Open Source Generative AI in Question-Answering (NLP) using Python

This video explores the concept of abstractive or generative question-answering in natural language processing (NLP) using Python. The presenter outlines a process for building a system that can take a question in natural language, retrieve relevant documents or web pages, and generate a human-like answer using a generator model like BART, which is open source. The system utilizes a retriever model to encode text segments from sources like Wikipedia into vector embeddings, stored in a vector database such as Pinecone. The question asked by the user is also encoded into a query vector, which is used to find the most relevant documents based on semantic understanding rather than keyword matching. The selected documents are then fed into the generator model along with the original question to produce a coherent and informative answer. The video provides a step-by-step guide on implementing this system, emphasizing the use of GPU for faster processing and the importance of checking the model's outputs for accuracy, especially when dealing with fact-based questions.

Takeaways

• 📚 We are building an abstractive question-answering system using Python, which will allow us to ask questions in natural language and receive relevant documents or web pages.
• 🧠 A generator model, such as GPT or Bart, will be used to generate human-like answers based on the retrieved documents, providing both the answer and the sources of information.
• 🌐 We will use text from Wikipedia, encode it using a retriever model, and store the resulting vector embeddings in a vector database, specifically using Pinecone for this task.
• 🔍 The retriever model will output a query vector when given a natural language question, which Pinecone will use to find the most relevant documents based on semantic understanding rather than keyword matching.
• 📈 The generator model will take the retrieved documents and the original question, convert them into a suitable format, and generate a natural language answer.
• 💻 We will use the Hugging Face datasets and Sentence Transformers libraries, along with PyTorch, to handle the data and model operations.
• ⚙️ The process involves installing necessary dependencies, loading and preparing the dataset, filtering for relevant documents, and ensuring the use of GPU acceleration for faster processing.
• 📈 The retriever model uses a pre-trained sentence transformer (like mpnet-base) to convert text into vector embeddings that represent the semantic meaning of the text.
• 🔗 The Pinecone index is created with a dimensionality that matches the embedding size of the retriever model, and cosine similarity is used for vector comparison.
• 🔑 An API key from Pinecone is required to connect and interact with the vector database, and the index is created with a specific name and dimensionality.
• 🏗️ The embedding and indexing process involves batching the text passages, encoding them, attaching metadata, and uploading them to the Pinecone vector database.
• ❓ The generator model, such as BART, is initialized and used to generate answers by tokenizing the formatted query and context, then decoding the output tokens into readable text.

Q & A

What is the main focus of the video on open source generative AI in question-answering using Python?

The video focuses on building an abstractive or generative question-answering system using Python. This system allows users to ask questions in natural language and returns relevant documents, web pages, or other sources. It also uses a generator model to produce human-like answers based on the retrieved documents.

What is the role of the retriever model in the question-answering system?

The retriever model encodes the text from documents into vector embeddings, which are then stored in a vector database. When a user asks a question, the retriever model converts it into a query vector that is used to find the most relevant documents to the question from the database.

How does the semantic understanding of the question and documents work in the system?

The system uses semantic understanding to match the question vector with the document vectors, not by keyword matching but by the concept or idea behind the language used in the question and the relevant documents.

What is the function of Pinecone in the retrieval pipeline?

Pinecone is used as the vector database to store and manage the encoded text vectors. It compares the query vector with all the stored vectors and returns the most relevant documents based on cosine similarity.

What is the purpose of the generator model in the question-answering process?

The generator model takes the relevant documents retrieved by the retriever model and the original question, and generates a natural language answer. It can use different models like GPT-3 or BART to produce the answer.

How does the system ensure that the answers are based on the most relevant information?

The system ensures relevance by first using the retriever model to find the most semantically similar documents to the user's question. Then, the generator model uses these documents and the question to produce an answer, ensuring it is based on the most pertinent information.

What is the significance of using an open-source model like BART for the generator model?

