Complete Coze tutorial: Building an AI chatbot from scratch

This tutorial offers a comprehensive guide to building AI chatbots using the Co platform. It introduces the Co bot store, where users can find community-created bots for inspiration or use. The guide then explains how to create a chatbot from scratch, starting with naming the bot and utilizing large language models (LLMs) for understanding context. It highlights features like knowledge bases, workflows, plugins, and card bindings that enhance the bot's capabilities. The process includes designing the bot's persona, adding skills, and customizing the bot's response behavior through variables and databases. The tutorial also covers testing the bot, using plugins to access external services like Yelp and Trip Advisor, and leveraging workflows for multi-step processes. Additionally, it discusses personalization through variables and knowledge bases, which allow the bot to remember user preferences and provide more contextual responses. The guide concludes with instructions on publishing the bot to various chat applications and the Co bot store, as well as utilizing triggers, opening dialogues, and databases for data management.

Takeaways

• 🤖 **Building AI Chatbots**: The tutorial walks through the process of creating AI chatbots using Co, a platform that allows users to build on top of large language models like GPT-4.
• 🏪 **Co Bot Store**: Co features a bot store filled with community-created bots for various purposes, such as learning, productivity, and entertainment, which can inspire developers and be used as a starting point for new bots.
• 📚 **Knowledge Bases**: Co enables the use of knowledge bases to provide bots with context from various file types and sources, enhancing their ability to answer questions accurately.
• 🔌 **Plugins**: Users can leverage plugins to integrate different services and functionalities, like Google web search, GitHub, and Trip Advisor, to enrich the bot's capabilities without extensive coding.
• 📈 **Workflows**: Workflows in Co allow for the creation of multi-step processes that combine various skills and plugins to generate structured and predictable responses.
• 💬 **Conversational UI**: Co's platform uses a conversational user interface, allowing developers to build bots through natural language interactions and simple configurations.
• 🧰 **Customization & Optimization**: Developers can customize and optimize their bot's persona and skills using prompts, and Co assists with prompt engineering to improve bot understanding and responses.
• 🔗 **Integration with Chat Apps**: Bots built on Co can be published to popular chat applications like Discord, Slack, and Telegram, making them portable and accessible.
• 🌐 **Webhooks and Triggers**: Co supports the creation of triggers based on events or schedules, allowing bots to send messages proactively based on certain conditions or on a timetable.
• 📊 **Databases**: The platform includes database functionality, enabling bots to store, retrieve, and manage data in a structured format, which is useful for managing information like contact lists.
• 🔑 **Multi-Agent Mode**: Co allows for the creation of teams of bots, each specializing in different skills, which can collaborate to complete complex tasks more efficiently.

Q & A

What is the purpose of the Co Bot Store?

The Co Bot Store serves as a platform where community members can share and explore AI chatbots they have created. It contains a variety of bots for different purposes, such as learning, productivity, character interaction, and more.

How can one get inspired to create a chatbot?

One can get inspired by exploring the Co Bot Store, which features a range of bots created by the community. These bots can serve as a source of ideas and use cases for building new AI chatbots.

What are the key features of Co that differentiate it from other AI bot builders like GPT Builder?

Co offers features like knowledge bases, which can pull information from various sources like PDFs, Excel sheets, APIs, and websites. It also provides workflows for multi-step processes, the ability to publish to popular chat applications directly from the interface, and access to different OpenAI models for free.

How does the Persona prompt help in creating a chatbot?

The Persona prompt allows developers to define the chatbot's character, its abilities, and the features it will have. This is done using natural language, which helps the AI understand the context and assists in the bot's creation and functionality.

What is the role of plugins in enhancing the capabilities of a chatbot?

Plugins extend the chatbot's functionality by connecting it to various services through APIs. They can provide information from the web, generate images from text, or access specific data from services like Trip Advisor and Yelp, making the bot's responses more relevant and useful.

How can the user personalize the chatbot to their specific needs?

