Flowise Chain Node Mastery (Mini-Course)
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- Which nodes have been replaced by newer tools and updates.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
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.
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.
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
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
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)
In this project, we'll create an App Idea Generator using LLM prompt chaining
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?