12/24/2023 0 Comments Amazon chatbot lexThe matching documents (chunks) are added to the prompt as context by the Lambda function and then the function uses the flan-t5-xxl model deployed as a SageMaker endpoint to generate an answer to the user question. We implement a chatbot application in Streamlit which invokes the function via the API Gateway and the function does a similarity search in the OpenSearch Service index for the embeddings of user question. We implement the RAG functionality inside an AWS Lambda function with Amazon API Gateway to handle routing all requests to the Lambda. We convert the HTML pages on this site into smaller overlapping chunks (to retain some context continuity between chunks) of information and then convert these chunks into embeddings using the gpt-j-6b model and store the embeddings in OpenSearch Service. We use the SageMaker docs as the knowledge corpus for this post. After Amazon Bedrock launches, we will publish a follow-up post showing how to implement similar generative AI applications using Amazon Bedrock, so stay tuned. We can use the same architecture to swap the open-source models with the Amazon Titan models. Implementing the question answering task.Ingesting document embeddings into Amazon OpenSearch Service.Interfacing with LLMs hosted on Amazon SageMaker.We then demonstrate how to use LangChain for tying everything together: We provide an AWS Cloud Formation template to stand up all the resources required for building this solution. We use a combination of different AWS services, open-source foundation models ( FLAN-T5 XXL for text generation and GPT-j-6B for embeddings) and packages such as LangChain for interfacing with all the components and Streamlit for building the bot frontend. In this post we provide a step-by-step guide with all the building blocks for creating an enterprise ready RAG application such as a question answering bot. To understand the overall structure of a RAG-based approach, refer to Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart. in 2020 as a model where parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. RAG models were introduced by Lewis et al. A small number of similar documents (typically three) is added as context along with the user question to the “prompt” provided to another LLM and then that LLM generates an answer to the user question using information provided as context in the prompt. In the RAG-based approach we convert the user question into vector embeddings using an LLM and then do a similarity search for these embeddings in a pre-populated vector database holding the embeddings for the enterprise knowledge corpus. Furthermore, FMs are trained with a point in time snapshot of data and have no inherent ability to access fresh data at inference time without this ability they might provide responses that are potentially incorrect or inadequate.Ī commonly used approach to address this problem is to use a technique called Retrieval Augmented Generation (RAG). Pre-trained foundation models (FMs) perform well at natural language understanding (NLU) tasks such summarization, text generation and question answering on a broad variety of topics but either struggle to provide accurate (without hallucinations) answers or completely fail at answering questions about content that they haven’t seen as part of their training data. Amazon Lex provides the framework for building AI based chatbots. One of the most common applications of generative AI and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |