Anyone interested in learning more about building AI chat applications. Will be interested to know that IBM has created a fantastically quick and to the point introduction to using Retrieval Augmented Generation (RAG) and large language models. By building your own chat applications and customizing them with your own personal data makes them far more valuable. Whether you are in than individual wishing to keep your documentations of third-party servers or businesses with sensitive data. Training your own AI models on specific information and knowledge is a great way to integrate AI into your workflows.
This guide will explore the process of creating such an app, focusing on the use of the RAG, the LangChain framework, and Streamlit components. It will also divide a little insight on the use of LangChain’s interface for a credentials dictionary with an API for IBM Cloud, and the use of the large language model ‘Llama 2 70b chat‘ for the project.
The Retrieval-Augmented Generation (RAG) technique is a powerful tool that combines the strengths of retrieval-based and generative models for natural language understanding and generation tasks. In this approach, an initial query or context is used to fetch relevant information from a large database or corpus using a retriever. The retrieved information is then supplied to a generator model, often a sequence-to-sequence model like a Transformer, to produce a more informed and contextually relevant output. In the context of building a chat app using large language models and personal data, this technique can be highly beneficial. It allows the app to provide responses that are not only coherent and contextually appropriate but also highly personalized based on the user’s data.
Building an AI chat app using LLMs
LangChain is a framework designed for developing applications powered by language models. It provides modular abstractions for the components necessary to work with language models and has collections of implementations for these abstractions. The components are designed to be easy to use, regardless of whether you are using the rest of the LangChain framework or not. Nicholas Renotte takes you through the process of Building a chat app with LangChain and Streamlit, covering everything you need to know in just three minutes.
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LangChain also provides use-case specific chains, which can be thought of as assembling these components in particular ways to best accomplish a specific use case. These chains are designed to be customizable and provide a higher level interface through which people can easily get started with a specific use case.
Easy GUI design using Streamlit
Building a chat app with LangChain and Streamlit can offer a seamless and efficient development experience. Streamlit components can be used for chat input and message display, creating a user-friendly interface. One can create a state variable for storing user prompts, facilitating the process of tracking and responding to user interactions. LangChain’s interface to WhatOnNext can be used to guide the chat app’s responses, making it more dynamic and engaging.
Llama 2
For the project, the large language model ‘llama 2 70b chat‘ can be utilized. This model can generate coherent and contextually appropriate responses, enhancing the user experience. The responses generated by the language model can be displayed using the Streamlit chat message component, creating a smooth and interactive interface for users.
“Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.”
Adding Personal data
Incorporating personal data into the chat app can be accomplished in a number of different ways. For instance loading PDF data into the LangChain Vector store index. This allows the app to retrieve and utilize personal data from PDF files, adding a layer of personalization to the chat interactions. The LangChain retriever QA chain can be used for chat interactions with the PDF data, enabling the app to provide responses based on the user’s personal data.
To facilitate the connection to external data sources and services, a credentials dictionary can be used with an API for IBM Cloud. This allows the chat app to access and utilize cloud-based resources, enhancing its capabilities and performance.
Building a chat app using large language models and personal data involves a combination of advanced techniques and tools. The use of the Retrieval Augmented Generation technique, the LangChain framework, and Streamlit components, along with the integration of personal data, can result in a dynamic, interactive, and personalized chat app. The process, while complex, can offer a rewarding outcome: a chat app that not only understands and responds to user prompts but also provides a personalized and engaging user experience.
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