Building an Advanced Question Answering System with GPT-4

Building an Advanced Question Answering System with GPT-4

Table of Contents:

1. Introduction
2. Why Build a Semantic Search and Question Answering System?
3. The Architecture of the System
4. Creating Chunks from Documents
5. Generating Embeddings for Chunks and Queries
6. Storing Embeddings in a Vector Database
7. Using Semantic Search to Match Chunks and Queries
8. Retrieving Relevant Documents
9. Using a Large Language Model for Question Answering
10. Implementing the System with OpenAI and LLM

Introduction

In this article, we will explore how to build a semantic search and question answering system using Python. This system allows You to answer questions from your own data, such as PDF documents or enterprise data, using the power of large language models. We will use the LangChain framework and the Pinecone vector database to store and retrieve the necessary documents and embeddings. But why do we need to build such a system, and why can't we rely on existing models like ChatGPT or GPT?

Why Build a Semantic Search and Question Answering System?

While models like GPT have been trained on vast amounts of internet data, they can produce factually incorrect information at times. To ensure the accuracy and reliability of answers, it’s important to leverage their reasoning capabilities and not just rely on their pre-existing knowledge. By providing our own documents and using a semantic search system, we can ensure that answers come from our trusted knowledge base. Additionally, when dealing with a large number of documents, it's impractical to pass all of them to a language model as Context. Semantic search helps narrow down relevant documents and improves the efficiency of the question-answering process.

The Architecture of the System

Let's understand the overall architecture of our system before diving into the implementation details. The architecture consists of several key components:

1. Document Loading: The system starts by loading the documents that will be used for question answering, such as PDF files or text documents.

2. Chunk Creation: The system splits the documents into smaller chunks. This allows for more specific matching and improves the accuracy of the question-answering process.

3. Embedding Generation: The system generates embeddings for both the document chunks and the user queries. These embeddings capture the semantic information necessary for matching and retrieval.

4. Vector Database: The embeddings are stored in a vector database. We will use the Pinecone vector database to store and retrieve the embeddings efficiently.

5. Semantic Search: When a query is received, the system uses a semantic search algorithm to match the query with the most relevant document chunks. This reduces the number of documents that need to be analyzed further.

6. Document Retrieval: The system retrieves the relevant document chunks Based on the semantic search. These chunks will be used as context for the question-answering model.

7. Question Answering: The system uses a large language model, such as GPT, to answer the user's question based on the retrieved document chunks.

In the following sections, we will explore each of these components in Detail and understand how to implement them using Python and the LangChain framework.

Creating Chunks from Documents

To ensure efficient matching and retrieval, the system splits the documents into smaller chunks. We will use the text splitter utility from LangChain to achieve this. The text splitter recursively splits the text by Paragraph and line, creating chunks of a desired size. By splitting the documents into chunks, we can improve the accuracy and performance of the semantic search and question-answering processes.

Generating Embeddings for Chunks and Queries

To match the document chunks with user queries, we need to generate embeddings for both of them. We will use OpenAI's embedding utility to generate embeddings for the document chunks. These embeddings capture the semantic information of the chunks and allow for more accurate matching. Similarly, we will generate embeddings for the user queries. These embeddings will be used to find the most relevant document chunks for a given query.

Storing Embeddings in a Vector Database

To store and retrieve the embeddings efficiently, we will use the Pinecone vector database. Pinecone provides a simple and scalable solution for storing and retrieving high-dimensional embeddings. We will Create an index in the Pinecone vector database and store the document chunk embeddings in this index. This will allow us to quickly access the relevant document chunks during the question-answering process.

Using Semantic Search to Match Chunks and Queries

Once we have the document chunk embeddings stored in the vector database, we can use semantic search algorithms to match the document chunks with user queries. A semantic search algorithm compares the embeddings of the query and the document chunks and finds the most similar ones. This process helps narrow down the search space and improves the accuracy of the question-answering process.

Retrieving Relevant Documents

Based on the results of the semantic search, we retrieve the relevant document chunks that match the user's query. These chunks will be used as context for the question-answering model. By retrieving only the most relevant document chunks, we can improve the efficiency of the system and ensure that the question-answering model has the necessary information to provide accurate answers.

Using a Large Language Model for Question Answering

With the relevant document chunks and the user's query, we can now pass them to a large language model, such as GPT, for question answering. The question-answering model will use the context provided by the document chunks to generate accurate and relevant answers to the user's query. By leveraging the power of large language models, we can provide more sophisticated and context-aware answers to user queries.

Implementing the System with OpenAI and LLM

To implement the semantic search and question answering system, we will use the LangChain framework and the OpenAI library. LangChain provides utilities for document loading, chunk creation, embedding generation, and more. We will leverage these utilities to build our system. Additionally, we will use the OpenAI library to Interact with the large language models, such as GPT, for question answering. By combining these tools and frameworks, we can create a powerful and efficient semantic search and question answering system for our own documents.

FAQ

Q: Can I use this system with my own custom documents? A: Yes, this system is designed to work with your own documents, such as PDFs or text files. You can load your documents into the system and use them for semantic search and question answering.

Q: How accurate is the question-answering process in this system? A: The accuracy of the question-answering process depends on the quality of the documents, the embeddings generated, and the large language model used. By using semantic search and context-aware models like GPT, the system aims to provide accurate and relevant answers.

Q: Can I use other vector databases instead of Pinecone? A: Yes, you can use other vector databases like Faiss or Elasticsearch if they suit your requirements. The choice of vector database depends on factors like scalability, performance, and ease of use.

Q: How can I improve the performance of the system with large document collections? A: For large document collections, you can optimize the indexing and retrieval processes by using techniques like distributed indexing, sharding, and caching. Additionally, you can explore methods like document summarization or keyword extraction to further narrow down the search space.

Q: Can I use different large language models like GPT-3.5, GPT-4, or GPT-5 in this system? A: Yes, you can switch between different large language models based on your requirements. The LangChain framework allows you to easily specify the model name and use the appropriate model for question answering.

Highlights

• Build a semantic search and question answering system for your own documents.
• Leverage large language models like GPT for context-aware question answering.
• Use semantic search to efficiently retrieve relevant documents from large collections.
• Store and retrieve embeddings in a vector database like Pinecone.
• Improve the accuracy and reliability of answers compared to generic language models.

In conclusion, this article has provided an in-depth understanding of how to build a semantic search and question answering system using Python. By combining different components like document loading, chunk creation, embedding generation, vector database, semantic search, and large language models, we can create a powerful system that enables accurate and context-aware question answering on our own documents. Whether you need to build a knowledge base for your enterprise or provide customer support through a chatbot, this system can be customized to fit your requirements and improve the efficiency of information retrieval.