Building an Intelligent Chat Application with ChainLit, LangChain, Ollama & Mistral

Table of Contents:

1. Introduction
2. AMA Series Overview
3. Creating a Simple Chat UI
4. Using AMA and Lang Chain
5. Creating a RAG Application
6. Using Open Source Models
7. Introduction to Lang Smith
8. Setting Up the Project
9. Ingesting Documents and Creating a Chain Lead Application
10. Uploading PDF Files and Creating a Chain Lead Application
11. Conclusion

Introduction

In this article, we will explore the process of creating a simple RAG (Retrieval-Augmented Generation) application using open-source models and tools like AMA (Ask Me Anything) and Lang Chain. But before we dive into the specifics, let's start by giving you an overview of the AMA series and the previous videos that have been created. If you are new to AMA, it is recommended to refer to these videos to get a better understanding of the concepts and applications discussed.

AMA Series Overview

The AMA series is a collection of videos that explore the capabilities and usage of the AMA system. AMA provides a chat-like interface that allows users to interact with language models and retrieve information based on their queries. The series covers topics such as creating a simple chat UI, using AMA and Lang Chain together, and deploying applications using Lang Smith.

Creating a Simple Chat UI

In the first few videos of the series, a simple chat UI application was developed. This application allows users to interact with the chat GPT (Generative Pre-trained Transformer) interface and utilize the models provided by AMA. The implementation of this application was discussed step by step, including the setup, installation, and usage of AMA and Lang Chain.

Using AMA and Lang Chain

AMA and Lang Chain are powerful tools that can be used together to enhance the functionality of applications. Lang Chain provides a chain lead application that allows users to ingest documents, split them into chunks, and create a Knowledge Base for retrieval and generation tasks. AMA, on the other HAND, provides a wide range of pre-trained language models that can be used for various NLP (Natural Language Processing) tasks. By leveraging the capabilities of both tools, developers can create sophisticated applications that can understand and respond to user queries effectively.

Creating a RAG Application

In this article, we will focus on creating a RAG application using open-source models. Unlike the previous videos where existing code was used, we will build a simple PD (Prompt-Driven) RAG application from scratch. This approach allows us to have more control over the application and eliminates the need for complex codebases. We will utilize the models provided by AMA and Lang Chain to download and deploy the required components of the application.

Using Open Source Models

Open-source models are a valuable resource for developers who want to create RAG applications quickly. In this article, we will explore the options available in the Lang Chain Hub and select the appropriate model for our application. We will discuss the process of installing and using the selected model in combination with AMA to achieve the desired functionality.

Introduction to Lang Smith

Lang Smith is a tool that can be used to Trace and monitor the performance of applications created using Lang Chain and related technologies. We will briefly introduce the concept of Lang Smith and its role in our RAG application. This will help us ensure that our application is functioning correctly and can be optimized if needed.

Setting Up the Project

Before we can start developing our RAG application, we need to set up the project environment. This involves cloning the required GitHub repository, installing the necessary dependencies, and creating a virtual environment. We will provide detailed instructions on how to complete these steps and ensure that the project is ready for development.

Ingesting Documents and Creating a Chain Lead Application

Once the project is set up, we can proceed to ingest the documents and create a chain lead application. We will explore two different methods for ingesting documents: loading files from a data folder and uploading files directly on the UI. We will demonstrate both approaches and guide you through the necessary steps to ensure that the documents are processed correctly and the chain lead application is created successfully.

Uploading PDF Files and Creating a Chain Lead Application

In this section, we will focus on the process of uploading PDF files on the UI and creating a chain lead application. We will discuss the importance of setting the correct parameters and demonstrate how to run the application smoothly. We will also address some common issues that developers may encounter and provide tips on how to troubleshoot them effectively.

Conclusion

In conclusion, creating a RAG application using open-source models and tools like AMA and Lang Chain can be a straightforward and efficient process. By following the steps outlined in this article, developers can build applications that leverage the power of language models and provide accurate and Relevant information to users. The combination of AMA and Lang Chain offers endless possibilities for creating intelligent chat interfaces and enhancing the user experience.

Highlights:

1. AMA and Lang Chain: Powerful tools for creating sophisticated applications.
2. Creating a RAG application from scratch for more control and flexibility.
3. Utilizing open-source models and the Lang Chain Hub for quick application development.
4. Introduction to Lang Smith for monitoring and tracing application performance.
5. Setting up the project environment for seamless development.
6. Ingesting documents and creating a chain lead application.
7. Uploading PDF files and creating a chain lead application.
8. Tips and troubleshooting for smooth application deployment.
9. Leveraging the power of language models for intelligent chat interfaces.

FAQ:

Q: Can I use different file formats besides PDF in the RAG application? A: Yes, the RAG application supports various file formats, such as CSV and plain text documents. You can customize the application to ingest and process different file types according to your requirements.

Q: How can I optimize the performance of the RAG application? A: To optimize the performance of the RAG application, consider fine-tuning the parameters, such as chunk size and overlap, based on the specific needs of your application. Additionally, ensure that your hardware resources are sufficient to handle the workload effectively.

Q: What if I encounter issues or errors during the development process? A: If you encounter any issues or errors during the development process, it is recommended to refer to the official GitHub repository for Lang Chain and Chain Lead. You can create an issue there to get support from the developers and the community. Additionally, reviewing the documentation and seeking assistance from relevant forums or communities can also be helpful in troubleshooting and resolving any issues you may encounter.

Resources:

• Lang Chain GitHub repository: https://github.com/lang-ai/lang-chain
• Chain Lead documentation: https://chain-lead-docs.com
• AMA documentation: https://ama-docs.com