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?