Using an open-source model like BART allows for flexibility and customization within the code. It can be easily integrated and run in a code environment like a Jupyter notebook, making it accessible for developers to implement and modify as needed.

How does the system handle large datasets for encoding and indexing?

The system handles large datasets by streaming the data and processing it in batches, which allows for efficient memory usage and processing speed. This approach is particularly useful when dealing with large datasets like the Wikipedia snippets used in the example.

What is the importance of using a GPU for the embedding process?

Using a GPU for the embedding process significantly speeds up the computation. Embedding large amounts of text into vector form can be computationally intensive, and a GPU can handle these tasks much faster than a CPU.

How does the system ensure the accuracy of the generated answers?

The system ensures accuracy by basing the generated answers on the most relevant documents retrieved. Additionally, the user can verify the source of the information by checking the documents used to generate the answer, providing a layer of fact-checking.

What are some potential limitations of the generative AI system?

Potential limitations include the quality and recency of the training data for the generator model, as well as the scope of the documents in the vector database. The system may also struggle with very specific or new topics that are not represented in the training data or indexed documents.

How can the system be improved for better performance?

The system can be improved by using more advanced or specialized models for the retriever and generator components, updating the training data to include more recent and diverse information, and expanding the vector database to cover a broader range of topics.

Outlines

00:00

🚀 Building a Retrieval Pipeline for Abstractive Question Answering

The first paragraph introduces the concept of abstractive or generative question answering, focusing on creating a system that can understand natural language questions and return relevant documents or web pages. It also mentions using a generator model to produce human-like answers based on retrieved documents. The process involves encoding text from sources like Wikipedia using a retriever model, storing the resulting vectors in a vector database such as Pinecone, and then using the query vector to find the most relevant documents. The paragraph sets the stage for building a retrieval pipeline and hints at the use of a GPT model for generating answers.

05:01

📚 Preparing and Encoding the Data Set

The second paragraph delves into the technical steps for preparing the data set, which includes loading Wikipedia snippets from a data sets Hub. It emphasizes streaming the large data set to manage memory efficiently and shuffling it for randomness. The script focuses on filtering for history-related documents and using the Flex Sentence Embeddings model for encoding the text into vectors. It also discusses the importance of using a GPU for faster processing and initializing the retriever model with a specific embedding dimension that matches the Pinecone index requirements.

10:03

🔍 Creating the Pinecone Index and Embedding Passages

The third paragraph outlines the process of creating a new index in Pinecone, connecting to the Pinecone environment, and embedding the passages from the filtered data set. It explains how to use the API key, create an index named 'abstractive question answering', and align the index's embedding dimensionality with that of the retriever model. The paragraph also details the steps to generate embeddings in batches, attach metadata, and upsert the data into the Pinecone vector database, ensuring that the vectors are stored with their associated metadata.

15:03

🤖 Initializing the Generator Model and Querying Pinecone

The fourth paragraph introduces the generator model, which is used to generate natural language answers from the retrieved documents and the original question. It discusses initializing the tokenizer and model, formatting the query and context for the model, and creating helper functions for querying Pinecone. The paragraph also demonstrates how to encode a query, retrieve relevant passages with metadata, and format them for the generator model. It shows an example of generating an answer from a query about the first electric power system and emphasizes the importance of including metadata for human-readable context.

20:05

📉 Evaluating the Model's Performance and Fact-Checking

The fifth and final paragraph discusses evaluating the performance of the question-answering system by asking various questions and examining the answers generated by the model. It highlights the importance of fact-checking the model's responses, especially when the information provided is incorrect or nonsensical, as in the case of a question about the origin of COVID-19. The paragraph also touches on the limitations of the model when it encounters topics outside its training data. It concludes with a summary of the walkthrough and a thank you note to the viewers.

Keywords

💡Abstractive Question Answering

Abstractive Question Answering refers to the process of generating answers that are not explicitly present in the provided text. It requires understanding the context and generating a response that is not just a simple extraction from the text. In the video, this concept is central as it discusses building a system that can take a natural language question and return a human-like, natural language answer based on retrieved documents.

