Master OpenAI's LLM Model with Python Workshop

Table of Contents:

1. Introduction
2. The Power of Large Language Models
3. Applying Large Language Models to Enterprise Content
4. Using Large Language Models for Question-Answering
5. Challenges of using publicly available language models for Enterprise data
6. Extending Large Language Models to Enterprise Data
7. Value of Large Language Models for Enterprises
8. The Objective of the Video
9. The Functionality of Chat GPT for Organizations
10. Challenges of Scattered Information in Enterprises
11. Combining and Processing Information for Easy Access
12. Applying Large Language Models to Enterprise Data
13. Open Source and Proprietary Language Models
14. Creating a Knowledge Base with Web URLs
15. The H2O Wave Platform and Source Content Data
16. Setting up the Embedding Database: ChromaDB
17. Utilizing the Language Chain Application Framework
18. Accessing Pre-trained Large Language Models with OpenAI
19. The Flowchart of Solution Creation
20. Libraries Used in the Project
21. Collecting Source Content URLs
22. Setting up OpenAI API Key
23. Loading and Processing URLs
24. Creating and Saving Embeddings with ChromaDB
25. Setting up and Using the Large Language Model
26. Asking Questions and Obtaining Answers with the Model
27. Looping Through Multiple Questions
28. Summary and Conclusion

Article:

Leveraging Large Language Models for Enterprise Question-Answering

In recent years, the capabilities of large language models have become increasingly apparent. These models have showcased their ability to process and generate natural human-like text, leading to the question of whether they can be applied to enterprise content. Imagine a Scenario where users can ask questions to large language models in the same manner as interacting with chatbots, and receive answers that are tailored to their specific queries. This would add immense value to any enterprise. However, publicly available large language models are not trained on enterprise data, making it essential to extend their capabilities. The objective of this article is to explore the application of large language models to enterprise data and highlight the benefits they can bring.

The Power of Large Language Models

With the advent of chat GPT, organizations are increasingly interested in having a similar functionality for accessing information about projects or their overall business. Enterprises possess vast amounts of data, scattered across various web URLs, and they desire a solution to consolidate and process this information. The chat GPT's strength lies in its ability to provide accurate and informative answers. It's crucial to understand how a large language model can be trained on the custom data available from multiple web sources. This training allows users to ask questions and receive answers that are akin to the responses provided by the chat GPT, even if the original language model has not been trained on the specific content.

Challenges of Publicly Available Language Models

Publicly available language models, such as Neo GPT and GPT-3, offer the potential for training large language models on custom data. However, accessing these models often requires the use of APIs. Another challenge arises when dealing with enterprise-specific data as existing language models may not be fine-tuned for such content. To address this, a combination of open-source embedding databases and application frameworks, like ChromaDB and Language Chain, can be utilized. These tools allow enterprises to store and process large amounts of content and connect them with the open AI language models.

Leveraging Large Language Models for Enterprise Data

To Apply large language models to enterprise data, we need to follow a multi-step process. First, we must Create a knowledge base by collecting Relevant web URLs associated with the enterprise. These URLs will serve as the source content for training the large language model. Next, the content from these URLs is processed using the open AI text embedding model, which converts the text into vector representations. These embeddings need to be stored in an embedding database, such as ChromaDB, to facilitate easy access and retrieval.

In order to connect the embeddings with the large language models, we employ the Language Chain application framework. This framework acts as an intermediary between the embeddings and the language models, allowing for seamless integration. Finally, we access the pre-trained large language model, such as GPT-3.5 Turbo, using the OpenAI API framework. By combining the embeddings, the Language Chain application, and the large language model, we can create a powerful tool for enterprise question-answering.

Value of Large Language Models for Enterprises

The incorporation of large language models in enterprise workflows can bring significant value. It enables users to Interact with the models in a chat-like manner, posing questions and receiving accurate and relevant answers. Gone are the days of sifting through scattered information across web URLs. With the application of large language models, enterprises can consolidate their data and provide a user-friendly interface for information retrieval. This not only enhances productivity but also facilitates knowledge sharing and decision-making processes within the organization.

Conclusion

Applying large language models to enterprise data opens up new possibilities for improving information retrieval and knowledge sharing within organizations. By leveraging open-source embedding databases, application frameworks, and pre-trained large language models, enterprises can build powerful question-answering systems. These systems enable users to extract insights from enterprise-specific content and improve decision-making processes. As the field of language models continues to advance, the potential for using them in enterprise settings grows exponentially.