Build a Question Answering App with Haystack: Step-by-Step Guide
Table of Contents
- Introduction
- Motivation for Building a Question Answering Application
- Example of Question Answering Application
- Advantages of Extractive Question Answering
- Steps to Build a Question Answering Demo
- Haystack: An Open Source NLP Framework
- Creating Pipelines in Haystack
- Using the Haystack API
- Next Steps in Building a Question Answering Application
- Conclusion
🧠 Introduction
In this article, we will explore how to build a question answering application using the open-source framework called Haystack. Question answering applications are useful when searching for specific information within a large corpus of documents, as they can extract Relevant answers to user queries. We will discuss the motivation behind building such applications, the advantages of extractive question answering, and the steps to build a question answering demo using Haystack's NLP framework.
🎯 Motivation for Building a Question Answering Application
The motivation behind building a question answering application Stems from the need to efficiently search for information within a large collection of documents. Traditional document search methods require manual reading and skimming through numerous documents, which can be time-consuming and inefficient. Question answering applications automate this process by extracting the most relevant documents and providing concise answers to user queries.
⚡️ Example of Question Answering Application
Let's consider an example to understand how a question answering application works. Imagine we have a collection of clinical practice guidelines related to oncology. If we have a question like "How many people are affected by cancer-related fatigue?", a question answering application can analyze the documents, retrieve the most relevant one, and extract the answer from that document. The application can then Present the answer, along with the surrounding context, to provide a better understanding of the answer's relevance.
✅ Advantages of Extractive Question Answering
Extractive question answering offers several advantages. Firstly, it guarantees that the answers provided are within the given context since it extracts the answers directly from the text rather than generating new text. This eliminates the risk of the model hallucinating or providing incorrect answers. Additionally, extractive question answering models tend to be smaller and can be easily run on local machines, making them more accessible for individual users.
🛠️ Steps to Build a Question Answering Demo
To build a question answering demo, we will need three Docker images: Elasticsearch for data storage, Haystack for NLP processing, and Streamlit for the user interface. These images can be easily obtained and deployed using Docker commands. Once the demo is set up, users can input their text-based questions, which will be processed using Haystack's retrievers and readers to extract and present the answers from the indexed documents.
🌿 Haystack: An Open Source NLP Framework
Haystack is an open-source NLP framework designed for various NLP tasks, including question answering, semantic search, and summarization. It provides a collection of pre-trained models specialized for different domains, such as Healthcare. Haystack's architecture allows you to build custom pipelines by combining different components, such as retrievers, readers, summarizers, and classifiers. It also offers support for various document stores, including Elasticsearch and other vector-optimized databases.
⚙️ Creating Pipelines in Haystack
Haystack pipelines are the core of question answering applications. They consist of different nodes or components that perform specific tasks. For example, a pipeline may include a retriever to fetch relevant documents, a reader to extract answers from those documents, and a Summarizer to generate shorter summaries. These pipelines can be created using either Python code or YAML files, which provide a more intuitive and easier-to-understand way of defining pipeline architectures.
📡 Using the Haystack API
Haystack provides a RESTful API that allows developers to interact with the question answering application programmatically. The API supports various HTTP methods, such as POST requests for querying the application. The API documentation provides detailed information about the available endpoints and the required parameters. Developers can also experiment with different parameters and observe the API responses to fine-tune and customize the question answering application.
🚀 Next Steps in Building a Question Answering Application
After setting up the question answering demo, there are several next steps to explore. One recommendation is to try out different pipelines tailored to specific use cases. This could involve experimenting with hybrid retrieval pipelines that handle both full-text queries and keyword-based queries. User feedback is also valuable in refining the application's performance. Collecting user feedback and incorporating it into the development process can help improve the relevance and accuracy of the answers generated.
🎉 Conclusion
Building a question answering application using the Haystack framework allows users to search for answers within a large corpus of documents efficiently. By leveraging Haystack's features, such as retrievers, readers, and custom pipelines, developers can create powerful and customizable question answering applications. Additionally, the Haystack API provides a way to programmatically interact with the application, enabling further customization and integration into different systems.
Highlights:
- Question answering applications automate the process of searching for information within documents.
- Extractive question answering guarantees that the answers provided are within the given context.
- Haystack is an open-source NLP framework designed for question answering and other NLP tasks.
- Haystack pipelines can be customized by combining different components for specific use cases.
- The Haystack API allows developers to programmatically interact with the question answering application.
FAQ
Q: Can I use my own data with the Haystack question answering demo?
Yes, you can upload your own files to the Haystack question answering demo by simply adding them to the local Elasticsearch instance. This ensures that your data remains on your own machine and is not sent elsewhere.
Q: Can I fine-tune the language model used in the question answering pipeline?
Yes, you can fine-tune the language model by training it on your specific domain or subdomain data. This allows the model to better understand the vocabulary and context specific to your use case.
Q: Can I calibrate the confidence scores of the question answering model?
Yes, you can calibrate the confidence scores returned by the question answering model to make them more reliable. This can be done by adjusting the relevance scores based on user feedback or evaluating the model's performance with an annotated evaluation dataset.
Q: Can I build a question answering pipeline for tabular data?
Yes, you can build a question answering pipeline for tabular data as well. Haystack provides components that can preprocess and index tables, allowing you to search for answers within table cells along with the surrounding context.
Q: Is Haystack suitable for production-level question answering applications?
Yes, Haystack can be used to build production-level question answering applications. However, considerations should be made regarding model size and computational resources. Fine-tuning the models and tuning the pipeline parameters based on evaluation data can help optimize the application's performance.