💡Generative AI

Generative AI is a branch of artificial intelligence that focuses on creating new content, such as text, images, or music, that is similar to the content it was trained on. In the context of the video, generative AI is used to produce natural language answers to questions by generating text that is not directly copied from the source material.

💡Retriever Model

A Retriever Model is a type of machine learning model used to find and retrieve relevant information from a dataset in response to a query. In the video, it encodes text from Wikipedia into vector embeddings, which are then used to find relevant documents in response to a user's question.

💡Vector Embeddings

Vector Embeddings are numerical representations of words, phrases, or documents in a continuous vector space. They allow for complex semantic relationships between different pieces of text to be captured and compared. The video discusses using vector embeddings to represent text segments and to perform semantic searches in a vector database.

💡Pinecone

Pinecone is a vector database used for efficient storage and retrieval of vector embeddings. In the video, it is utilized to store the vector embeddings of text segments from Wikipedia and to perform searches to find the most relevant documents in response to a user's query.

💡Generator Model

A Generator Model in the context of the video is a type of neural network that generates natural language responses. It uses the retrieved documents and the original question to produce an answer. The video mentions using an open-source model called BART for this purpose, which is similar to GPT-3 but can be run in a local environment.

💡Semantic Understanding

Semantic Understanding refers to the ability of a system to comprehend the meaning of words, phrases, and sentences in context. The video emphasizes that the system being built is based on semantic understanding rather than keyword matching, allowing it to retrieve documents that are conceptually related to the query, not just those with matching terms.

💡GPT Model

GPT (Generative Pre-trained Transformer) is a type of language model that is pre-trained on a large corpus of text data and can generate human-like text. The video suggests the use of a GPT model as a basis for answering questions, with the added feature of providing sources for the information it generates.

💡BART Model

BART stands for Bidirectional and Auto-Regressive Transformers. It is an open-source model used for natural language generation tasks. The video mentions using BART to generate answers from the retrieved documents and the original question, as an alternative to GPT-3.

💡Data Streaming

Data Streaming is a method of processing data in which the data is sent and processed in chunks or 'streams' rather than all at once. In the video, the large dataset of Wikipedia snippets is loaded using data streaming to manage memory usage and processing time.

💡GPU Acceleration

GPU (Graphics Processing Unit) Acceleration refers to the use of a GPU to speed up computations, which is particularly useful for machine learning tasks that involve large datasets and complex models. The video script mentions ensuring that the runtime is set to use a GPU for faster embedding of text into vector space.

Highlights

The session focuses on abstractive or generative question-answering in NLP using Python.

The goal is to ask a question in natural language and return related documents or web pages.

A generator model will be used to generate human-like answers based on retrieved documents.

Wikipedia text will be encoded using a retriever model to create vector embeddings.

Pinecone will be used as a vector database to store and compare vector embeddings.

The retrieval pipeline is built to find the most relevant documents based on semantic understanding.

The generator model, such as BART, will take relevant documents and the original question to generate an answer.

The process involves installing dependencies like datasets, pineco, sentence-transformers, and pytorch.

Wikipedia snippets are streamed and filtered for history-related documents.

The retriever model uses a pre-trained sentence-transformer to encode text segments.

Pinecone's API key is utilized to connect and create an index for storing embeddings.

The embeddings and metadata of text passages are uploaded to the Pinecone vector database.

The generator model is initialized with a tokenizer and a long-form question-answering model.

Helper functions are created to query Pinecone and format the query for the generator model.

The model generates answers by tokenizing the formatted query and predicting token IDs.

Answers are decoded from token IDs into human-readable text.

The system can answer historical questions, such as when the first electric power system was built.

The model can also be used for fact-checking and verifying the source of information.

The walkthrough demonstrates the practical application of abstractive question-answering using open-source tools.