8 Building LangChain Applications Visually using LangFlow

 

This chapter covers

  • What is LangFlow?
  • Creating a LangChain project using LangFlow
  • Using and configuring the various components in LangFlow
  • Using LangFlow to query your own data

Previously, you learned how to build LLM-based applications by chaining various components such as Prompt Template, LLMs, Memory, and more. You also learned how to use LlamaIndex to connect an LLM to answer questions pertaining to your own data. To use LangChain, you must download the langchain package and then use the various APIs in the framework.

Now, you will learn an easier approach to building LLM-based applications using LangChain – instead of writing code, you will build LangChain apps using a drag-and-drop tool known as LangFlow. Using LangFlow, you can now get started with LangChain without getting bogged down with the details of coding and can instantly preview your applications without any complicated setup process.

8.1 What is LangFlow

LangFlow is an open-source library that allows you to build LLM-based applications using LangChain through a drop-and-drop visual interface. LangFlow is built on top of LangChain so that you can develop AI applications faster and easier through a No Code Low Code experience.

The source code for LangFlow can be downloaded from https://github.com/logspace-ai/langflow. For this chapter, we will focus on how to install LangFlow on your computer so that you can get productive immediately.

8.1.1 Installing LangFlow using the pip command

8.1.2 Installing LangFlow using Docker

8.1.3 Running LangFlow on the Cloud

8.2 Creating a New LangFlow Project

8.2.1 Adding a Prompts Component

8.2.2 Adding a Models Component

8.2.3 Adding a Chains Component

8.2.4 Adding an Inputs and Outputs Component

8.2.5 Testing the Project

8.2.6 Maintaining a Conversation using the Chat Memory Component

8.3 Asking Question on Your Own Data

8.3.1 Loading PDF documents using the File Component

8.3.2 Splitting Long Text into Smaller Chunks using the Parse Data Component

8.3.3 Getting questions using the Prompt Component

8.3.4 Using the HuggingFace Component

8.3.5 Connecting to the Chat Output Component

8.3.6 Testing the Project

8.3.7 Using an LLM using the OpenAI Component

8.4 Using Your Project Programmatically

8.4.1 cURL

8.4.2 Python Code

8.5 Summary