Build an AI Dating Coach with Flow Wise and Zep for Long-Term Relationship Success

Table of Contents:

1. Introduction
2. Building an AI Assistant with Long-Term Memory
   1. Deploying Flow Wise on Render
   2. Configuring Authentication
   3. Adding Environment Variables
   4. Creating the Skeleton of the Chat Flow
   5. Using Chains and Agents in Flow Wise
   6. Adding Tools and Integrations
   7. testing the AI Assistant
3. Conclusion

Building an AI Assistant with Long-Term Memory

In this Tutorial, we will learn how to build an AI assistant with long-term memory using Flow Wise. Flow Wise is a powerful tool that allows for the creation of complex chat flows by combining chains and agents.

Deploying Flow Wise on Render

To get started, we need to deploy Flow Wise on Render. Render provides an easy-to-use platform that allows for quick deployment of web services. Simply click on the link provided in the tutorial, deploy Flow Wise on Render, and wait for the deployment to complete.

Configuring Authentication

Next, we need to configure authentication for Flow Wise. This step is crucial and should not be Skipped. To do this, download the Zap CLI file specified in the tutorial and run it using the provided command. Follow the instructions to generate a JWT token, which will serve as your API key. Copy and save this token for later use.

Adding Environment Variables

After configuring authentication, we need to add environment variables to our Render environment. These variables are necessary for the proper functioning of Flow Wise. Add the "zipcore-alore-required" variable with a value of "true" and the "zap-aore-secret" variable with the JWT token as the value. Save the changes and wait for the deployment to finish.

Creating the Skeleton of the Chat Flow

Now that Flow Wise is deployed and the environment is set up, we can start building the chat flow. Every chat flow in Flow Wise should start with either an agent or a chain. In this tutorial, we will use both. Agents are advanced chat models that can make decisions and use tools. Chains, on the other HAND, allow for the integration of various functionalities and data sources.

Using Chains and Agents in Flow Wise

To begin, we will add an Open AI Chat model as the first component in our flow. This chat model will act as our AI assistant. Set up the credentials for the chat model and customize the temperature parameter for more deterministic answers. We will then create a "zap" memory to store and summarize the conversation.

Next, we will add a chain tool to our flow. Chains connect agents to other functionalities and allow for the creation of more complex chat flows. We will use the "Ral Q&A" chain to add documents as a Knowledge Base for our chat bot. Connect the language model, vector store retriever, and document splitter to the chain, and specify the type of documents (PDF files in this case) to be used.

Adding Tools and Integrations

In addition to chains and agents, Flow Wise allows for the integration of various tools. We can add a custom tool to our flow to connect the AI assistant to external tools and services. By utilizing tools, we can enhance the capabilities of our chat bot. Tools such as API calls, data loaders, and calculators can be added to extend the functionality of the AI assistant.

Testing the AI Assistant

With the chat flow set up, we can now test the AI assistant. We can interact with the assistant and provide prompts to simulate user conversations. The AI assistant will respond based on the knowledge base and the defined prompts. We can also experiment with different chains and agents to explore different capabilities and customize the AI assistant's behavior.

Conclusion

In this tutorial, we learned how to build an AI assistant with long-term memory using Flow Wise. We deployed Flow Wise on Render, configured authentication, created the skeleton of the chat flow, and added chains, agents, and tools to enhance the capabilities of our AI assistant. By following the steps outlined in this tutorial, you can create your own AI assistant with a long-term memory and extend its functionality by integrating with other tools and services.

Highlights:

• Build an AI assistant with long-term memory using Flow Wise
• Deploy Flow Wise on Render for easy web service deployment
• Configure authentication and add environment variables for secure and proper functioning
• Create the skeleton of the chat flow using chains and agents
• Enhance the AI assistant's capabilities by adding tools and integrations
• Test and customize the behavior of the AI assistant based on user prompts

FAQ: Q: Can I deploy Flow Wise on a different platform other than Render? A: Yes, Flow Wise can be deployed on various platforms, but Render is recommended for its ease of use and availability in the free version.

Q: What are the benefits of using chains and agents in Flow Wise? A: Chains and agents allow for the creation of complex chat flows, decision-making capabilities, and integration with external tools and services.

Q: Can I customize the behavior of the AI assistant? A: Yes, you can customize the behavior of the AI assistant by adding prompts, adjusting parameters, and experimenting with different chains and agents.

Q: Can I extend the functionality of the AI assistant with external tools? A: Yes, Flow Wise allows for the integration of various tools, such as API calls, data loaders, and calculators, to enhance the capabilities of the AI assistant.