Users can personalize the chatbot by setting variables that act as memory for the bot. These variables can store user preferences, which the bot can use to tailor its responses and provide a more customized experience.

What is the significance of knowledge bases in a chatbot's functionality?

Knowledge bases allow the chatbot to reference and use content stored in different files and websites, providing more context for answering questions. This feature helps reduce 'hallucinations' where the bot provides inaccurate or irrelevant information, and enhances the bot's ability to provide precise and contextually relevant answers.

How can workflows be utilized to create a more structured response from the chatbot?

Workflows combine different skills like plugins, knowledge bases, and variables to create a multi-step process. This process helps in orchestrating between different nodes to generate a stable and structured response based on the user's query.

What is the benefit of using card bindings in formatting the bot's responses?

Card bindings help structure the bot's responses in a way that is expected based on the function of the API. They allow for the organization of responses into a visual format that includes elements like titles, descriptions, and images, making the information more accessible and user-friendly.

How does the multi-agent mode in Co work?

Multi-agent mode allows for the creation of a team of bots, each specializing in different skills. These bots can work together to complete tasks, providing a more comprehensive and efficient response to user queries.

What are the steps to publish a chatbot created with Co?

After building the bot, one can publish it to the Co Bot Store or to various chat applications like Discord, Telegram, or Slack. This involves generating a change log, selecting the publishing platform, and providing the necessary API keys or secret keys from the developer portals of these services.

Outlines

00:00

🤖 Introduction to AI Chatbots on Co So Co

The video begins with an introduction to creating powerful AI chatbots using Co So Co, a platform that allows users to build AI bots on top of large language models like GPT 4. The presenter, playing the role of a developer or user, is guided by Joshua through the process. They discuss the Co bot store, which contains community-built bots for various purposes such as learning, productivity, and entertainment. The store serves as a source of inspiration for bot creators and offers pre-built bots for different use cases.

05:02

🚀 Getting Started with Building a Chatbot

The presenter expresses excitement about building a chatbot to help plan a trip to Japan. They demonstrate how easy it is to get started on Co So Co by naming the bot 'Plan a Trip Bot' and utilizing large language models. The workspace features tools like Persona prompts to design the bot's character and skills. The presenter also explains how to optimize prompts for better understanding by the AI and customize the bot's profile image. Additionally, the video covers the integration of plugins and the use of GPT models for more personalized bot interactions.

10:03

🔍 Customizing the Large Language Model

The video discusses the importance of selecting the appropriate large language model based on the bot's use case. It covers the customization options available in the model configuration, such as changing the temperature for precision or response length. The presenter also talks about testing the chatbot's functionality and how it utilizes the GPT's training data to answer questions. They highlight the potential for further personalization using Co So Co's features to make the bot more relevant and comprehensive.

15:04

📚 Utilizing Plugins to Enhance Bot Capabilities

Plugins are introduced as a way to add functionality to the chatbot through APIs. The presenter demonstrates adding plugins like Google web search and Trip Advisor to enhance the bot's capabilities. They show how to use these plugins within the chatbot's workflow to provide recommendations and access various services. The video also covers creating custom plugins using external APIs, which can be beneficial for specific use cases not covered by existing plugins.

20:05

🏗️ Structuring Responses with Card Bindings

The presenter introduces card bindings, a feature that structures bot responses in a visually appealing way by utilizing data from APIs. They show how to use card bindings with the Yelp API to recommend restaurants, including their names, descriptions, and images. The video demonstrates customizing the bot's responses with card styles and data bindings to create a more user-friendly and interactive experience.

25:06

🧳 Personalizing the Travel Bot with Variables

Variables are showcased as a means to personalize the chatbot based on user preferences. The presenter guides through creating variables for trip preferences, which the bot can use to tailor its responses. They explain how the bot can remember these preferences using keyword memory to provide a more customized experience. The video also assures that the variables are private to each user, and as a developer, one cannot access personal variables of other users.

