Conversational AI offers a fresh approach to interacting with computers, making it more natural for tasks like information retrieval, organization, and brainstorming. Its potential is immense. However, there’s a flip side to this. Chatbots can be both impressive and risky. They can provide convincing yet inaccurate information. While we can…

RAG(Retrieval Augmented Generation) Retrieval Augmented Generation (RAG) is a very popular framework or class of LLM Application. The basic principle of RAG is to leverage external data sources to give LLMs contextual reference. Any RAG implementation has two aspects: Generation : Generation is performed by the LLM, which generates the answer by using…

Fine-tuning enables users to shape the behaviors and capabilities of an existing large language model by further training it using carefully selected custom data. For instance, imagine a health-and-wellness chatbot powered by a fine-tuned language model with additional medical advice — it’s more likely to provide accurate responses compared to…

What is Chainlit ? Chainlit, is an open-source Python package designed to revolutionize the way you build and share Language Model (LM) applications. With Chainlit, you can create stunning user interfaces (UIs) similar to those of ChatGPT, the renowned language model developed by OpenAI. It develops a streamlit like web interface. Key Features of Chainlit Visualize Intermediary Steps…

What is RAG(Retrieval Augmented Generation) ? Retrieval Augmented Generation (RAG) is a natural language processing (NLP) technique that combines two fundamental tasks in NLP: information retrieval and text generation. It aims to enhance the generation process by incorporating information from external sources through retrieval. The goal of RAG is to produce more accurate and contextually relevant…

Caching, in simple terms, involves storing the output of a resource-intensive calculation. This allows for retrieving the stored result from the cache instead of recomputing it when the same calculation is required again. Caching proves particularly beneficial when repetitive computations are likely to occur, either within a single session or…

Here we will Implement Question Answering Application with a website as the source. Steps Involved is as follows : Specify a website Extract the contents of the website and covert to pdf using weasyprint package. Load the contents of the PDF using LangChain. Convert the document content into vector embeddings…

What is LangChain ? LangChain is an open source framework available in Python or JavaScript (TypeScript) packages, enabling AI developers to integrate Large Language Models (LLMs) like GPT-4 with external data. Unlike traditional limitations imposed by LLMs, which are trained on data up until 2021, LangChain overcomes this constraint by allowing LLMs to access…

In general when performing Question Answering task , we can get answers pertaining to context in which the question was asked. However if we ask more advanced or specific questions to LLMs like COHERE xlarge or google/Flan-t5-xl, these models will not perform well. So in order to to accurately and…