Unleashing the Power of Open Source Generative AI

Unleashing the Power of Open Source Generative AI

Table of Contents

1. Introduction
2. Building the Retrieval Pipeline
3. Encoding and Indexing Text
4. Initializing the Generator Model
5. Querying Pinecone for Relevant Passages
6. Generating an Answer
7. Analyzing the Output
8. Fact-checking with External Sources
9. Asking Specific Questions
10. Conclusion

Introduction

In this article, we will explore the concept of abstractive or generative question answering. We will focus on building and implementing a system that can retrieve relevant documents Based on natural language queries and generate human-like answers. The system utilizes a retriever model to encode and store documents in a vector database, and a generator model to generate answers based on the retrieved documents. We will dive into the code and walk through each step of the process.

Building the Retrieval Pipeline

To start, we need to build the retrieval pipeline. This involves encoding and storing the documents we will be using for our system. In our case, we will be using text from Wikipedia. We will encode the text using a retriever model and store the vector embeddings in a vector database. Pinecone is the tool we will be using for our vector database.

Encoding and Indexing Text

Once we have the documents, we need to encode and index them using the retriever model. We will use the Flex Sentence Embeddings model, which is a powerful sentence transformation model. This model will transform our text into vector embeddings that we can store in Pinecone. We will initialize the retriever model and the Pinecone index, connect to the Pinecone environment using an API key, and Create a new index for our data. Then, we will encode and index the passages, attaching the metadata (human-readable text) to the embeddings.

Initializing the Generator Model

Next, we will initialize the generator model. The generator model is responsible for generating answers based on the retrieved documents. We will be using the bart model, an open-source model that can be easily run in our code notebook. However, there are various other models available, such as GPT-3, that can be used depending on our requirements.

Querying Pinecone for Relevant Passages

With the retrieval pipeline set up, we can now ask questions in natural language. These questions will be passed into the retriever model, which will output a query vector. The query vector is then compared to the previously encoded vectors in Pinecone, and the most relevant passages are returned. This retrieval is not based on keyword matching, but rather on the semantic understanding of the question and the passages.

Generating an Answer

Once we have the relevant passages, we can feed them into the generator model along with the original question. The generator model will use this information to generate an answer in natural language format. We will format the passages and the question in a specific format that the generator model can understand. The generator model will output a set of token IDs, which we can decode into human-readable text using the tokenizer's decoding function.

Analyzing the Output

After generating an answer, we will analyze the output and understand where the information is coming from. We can see which passages are most relevant and how the generator model has synthesized the information to form the answer. This analysis helps us understand the reliability and accuracy of the system's output.

Fact-checking with External Sources

To further enhance the credibility of the answers, we can fact-check them with external sources. If the answer is sourced from a specific document or Context, we can provide a link back to that source. This allows us to verify the accuracy of the information provided by the system.

Asking Specific Questions

We can ask a wide variety of questions using the system. Whether it be historical facts, scientific inquiries, or general knowledge questions, the system is capable of retrieving relevant information and generating answers. We can experiment with different types of queries to explore the capabilities of the system.

Conclusion

In this article, we have explored the concept of abstractive or generative question answering. We have learned how to build a retrieval pipeline, encode and index text, initialize the generator model, query Pinecone for relevant passages, generate answers, analyze the output, fact-check with external sources, and ask specific questions. This system opens up possibilities for automating information retrieval and generating human-like answers to natural language queries.

Highlights

• Building a retrieval pipeline for abstractive question answering
• Encoding and indexing text using a retriever model
• Initializing a generator model for answer generation
• Querying a vector database for relevant passages
• Generating answers based on retrieved documents
• Analyzing the output and fact-checking with external sources
• Asking a wide variety of specific questions

FAQ

Q: How does the retrieval pipeline work?

A: The retrieval pipeline involves encoding and indexing text using a retriever model. The encoded text is stored in a vector database, which can be queried with natural language questions to retrieve relevant passages.

Q: What is the purpose of the generator model?

A: The generator model is responsible for generating human-like answers based on the retrieved passages. It utilizes the information from the passages and the original question to synthesize a coherent answer.

Q: How does the system determine the relevance of passages?

A: The system determines the relevance of passages based on the semantic understanding of the question and the passages. It does not rely on keyword matching but considers the overall context and meaning.

Q: Can the system fact-check the generated answers?

A: Yes, the system can fact-check the generated answers by comparing them with external sources. If the answer is sourced from a specific document, a link can be provided to verify the information.

Q: What types of questions can be asked using the system?

A: The system can answer a wide range of questions, including historical facts, scientific inquiries, and general knowledge questions. It is designed to handle various types of queries and retrieve relevant information.