Working With Memory for LLM Chat Agents

Multi Prompt Chains allow you to create complex interactions inside of Flowise.

Mastering Prompt Chaining for Advanced AI Applications

Introduction: Prompt chaining is a powerful technique that allows developers to harness the capabilities of multiple language models and chains to create sophisticated AI-driven applications. This article explores the key concepts and steps involved in prompt chaining, using insights from a YouTube video transcript focused on prompt training in the context of Lang chain and Flowwise.

Understanding Prompt Chaining: Prompt chaining involves combining several chains and AI models to produce a desired output for an application. The core benefit lies in the ability to build advanced AI applications with enhanced flexibility and power. The YouTube video transcript provides a practical example of prompt chaining, where three chains are combined to create a unique AI-driven experience.

Example Application: The video walks through the creation of a simple yet illustrative application. In this scenario, the application comprises three chains:

Ingredient Chain:
- Purpose: Generates an ingredient for a recipe based on the user-provided public holiday.
- Components: Uses an LLM chain with the OpenAI API, and a prompt template to request the main ingredient for a recipe related to a public holiday.
Shift Chain:
- Purpose: Generates a unique recipe based on the public holiday and main ingredient from the first chain.
- Components: Utilizes another LLM chain connected to the output of the Ingredient Chain. A prompt template is employed to instruct the model to create a recipe based on the given inputs.
Critic Chain:
- Purpose: Behaves like a food critic, analyzing the generated recipe and public holiday to produce a review.
- Components: Incorporates an additional LLM chain connected to the output of the Shift Chain. A prompt template is structured to instruct the model to critique a recipe based on the provided inputs.

Key Steps in Prompt Chaining: The video emphasizes several crucial steps in implementing prompt chaining:

Prompt Template Design:
- Define clear and concise prompt templates for each chain, specifying variables for user inputs and outputs from the previous chain.
Variable Assignment:
- Ensure proper assignment of variables, especially when passing values from one chain to another. The first variable in the prompt template is assumed to be the user input.
Debugging and Testing:
- Use debugging tools to trace the flow of data between chains. Debugging aids in identifying any issues with variable assignments and ensures the correct passage of information.
Flexibility with Models:
- Experiment with different language models based on the specific requirements of each chain. The choice of models can significantly impact the quality of the output.

Conclusion: Prompt chaining is a versatile and powerful technique for building advanced AI applications. By carefully designing prompt templates, assigning variables, and utilizing debugging tools, developers can create intricate applications with diverse functionalities. Experimenting with different language models adds an extra layer of flexibility, enabling developers to optimize each chain for specific tasks. As AI applications continue to evolve, mastering prompt chaining becomes an invaluable skill for developers seeking to push the boundaries of what is possible in the realm of artificial intelligence.

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Flowise Chain Node Mastery (Mini-Course)

Picture of Level: Intermediate

Level: Intermediate

Flowise Training

Level: Intermediate Training

Discover How to use Chains Nodes in Flowise to Build Powerful AI Applications

Chain nodes are the building blocks of Flowise, a powerful tool for creating conversational AI applications. These nodes enable you to perform a wide range of tasks, from retrieving data from APIs and databases to generating responses using large language models. In this mini course, you will learn about the different chain nodes available in Flowise and how to use them to build sophisticated conversational AI applications.

Here’s What You’ll Learn:

How each of the chain node works and how to use them in Flowise.
Which nodes have been replaced by newer tools and updates.
How to connect to a variety of APIs to extend the power of your Chatflows and AI applications
How to use the SQL Database Chain to interact with SQL databases using natural language
When to use prompt chains vs retrieval chains in your workflows
How to add chat models in your chains that might not be supported yet in Flowise
How to create multi-prompt chains as well as retrieval QA chains that can handle multiple documents.

By the end of this mini course, you will have a better understanding of the different chain nodes available in Flowise and how to use them to build sophisticated conversational AI applications.

You will also learn best practices for improving the performance of your chains, handling errors, and formatting your data and loading documents for your RAG (Retrieval) applications.

Course Content

8 Sections | 4 Lectures | 50 Minutes Total Length

Lesson 1: API Chains

In this lesson we’ll look at the API Chains including the GET and POST Chain as wel as the OpenAI API Chain. These Chains are used to connect to a variety of third party APIs for integration with Flowise.

Project Files: Open Movie Review Chatbot (POST | GET Request)

Lesson 2: Database Chains

In this lesson, we’ll expore our database chains, including the SQL Database Chain, Vectara QA Chain and the VectorDB QA Chain. These chains help you connect to and query a database.

In the case of the SQL Chain, it will use SQL query language to query a database based on your questions. The Vectara and Vector DB chains connect to a vector database in order to summarize and answer questions based on your documents.

Project Files: SQL Database Chain

In this project, we've created our own SQL database with data from the Open Movie Database to test in our SQL Chain in Flowise

Lesson 3: Retrieval QA Chains

Retrieval QA Chains are a core feature of Flowise that allow you to build powerful Retrieval Augmented Generation or RAG applications.

In this lesson, we’ll look at each of the Retrieval QA chains and sh

Project Files: Retrieval QA Chains

Lesson 4: LLM Prompt Chains

LLM Prompt Chains allow us to add Prompt Templates to our chatflows and customise exacly how we want our LLMs to respond to our queries or user input.

Prompt templates also allow you to add additional information from previous nodes, allowing you to create multistep workflows based on what you need your application to do.

In this lesson, we’ll look at three nodes, The LLM Chain, The Conversation Chain and the Multi Prompt Chain. Each one can be connected to chat prompt templates so you can create complex conversational workflows that leverage the power of large language models (LLMs)

Project Files: LLM Prompt Chains

In this project, we'll create an App Idea Generator using LLM prompt chaining

Review Q+A

In this session, we'll answer some common questions about Flowise including:
1. How can I improve the performance of a chain in Flowise?(i.e improving quality of responses, Reduce hallucinations, increasing speed etc)
2. What is the best way to connect multiple chain nodes in Flowise?
3. How can I use chat models in my chains that might not be supported yet in Flowise
4. What is the best way to load documents for retrieval (PDF, CSV, TXT etc)
5. How do I handle errors in a chain in Flowise?
6. How do I format the output of a Retrieval QA Chain in Flowise?
7. How to create a Retrieval QA Chain that can handle multiple documents in Flowise?
8. What do I need to make sure that values are passed between chain nodes?
9. Is it possible to use a form with many inputs in Flowise instead of the chat input method?
10. How to create a chain that can handle large datasets in Flowise?