30:07

📚 Knowledge Bases for Enhanced Context

Knowledge bases are highlighted as a way to give the chatbot more context when answering questions. The presenter demonstrates how to create a knowledge base using various file types and websites. They show how to use the knowledge base to answer questions more accurately and reduce 'hallucinations' where the bot provides incorrect or fabricated information. The video also covers automatic call, which customizes the knowledge base and responses further.

35:10

🤝 Combining Skills with Workflows

Workflows are introduced as a way to combine different skills like plugins, knowledge bases, and variables to create a multi-step process. The presenter explains how workflows can help in orchestrating between different nodes to generate stable and structured responses. They demonstrate the creation of an NBA scores bot using workflows to fetch, parse, and format game data from an API.

40:10

📅 Automating Tasks with Triggers

Triggers are discussed as a feature that allows the bot to send messages at specific times or in response to events without user interaction. The presenter shows how to set up triggers for tasks like sending daily news summaries or reminders. They also mention the possibility of allowing users to create their own triggers through the bot, enhancing the bot's proactive capabilities.

45:11

🗨️ Opening Dialogue for User Onboarding

The presenter covers the use of opening dialogue to help users understand what the bot can do and how to interact with it. They demonstrate setting up opening questions and using autogenerated responses to guide the user. The video emphasizes the importance of this feature for user onboarding and ensuring a smooth start to the conversation with the bot.

50:12

🗂️ Using Databases for Data Organization

Databases are introduced as a feature for organizing data in a tabular structure, allowing for tasks like bookmark or contact management. The presenter guides through creating a database table, adding fields, and saving entries through natural language interactions with the bot. They highlight the power of databases in storing information that can be easily retrieved and used by the bot.

55:13

📈 Long-term Memory and Filebox for Data Retention

The presenter discusses long-term memory, which allows the bot to remember past interactions and use them to enhance future responses. They also introduce the filebox feature, which acts like a photo album for storing and querying media like photos using natural language. The video outlines how these features contribute to a more personalized and enriched user experience.

00:18

🔉 Text-to-Voice Feature for Audio Responses

The video covers the text-to-voice feature, which enables the bot to read out responses in selected voices. The presenter notes that this feature is currently supported in certain chat applications and not all. They highlight the potential of this feature for accessibility and user preference.

05:24

🏢 Single and Multi-user Modes in Databases

The presenter explains the difference between single and multi-user modes in databases. Single-user mode is for individual use, while multi-user mode allows multiple people to interact with the same database, making it suitable for community bots or collaborative environments. The video emphasizes the flexibility this feature provides for different user needs and scenarios.

10:27

🤖 Multi-agent Mode for Collaborative Bots

Multi-agent mode is introduced as a way to create a team of bots, each specializing in different skills to work together on tasks. The presenter demonstrates how these bots can be coordinated to provide a more comprehensive service, such as a personal assistant bot that manages emails, calendar events, and Google Sheets updates.

15:29

📝 Publishing Bots for Wider Usage

The presenter outlines the process of publishing bots to make them accessible to more people. They discuss generating change logs using AI, publishing to the Co bot store, and the option to publish to various chat applications. The video also encourages sharing bots in the Co Discord server to increase visibility and potentially get featured in the recommended section of the bot store.

❓ Getting Help and Further Resources

The video concludes with information on where to get help and find further resources, such as Co's comprehensive documentation, the Co assistant bot in Discord for answers on features, and the onboarding bot within Co for guidance. They also mention a YouTube channel for tutorials, best practices, and case studies, and invite suggestions for future video content.

Keywords

💡AI chatbot

An AI chatbot is an artificial intelligence software designed to simulate a conversation with human users. In the context of the video, the chatbot is being built on the CoSoCo platform, which allows for integration with large language models like GPT-4. The chatbot is used to assist with tasks such as planning a trip to Japan, demonstrating its utility in personal assistance.

