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Overview Architecture

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The ingestion service is responsible for taking in files from various sources, such as web pages, SharePoint, or Atlassian products, and bringing them into the system. It handles different file types, including PDFs, Word documents, Excel files, PowerPoint presentations, text files, CSV files, and Markdown files. The service converts these files into Markdown format, preserving the structure and extracting important information like titles, subtitles, and tables. It then creates chunks out of these documents and saves them into a vector database and a Postgres database. The ingestion service also handles scalability and performance, as it needs to be able to handle large volumes of documents being ingested into the system.

Ingestion Workers

The ingestion workers are responsible for processing different types of files that are ingested into the system. They take in files such as PDFs, Word documents, Excel files, PowerPoint presentations, text files, CSV files, and Markdown files. Each type of file needs to be processed in a different way to extract the necessary information. For example, in the case of PDFs, the ingestion workers need to extract titles, subtitles, tables, and other relevant information. The workers convert the files into Markdown format, which helps preserve the structure of the document and allows the models to better understand and generate results based on the content. The ingestion workers also create chunks out of the documents, which are then saved into a vector database and a Postgres database. This process allows for efficient storage and retrieval of the ingested documents.

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The theme service is responsible for allowing users to customize the appearance of the Unique financeGPT platform according to their branding preferences. It enables users to set their own colours, logos, and other visual elements to create a personalized and branded experience within the platform.

Anonymization Service

Ensures data privacy by anonymizing sensitive information in user prompts and de-anonymizing model responses.

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The benchmarking enables the client to test prompts on a large scale to ensure a high quality (accuracy) of the output (answers) by automatically comparing answers to the ground truth and creating a score using LLMs and vector distance as well as detections of hallucinations to make sure data and model drift is detected early on.

Analytics

Analytics reports (e.g., user engagement) are available via API or also via Unique UI.

Tokenizers

A tokenizer is a crucial component that processes input text to be understood by the model. It segments text into tokens, which can be words, subwords, or characters. Each token is then matched with a unique integer from a pre-established vocabulary on which the model was trained. For words not in the vocabulary, the tokenizer uses special strategies, such as breaking them down into known subwords or using a placeholder for unknown tokens. Additionally, tokenizers may encode extra information like text format and token positions to aid the FinanceGPT's comprehension. Once tokenized, the sequence of integers is ready for the model to process. After FinanceGPT generates its output, a reverse process, known as detokenization, is used to convert the token IDs back into readable text.

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Unique offers an SDK specifically designed for FinanceGPT via an public API.

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Resources

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Logs

Unique produces these types of logs:

• Applicaiton logs (no CID data): Sent to stdout can be collected by log scrapers.

• Auditlogs (includes prompts and CID data): Sent to encrypted and secured write only storage account. For compliance and investigation puprposes

• DLP Logs (contains CID data): Available via API for Data Leakage pervention purposes and analysis.

• Kubernetes logs: Available for collection via log scrapers

Monitoring:

• Unique provides standart Prometheus metrics per service that can be collected.

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