Using OpenAI's large language models (LLMs) for semantic search for large data. This is because LLMs are trained on massive datasets of text, which allows them to understand the meaning of words and phrases in a way that traditional search engines cannot. This system can be used to find documents that are semantically related to a given query, even if the query is not a literal match for the text in the documents.
Leveraging OpenAI API to generate text completions for the queries. These text completions have been used to refine the queries and improve the accuracy of the search results.
ThirdEye Data builds Semantic Search systems for enterprises to to find information in large datasets of text.
We have prepared advanced Semantic Search systems which are ready to be deployed.
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Before leveraging the semantic search systems, businesses may have some common asks. We are trying to answer them here.
The cost of using OpenAI for semantic search will depend on the size of your dataset and the number of queries you need to process. You can use the OpenAI pricing calculator to estimate the cost of using OpenAI for your specific application.
The main technical challenge of using OpenAI for semantic search is the need for a lot of training data. LLMs require a massive dataset of text in order to learn the meaning of words and phrases. This can be a challenge for businesses that do not have access to large datasets of text.
Another technical challenge is the computational cost of using LLMs. LLMs can be computationally expensive to use, especially for large datasets. This means that businesses may need to invest in specialized hardware in order to use OpenAI for semantic search.
There are a number of revenue opportunities that can be unlocked by using semantic search for large datasets with OpenAI. For example, businesses can use semantic search to improve customer support, increase sales, and reduce costs.
Semantic search engines can be a target for cyberattacks. Businesses need to take steps to ensure that their semantic search implementation is secure. This includes using strong encryption and authentication methods, and monitoring the system for signs of malicious activity.
Semantic search can be used to increase sales in a number of ways. For example, businesses can use semantic search to target customers with relevant advertising. They can also use semantic search to improve the product discovery process by making it easier for customers to find the products they are looking for.
As with any new technology, there are ethical considerations that need to be taken into account when using semantic search for large datasets with OpenAI. For example, businesses need to be aware of the potential for bias in the results of semantic search engines. They also need to be careful not to use semantic search to collect or use personal data in a way that is not compliant with data privacy regulations.
ThirdEye Data brought in their best to support us.
Kumar Mankala Advisor of AI/ML Program & Products, SCE