Ask questions on your data with LangChain and GPT

Ask questions on your data with LangChain and GPT

Table of Contents

1. Introduction
2. Using OpenAI's Open Eye Chat GPT
   • Generating embeddings with OpenAI
   • Storing data in the ChromaVector database
3. Pulling data from Wikipedia
   • Using Wikipedia's API
   • Building a knowledge base with Wikipedia articles
4. Writing a Python script for querying Wikipedia
5. Retrieving data from Wikipedia
   • Handling redirect links
   • Extracting plain text from Wikipedia articles
6. Converting HTML to plain text with the Pi pandok library
7. Saving data to a text file
8. Customizing use cases and constants
9. Optimizing text vectorization
10. Using the nearest neighbors approach
11. Asking questions about specific content pieces
12. Additional AI capabilities
13. Conclusion

Using OpenAI's Open Eye Chat GPT

In this article, we will explore the use of OpenAI's Open Eye Chat GPT for answering questions using both existing data and our own knowledge base. Often, the pre-trained OpenAI model may not have complete knowledge on certain topics or may provide incorrect information. By leveraging our own data or documentation, we can improve the accuracy of the responses. To achieve this, we will first generate embeddings of our text data using OpenAI and then store the data in the ChromaVector database. We will also explore how to pull data from Wikipedia to Create our own knowledge base.

Introduction

Have You ever wondered if it is possible to utilize OpenAI's Open Eye Chat GPT to answer questions beyond what it is trained on? Can we tap into our own database or even leverage existing documentation for specialized use cases? In this video tutorial, we will Delve into the process of using OpenAI to answer questions Based on our own data and knowledge. Through a Python script and Data Extraction from Wikipedia's API, we will build a knowledge base to enhance the capabilities of OpenAI's model.

Using OpenAI's Embeddings and ChromaVector

To begin, we need to generate embeddings of our text data using OpenAI's Open Eye Chat GPT model. These embeddings serve as vector representations of our data, enabling efficient storage and retrieval. With the embeddings ready, we will then use the ChromaVector database to store our text data. The combination of OpenAI's embeddings and ChromaVector's query capabilities allows us to efficiently ask questions about our data. In this tutorial, we will provide a step-by-step guide on how to accomplish this using a Python script.

Pulling Data from Wikipedia

One of the most valuable sources of information for building a knowledge base is Wikipedia. By utilizing Wikipedia's API, we can easily pull data from its extensive collection of articles. In our example, we will focus on a master thesis topic related to anomaly detection in reliability engineering. To gain insights into algorithms Relevant to our topic, we will extract data from English Wikipedia articles. The API is free to use, making it an accessible resource for creating our own knowledge base.

Building a Python Script for Querying Wikipedia

To streamline the process of accessing data from Wikipedia's API, we will build a Python script. This script will handle querying the API for specific topics related to our use case. As an example, we will demonstrate how to extract data on algorithms for our master thesis topic. By automating the data retrieval process, we save time and effort in populating our knowledge base. We will also address any potential challenges, such as handling different formatting and redirect links.

Retrieving and Formatting Data from Wikipedia

When retrieving data from Wikipedia, we may encounter cases where our specified topic is not found exactly as we entered it. Wikipedia's API may provide us with redirect links to articles with similar or related content. By leveraging regex and the API's response, we can identify and access the correct data. In this section, we will explain how we handle redirect links and ensure that we retrieve the desired information from Wikipedia.

Converting HTML to Plain Text with pi pandok

Wikipedia articles are often written in HTML format, which may not be suitable for our purposes. To manipulate and analyze the data effectively, we need to convert the HTML content into plain text. We will leverage the pi pandok library, which simplifies the process of converting HTML to plain text. By utilizing a single line of code, we can seamlessly transform the Wikipedia API's HTML response into a readable and usable plaintext format.

Saving Retrieved Data to a Text File

Once we have successfully transformed the HTML content into plain text, we need to save it for further use. In this section, we will demonstrate how to save the retrieved data to a text file. The text file serves as our knowledge base, allowing us to access the information without relying on external sources. By organizing the data in a structured manner, we can easily query and refer to it whenever needed.

Customizing Use Cases and Constants

To adapt the process to suit our specific use cases, we need to make some customizations. In this section, we will explore how to modify the constants in the code to accommodate different topics or areas of interest. For example, if our focus shifts to topics like "Simpsons," "Python," or "JavaScript," we can easily adjust the constants to pull data related to these topics. We will provide clear instructions on how to make these changes and tailor the code to our requirements.

Optimizing Text Vectorization

To enhance the efficiency of text vectorization, we need to optimize the process. By dividing the text into smaller chunks, we can generate embeddings more effectively. In this section, we will explain how to implement a text splitter to divide the text into manageable chunks. By harnessing this optimization technique, we can improve the vectorization process and obtain more accurate results.

Using the Nearest Neighbors Approach

Once we have the vectorized data, we can employ the nearest neighbors approach to answer specific questions. By comparing the vector of the question to the vectorized data, we can identify similar vectors and retrieve relevant information. We will demonstrate how to use this approach in conjunction with ChromaVector and OpenAI's model to answer questions accurately and efficiently. Through examples, we will highlight the effectiveness of this technique in retrieving relevant information.

Asking Questions about Specific Content Pieces

With our knowledge base established and the nearest neighbors approach in place, we can now ask questions about specific content pieces. Whether it is a PDF, a textbook, or any other text source, we can extract information and ask questions using OpenAI's model. This section will showcase the versatility of our approach and its ability to provide answers based on the content we provide. The limitations and scope of this methodology will also be discussed.

Additional AI Capabilities

In addition to answering questions, OpenAI's Open Eye Chat GPT model possesses various other capabilities. This section will touch upon some of these capabilities, such as the ability to understand formulas and convert them into LaTeX format. The integration of AI technologies opens up a world of possibilities and empowers users to explore the vast array of information available. We will dive into some specific use cases and explore the potential of AI and its implications.

Conclusion

In this comprehensive tutorial, we have explored the potential of OpenAI's Open Eye Chat GPT for answering questions using personalized data and knowledge bases. By combining the functionalities of OpenAI's model, Wikipedia's API, and the ChromaVector database, we have demonstrated how individuals can leverage their own data and enhance the accuracy of the responses. By following the step-by-step guide and customizing the code to specific use cases, users can tap into the power of AI and unleash a world of possibilities. With seamless integration of AI technologies, We Are on the cusp of an exciting era of information access and knowledge dissemination.