Enhancing Generation with OpenAI/GPT and Chroma

Table of Contents

1. Introduction
2. Background on Retrieval Augmented Generation (RAG)
3. The Use of RAG in Answering Questions about Wimbledon 2023
4. Downloading the Wikipedia Page
5. Data Processing and Chunking
6. Storing Text as Embeddings in Chroma DB
7. Creating Templates for Question Answering
8. Initializing the Open AI Model
9. Creating a Question and Answer Model
10. Asking Questions and Obtaining Answers
11. Examples of Questions and Answers
12. Conclusion

Introduction

In this article, we will explore the technique of retrieval augmented generation (RAG) and how it can be used to enhance large language models. Specifically, we will focus on using RAG to answer questions about the Wimbledon 2023 tennis tournament. We will utilize OpenAI and the Chroma DB Vector database to retrieve Relevant information and generate accurate answers.

Background on Retrieval Augmented Generation (RAG)

Retrieval augmented generation (RAG) is a powerful technique that combines retrieval-Based methods with generation-based methods to enhance the capabilities of large language models. By providing additional data in the form of retrievable fragments or embeddings, RAG allows models to access relevant information, increasing their ability to answer specific questions accurately. This technique has been widely used in various applications, including question answering, information retrieval, and content generation.

The Use of RAG in Answering Questions about Wimbledon 2023

To demonstrate the effectiveness of RAG, we will focus on answering questions about the Wimbledon 2023 tennis tournament. By utilizing the power of RAG, we can extract relevant information from the Wimbledon 2023 Wikipedia page and generate accurate responses to specific queries.

Downloading the Wikipedia Page

To begin, we will download the Wikipedia page for the Wimbledon 2023 tournament using the Langchain framework. The Wikipedia loader module will allow us to retrieve the desired data by specifying the search term "2023 Wimbledon championships." By limiting the loader to retrieve only one document, we can focus solely on the relevant information.

Data Processing and Chunking

Once we have obtained the text data from the Wikipedia page, we will process it by splitting it into smaller, more manageable chunks. The recursive character text splitter module will help us split the text into chunks of 100 characters with a small overlap. This chunking process ensures that we provide only the necessary and relevant information to the large language model (LLM) later in the process.

Storing Text as Embeddings in Chroma DB

To effectively utilize the retrieval aspect of RAG, we need to store the processed text chunks as embeddings in Chroma DB. Chroma DB is an AI native open-source embedding database that allows us to store the text chunks as arrays of numbers or embeddings. By storing the text chunks in Chroma DB, we can perform nearest neighbor searches to retrieve the most relevant answers to specific questions.

Creating Templates for Question Answering

In order to generate answers to questions about Wimbledon 2023, we need to Create templates. These templates will provide the necessary structure for the question answering process. We specify the Context, which is where the extracted data will be inserted, and the questions, which will serve as the specific queries. By creating these templates, we can efficiently utilize the power of RAG to generate accurate and informative responses.

Initializing the Open AI Model

Before we can begin the question answering process, we need to initialize the Open AI model. We will set the temperature parameter to zero, ensuring that we obtain consistent answers each time we use the model. Additionally, we will use GPT-4 as the chosen model for this task.

Creating a Question and Answer Model

To facilitate the question answering process using RAG, we will create a question and answer model using the functionalities provided by the Langchain framework. By passing in the LLM and the Chroma database as a retriever, we enable the model to access the relevant documents for answering questions accurately. Additionally, we can instruct the model to return the source document, allowing us to review the documents used to obtain the answers.

Asking Questions and Obtaining Answers

With the question and answer model in place, we can now ask specific questions about the Wimbledon 2023 tournament and obtain accurate answers. By formulating questions related to tournament details, such as the location and dates, winners, and special events, we can gauge the effectiveness of RAG in providing informative responses.

Examples of Questions and Answers

To showcase the capabilities of RAG, we will present several examples of questions asked about Wimbledon 2023 and the corresponding answers generated by the model. These examples will cover a range of question types, including factual queries about tournament details and player participation. By evaluating the accuracy and relevance of the answers, we can assess the effectiveness of RAG in the context of question answering.

Conclusion

In conclusion, retrieval augmented generation (RAG) is a powerful technique that enhances large language models' capacity to answer specific questions accurately. By leveraging the capabilities of RAG, we can effectively retrieve and generate relevant information about the Wimbledon 2023 tennis tournament using OpenAI and the Chroma DB Vector database. The combination of retrieval and generation methods enables the model to access valuable data and provide informative answers to complex queries. By harnessing the power of RAG, we can enhance the capabilities of language models and improve their performance in various domains.