Private AI Infrastructure

There are archives containing project documentation and tender information. These archives can weigh hundreds of megabytes or even gigabytes, containing hundreds and thousands of files in various formats (txt, doc, docx, xls, xlsx, dwg, etc.).

We need to develop a pipeline for guaranteed analysis of such archives. Preliminary steps:

1. A task for archive analysis is created.
2. The archive is extracted (rar, zip, 7z) and all files are extracted. Each file goes into a separate subtask for analysis.
3. Each file is analyzed by a separate thread using a separate agent.
4. Once all files have been analyzed and all subtasks are completed, the main agent performs a final analysis of the contents.

Why this specific approach? The fact is that if you try to stuff all of this into a single agent thread, there is always a risk that at some point it will fail to perform certain actions or stop working entirely. Separate tasks allow, firstly, for a clearer localization of execution requirements and evaluation criteria, as well as more precise control over what has been done and what has not. And even if some tasks fail with errors, the main agent can ultimately perform at least a partial analysis and provide a report indicating which intermediate tasks were not completed and why. In our current use case, even a partial analysis is already valuable because it provides at least some preliminary understanding of the incoming application and gives grounds for deciding whether it is worth studying in more detail or if we shouldn't even waste time on it.&#x20;

Add project documentation analysis features to the AI agent

The current issue is that existing archives may contain not only final files, but also other archives with other files and archives.

Private AI Infrastructure

Active Tasks · 3

Add recursive archive extraction feature

Add PDF readers

Add project documentation analysis features to the AI agent