Project Overview [Video]
Semantic Search with SQuAD 2.0 Dataset Using Pinecone Vector DB, LangChain, and OpenAI GPT Chat API
The goal of this project is to develop a robust Semantic Search application capable of providing precise answers to user queries. This is achieved by leveraging the SQuAD 2.0 dataset, Pinecone vector database, LangChain tools and agents, and the OpenAI GPT Chat API. The application will create a searchable space for the SQuAD 2.0 dataset and enable interactive querying using natural language processing capabilities.
Key Components
- SQuAD 2.0 Dataset: A large-scale dataset for reading comprehension and question-answering that contains more than 150,000 question-answer pairs on 500+ articles.
- Pinecone Vector Database: A managed vector database service optimized for machine learning models. It allows for efficient similarity search, which is crucial for finding the most relevant answers to queries.
- LangChain: A library of tools for building applications with language models. LangChain will be used for managing conversational context and integrating with the OpenAI GPT API.
- OpenAI GPT Chat API: A state-of-the-art language model that can understand context, answer questions, and maintain a coherent conversation.
Project Workflow
Data Preparation and Indexing
- Data Ingestion: Load the SQuAD 2.0 dataset and preprocess it to extract the questions, answers, and contextual paragraphs.
- Vectorization: Use OpenAI’s embedding models to convert textual data into high-dimensional vectors.
- Upsert into Pinecone: Upsert the vectorized data into a Pinecone index, which will allow us to perform semantic searches over the dataset.
- Create LangChain Tool and Conversational Agent: Use the LangChain Framework Tool for the Q&A with Conversational Memory.
Expected Outcomes
- A functional Semantic Search application that can accurately answer questions using the SQuAD 2.0 dataset.