Boost OpenAI GPT with Open Source Embeddings for Semantic Search

Table of Contents

1. Introduction
2. The Challenge of Using LOMs in a Corporate Environment
3. Feeding Company Information to GPT
4. The Process of Embedding Text Chunks
5. Storing Text Chunks in a Vector Database
6. Using Queries to Retrieve Relevant Information
7. The Key AI Models Involved
8. Dependency on Third-Party Data Security
9. Alternatives to OpenAI's Embedding Model
10. Considerations and Pros/Cons
11. OpenAI's Pricing for Embedding Models
12. Open Source Embedding Models
13. The hugging face Repository
14. The Future of Enterprise LOM Solutions
15. Conclusion

Using GPT for Company-specific Tasks in a Corporate Environment

In the last video, we discussed the challenges faced when using Language Optimization Models (LOMs) within a corporate environment. One significant challenge is that these LOMs, such as GPT-4, are only trained on public domain information up until September 2021. This means that if your company has proprietary information stored within its servers, GPT-4 does not have access to or the ability to answer queries related to that information.

To address this challenge, we need to find a way to provide GPT with your company's information so you can use it for unique tasks within your organization. One approach is to feed the information to GPT by splitting each document into text chunks, which can be as small as a sentence, Paragraph, or page. Each chunk is then processed by an embedding model, which converts the text into numerical representations called embeddings or vectors.

By applying this process to each text chunk, You can generate millions of vectors that represent the meaning of the corresponding text. These vectors are stored in a vector database optimized for semantic search. When an employee queries GPT with a question or task, GPT will extract a query from the prompt and convert it into a vector using the embedding model. This vector is then matched against the vector database to identify the most relevant text chunks for answering the query.

This process involves two key AI models. The first is the LOM, which in this case is GPT-4, responsible for generating high-quality responses to user queries. The Second model is the embedding model, responsible for converting text chunks into vectors. While there are limited options for LOMs that perform at the same level as GPT-4, there are open-source alternatives for embedding models that can match or even outperform OpenAI's offerings.

When deciding between using OpenAI's API or hosting your own embedding model, several factors should be taken into consideration. First is the cost associated with using OpenAI's services, which fluctuates Based on the number of tokens processed. Tokens can be fragments of words, with one token roughly equivalent to 0.75 words. As shown in an example calculation, the cost of converting a document into vectors using OpenAI's AdaV2 model can be relatively affordable.

On the other HAND, relying on a third-party provider such as OpenAI introduces a dependency on their infrastructure and ongoing costs. There is a level of trust required for them to maintain the embedding model and keep it available for your use. Furthermore, as new data is generated, you will need access to the embedding model to convert queries into vectors for searching the vector database.

To explore alternatives to OpenAI's embedding model, the hugging face repository is a recommended resource. This repository offers a wide range of open-source models that can be downloaded and deployed on your own machines. The models undergo evaluation and comparison through standardized tasks, providing insights into their performance across different Dimensions. By leveraging such models, you can reduce your dependency on third-party providers and potentially achieve better results than with OpenAI's embedding model.

While there are ongoing advancements and potential future options for hosting enterprise-grade LOMs or open-source versions, the Current state of technology may not meet the desired requirements for an enterprise environment. However, building the embedding part of the process using open-source models can prove highly beneficial for companies dealing with large volumes of natural language assets.

In conclusion, leveraging GPT for company-specific tasks in a corporate environment requires feeding the necessary information to the model through embedding. The use of open-source embedding models can offer flexibility and control while reducing dependency on third-party providers. As the field evolves, it is expected that hosted enterprise versions or improved open-source LOMs will emerge, further expanding the possibilities for companies to deploy comprehensive and efficient language optimization solutions.

Highlights

• Using GPT for company-specific tasks within a corporate environment poses challenges.
• Feeding company information to GPT involves splitting documents into text chunks and using an embedding model to convert them into vectors.
• Storing these vectors in a vector database allows for efficient semantic search and retrieval of relevant information.
• There are limited options for LOMs that perform at the same level as GPT-4, but open-source alternatives for embedding models are available.
• OpenAI's API provides a quick prototyping option, but considerations of cost and third-party dependence arise.
• The hugging face repository offers a variety of open-source embedding models with competitive performance.
• Hosting enterprise versions or open-source LOMs may become viable options in the future.

FAQ

Q: Can GPT-4 access proprietary information stored within a company's servers? A: No, GPT-4 is trained on public domain information up until September 2021 and does not have access to proprietary data.

Q: How can I use GPT to handle company-specific tasks within my organization? A: By employing an embedding model, you can convert your company's information into vectors and store them in a vector database. GPT can then retrieve the most relevant information from the database based on user queries.

Q: What are the alternatives to OpenAI's embedding models? A: The hugging face repository offers numerous open-source embedding models that can be used instead of OpenAI's models. These models perform on par with or even better than OpenAI's offerings.

Q: Are there cost considerations when using OpenAI's embedding models? A: Yes, the cost is based on the number of tokens processed. However, with prices for embedding models like AdaV2 becoming more affordable, the overall cost can be reasonable for most organizations.