Description: This project demonstrates how to use the Haystack library for Generative Question Answering (QA) in a Jupyter Notebook on Google Colab. It utilizes the Haystack library to perform QA tasks, combining a Dense Passage Retriever and a Sequence-to-Sequence (Seq2Seq) Generator to answer questions. The project is particularly geared towards answering questions based on a set of documents, making it suitable for various information retrieval and QA tasks.
- Mounting Google Drive to access data.
- Installation of required dependencies and libraries.
- Setting up logging for monitoring.
- Importing and configuring a FAISS Document Store for efficient document storage and retrieval.
- Loading and converting documents from a directory into dictionaries.
- Indexing the documents into the Document Store with embeddings.
- Configuring a Dense Passage Retriever for question and passage embeddings.
- Initializing a Seq2Seq Generator for generative question answering.
- Creating a GenerativeQAPipeline to combine the retriever and generator.
- Defining a function to accept user questions and return generated answers.
- Basic understanding of Python and Jupyter Notebooks.
- Access to Google Colab for running the notebook.
- Adequate knowledge of the Haystack library.
To run this notebook, you will need to install the required dependencies. Please follow the installation steps provided in the notebook.
- Upload your document files to a directory in your Google Drive.
- Execute the notebook cells step by step to set up the environment.
- Enter your questions when prompted, and the notebook will provide generated answers based on the documents you uploaded.
- This project uses the Haystack library, developed by deepset-ai. Visit their GitHub repository for more information: Haystack.
- Pretrained models for Dense Passage Retriever and Seq2Seq Generator are used in this project.
This repository contains a Jupyter Notebook demonstrating how to perform Generative Question Answering (QA) using the Haystack library in a Google Colab environment. The project combines a Dense Passage Retriever with a Sequence-to-Sequence (Seq2Seq) Generator to answer user questions based on a set of documents.
- Python knowledge.
- Access to Google Colab.
- Familiarity with the Haystack library.
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Clone this repository:
git clone https://github.com/adimis-ai/Multiple-Document-Abstractive-Long-Form-Q-A.git -
Open the Jupyter Notebook in Google Colab.
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Follow the installation steps provided in the notebook to install the required dependencies.
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Upload your document files to a directory in your Google Drive.
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Execute the cells in the notebook one by one to set up the environment, import documents, and configure the QA pipeline.
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When prompted, enter your questions. The notebook will provide generated answers based on the documents you uploaded.
- Mount Google Drive to access data.
- Install necessary dependencies.
- Set up logging for monitoring.
- Import and configure a FAISS Document Store for document storage and retrieval.
- Load and convert documents from a specified directory into dictionaries.
- Index the documents into the Document Store with embeddings.
- Configure a Dense Passage Retriever for question and passage embeddings.
- Initialize a Seq2Seq Generator for generative question answering.
- Create a GenerativeQAPipeline to combine the retriever and generator.
- Define a function to accept user questions and return generated answers.
- This project uses the Haystack library developed by deepset-ai. Visit their GitHub repository for more information: Haystack.
- Pretrained models for Dense Passage Retriever and Seq2Seq Generator are used in this project.