The ability to accurately answer user questions based on specific documents is a challenging yet crucial task in various fields, including customer service, education, and professional services. Traditional chatbots often struggle to provide precise answers when the required information is embedded within complex documents. The problem we aim to solve is:
“Develop a chatbot using the RAG architecture to answer user questions based on uploaded documents while also incorporating chat history.”
To address this problem, we utilize our AI development services to propose a solution that leverages the Retrieval-Augmented Generation (RAG) architecture. Our solution consists of several key components: preprocessing uploaded documents, storing their embeddings in a vector database, performing similarity searches, generating answers using OpenAI’s GPT-3.5-turbo, and integrating chat history for contextually relevant responses. The workflow we have used is popular amongst the top chatbot companies.
The chatbot begins by allowing users to upload documents or provide URLs. This flexibility ensures that users can easily input the sources of information they need the chatbot to reference. The supported file types include various common formats to accommodate a wide range of documents.
The system accepts the following file types:
This variety ensures that the chatbot can handle different types of documents commonly used in professional and educational settings.
Step 1: Text Data Extraction
The first step in preprocessing is extracting text data from the uploaded documents. Each file type has a specific method for text extraction:
Step 2: Text Chunking
Once the text is extracted, it is chunked into manageable pieces. This is necessary because documents can be lengthy, and processing them as a whole can be inefficient. Chunking helps in breaking down the text into smaller, coherent segments that can be individually analyzed and embedded.
Step 3: Text Embedding
Each chunk of text is then converted into embeddings using a pre-trained model. Embeddings are dense vector representations of text that capture its semantic meaning. These embeddings are generated using models such as BERT, Roberta, or Sentence Transformers.
Step 4: Storing Embeddings in Astra DB
The extracted embeddings are stored in Astra DB, a highly scalable and performant vector database. Astra DB supports vector data types and provides efficient storage and retrieval capabilities.
This case study presents a comprehensive solution for developing a chatbot using the RAG architecture to answer user questions based on uploaded documents while incorporating chat history. By leveraging advanced technologies such as OpenAI’s GPT-3.5-turbo and Astra DB, the chatbot can efficiently process and retrieve relevant information, generating accurate and contextually aware responses.
Our team of highly skilled professionals combines deep knowledge of Cloud, DevOps, Website Development & Custom Development (into ReactJS, NodeJS, Angular, Python, Php, MongoDB).