💡CoSoCo

CoSoCo is a platform mentioned in the video that enables users to build AI bots on top of large language models. It provides features like knowledge bases, workflows, and the ability to publish bots to various chat applications. It is the central tool discussed in the video for creating and managing AI chatbots.

💡Knowledge bases

Knowledge bases in the context of the video are databases that the AI chatbot can access to retrieve information. They can include units from various file types like PDFs, Excel sheets, APIs, and websites. Knowledge bases are crucial for providing the chatbot with context and ensuring it can answer questions accurately.

💡Workflows

Workflows refer to a series of connected steps or processes that the AI chatbot can execute to complete a task. They are used in CoSoCo to help manage multi-step processes, enabling the chatbot to perform complex functions by orchestrating between different nodes or skills.

💡Plugins

Plugins are add-on features that can be integrated into the AI chatbot to extend its capabilities. They can connect to different services like the GitHub API or web search APIs to provide additional functionality. In the video, plugins are used to enhance the bot's ability to recommend restaurants and fetch information from the web.

💡GPT-4

GPT-4 is a large language model that the AI chatbot utilizes to understand and generate human-like text. It is one of the models available on CoSoCo for free, which the chatbot uses to process natural language and provide responses to user queries.

💡Persona prompt

A persona prompt is a description used to define the character and function of the AI chatbot. It includes details about the bot's purpose, skills, and how it should interact with users. In the video, the persona prompt is used to guide the creation of a 'plan a trip' bot with specific abilities like recommending restaurants and hotels.

💡Variables

Variables in the context of the video are used to store personalized information for each user of the AI chatbot. They allow the bot to remember user preferences and tailor responses accordingly, creating a more personalized experience for the user.

💡Triggers

Triggers are a feature that allows the AI chatbot to send messages or perform actions based on specific events or schedules. For example, a trigger can be set to remind a user about an appointment or to send a daily news summary, enhancing the bot's utility as a personal assistant.

💡Databases

Databases in the video are used to organize and store data in a structured format for the AI chatbot. They can be used to manage information like contact lists or travel itineraries, allowing the bot to reference and use this data to provide more informed responses.

💡Multi-agent mode

Multi-agent mode is a feature that enables the creation of a team of bots, each with specialized skills, to work together on tasks. This mode is highlighted as a powerful tool for creating more complex and efficient AI chatbot experiences, where different bots can handle different aspects of a user's request.

Highlights

Introduction to building AI chatbots using Co, a platform that integrates with large language models like GPT 4.

Exploration of the Co Bot Store, featuring community-created bots for various purposes.

Demonstration of how to use existing bots for inspiration or direct application in your projects.

Explanation of the differences between Co and other custom GPT Builders like Open AI's custom GPT.

Utilization of features like knowledge bases, workflows, and multi-step processes in Co to enhance bot functionality.

Live creation of a 'Plan a Trip Bot' to assist with planning a trip to Japan.

Discussion on how to use persona prompts to design a bot's character and skills.

Optimization of prompts using Co's built-in features to improve bot understanding and responses.

Customization of the large language model used by the bot, including options like GPT 4 and GPT 3.5.

Testing the chatbot's capabilities using a sample query about planning a trip to Ginza, Tokyo.

Integration of plugins like Yelp and Trip Advisor to enhance the bot's recommendations.

Use of card bindings to structure bot responses with a combination of text, images, and links.

Implementation of variables to personalize the bot's responses based on user preferences.

Introduction to knowledge bases, allowing bots to reference content from various sources to inform responses.

Explanation of how to use Co's automatic call feature to customize knowledge base responses.

Discussion on using workflows to create multi-step processes that combine various bot skills.

Example of a workflow for generating NBA scores using a combination of nodes and skills.

Highlighting the use of triggers for timed or event-based bot actions, such as sending reminders or updates.

Use of databases in Co to manage and query information in a tabular format through natural language.

Overview of publishing bots to various platforms, including the Co Bot Store and popular chat applications.

Emphasis on the importance of community engagement and sharing creations through Discord and the Co Bot Store.