As businesses grow, so does their internal knowledge base. The ability to retrieve information quickly and accurately is crucial for companies to remain competitive. Traditionally, companies have used keyword searches to retrieve information, but this method has limitations in terms of accuracy and context. However, advances in natural language processing (NLP) and deep learning have made it possible to use GPT models and embedding techniques for more effective internal information retrieval.

GPT models are pre-trained deep learning models that can generate human-like text. By fine-tuning a GPT model on a specific domain or business, it can understand the context of the queries and retrieve more relevant results. Embedding techniques can also be used to represent documents and queries as vectors in a high-dimensional space, allowing for more accurate similarity comparisons (as in the following diagram).

The following architecture makes use of the Azure OpenAI service to enhance an internal knowledge base which can be in multiple formats, and stay in multiple sources: e.g. SQL databases, ElasticSearch, Azure Cognitive, AWS CloudSearch ... Azure OpenAI Service's text embedding models can be used to turn all the documents into so-called embeddings or numerical vectors, which are then stored in vector databases such as Azure Cache for Redis or in a open source vector database like Milvus, Weaviate or Pinecone. When a query is sent by an user, suitable documents that have closest embeddings to the query's embedding are extracted and serve as the context for the query. Finally the query and the context are combined into a message sent to a GPT model in Azure OpenAI Service to receive the answer.

While these advancements in NLP and deep learning have revolutionized internal information retrieval, there are also concerns about the security of sensitive information. Working with GPT models requires the use of large amounts of data, which may include confidential information. OpenAI and Azure Open AI service both have measures in place to protect sensitive information. For example, Azure Open AI service has built-in security features such as identity and access management, data encryption, and compliance certifications.

In conclusion, businesses can greatly benefit from using GPT models and embedding techniques for internal information retrieval. These methods allow for more accurate and contextually relevant results. While security concerns exist, there are measures in place to protect sensitive information. With the help of these technologies, businesses can stay ahead of the competition by quickly and accurately retrieving the information they need.