or if they have any other questions.

• definePersona and Response Logic refers to Prompts or Instructions. The developer needs to guide the AI macromodel to accomplish a specific task through specific prompts (Prompts). Prompts usually contain:# Role Specification (Describe the roles and functions of intelligences),# Thinking Norms (Guided modeling thought process),# Response Specification (guides how the model organizes and optimizes the final output).
• corresponds English -ity, -ism, -ization: The quality of the Prompt directly determines the final result of the intelligence. Prompts are input prompts that guide AI models to generate text, images, or code. Through clear and effective prompts, the requirements of the generated results can be clarified, so that the models can generate results more in line with the user's needs.
• finesse::
  • Use structured language for presentation, avoid vague descriptions, and clearly state specific needs.
  • Try to avoid industry jargon in your instructions to minimize the likelihood of comprehension barriers for the AI Big Model.
  • You can borrow and reuse the sentence structure in the above instructions, such as "Ask ......, for example ....... If there is no ......, you can first ...... and finally request ......" and so on, and continuously adjust and optimize the prompt words according to the actual application effect.

4) Opening statement:

Mobile effect

Computer effect

• opening speech (grammar): It is recommended to adopt a friendly tone and add emoji expressions to the copy to enhance the interactivity and fun of the smart body. At the same time, the copy needs toconcise and unambiguous intelligent Core functions and roles, guiding the user in a clear direction of questioning.
• Opening questions: Select questions to which intelligentsia can give high-quality responses and questions that meet the needs of a wider range of users, highlighting the characteristics and strengths of intelligentsia.

5) Model Setting: Select DeepSeek-R1-32B model

The model setup path is as follows:

🙋Why choose the DeepSeek-R1-32B over the full-blooded DeepSeek-R1❓?

Model selection needs to be adapted according to the specific application scenarios of the intelligences. For example, for the book recommendation assistant, its core function is to recommend books according to the user's needs, and the requirements for model performance are relatively moderate. Considering the limited model resources, the DeepSeek-R1-32B model can already meet the functional requirements of the intelligent body.

⭐️ 'DeepSeek' series of model profiles:

3、Commercialization Practice: Commodity Mounted Commission Sharing

1) DefinitionsThe Wenshin Intelligent Body platform provides developers with access to a library of more than 700 million products from four major e-commerce platforms, namely Jingdong, Taobao, Jinduo, and Degree Goods. Developers can mount relevant products for sale through the smart body and get the corresponding commodity sales commission.

❗️take note ofThe product mounting function is divided into two modes: [AI auto-mount] and [manual mount]. It is recommended that developers choose [AI auto-mount] and check all platforms to get better results.AI auto-mount can cover all the products of the three platforms of Pinduoduo, Jingdong and Duoyao, which is easier to operate, covers a wider range of products, and displays the products more accurately.

2) Procedure: After clicking the "AI Commodity Mount" function, the system will pop up the commodity setting window. In the selection of commodity platform, check the three platforms, and select the category of "Books and Education" (or other categories as needed).

AI Mount Function

Select "Books & Education" for the product category

❗️ special hintIf you want to distribute goods from Jinduo and Jingdong platforms, you need to select the platform in advance and complete the platform-associated PID binding. Developers can long press and recognize the QR code below to see the detailed binding tutorial👇.

Documentation Tutorials

video tutorial

PID Definition: PID refers to "Promotion ID", which is the unique identification ID generated in the third-party affiliate platform.PID represents the identity of the promoter and points to the account of the promoter, which is the code used to identify the identity of the promoter. The PIDs of different e-commerce platforms are different, so it is necessary to generate the exclusive PID in the corresponding e-commerce platform and bind it in the background of Wenxin Intelligent Body Platform. After completing the PID binding, you can mount the commodity card in the intelligent body to promote the commodity and get the corresponding sales revenue.

3) Example of the effect of a commodity recall: