Uploading and Organizing Documents

omkarspace/synthesis empowers you to bring your research materials into a structured, AI-ready environment. This section guides you through uploading various document types, understanding the background processing, and how documents are organized within your projects.

Getting Your Documents into omkarspace/synthesis

To begin your AI-powered research, you'll first need to upload your source materials into a project.

Select a Project: All documents are associated with a specific project. Navigate to an existing project or create a new one.
Initiate Upload: Within your selected project's interface (e.g., in the project details view), look for an "Upload Document" button or a similar upload area.
Choose Your File(s): Select the document(s) you wish to upload from your local machine. omkarspace/synthesis supports common research document formats.
Confirm Upload: Once selected, confirm the upload to send your files to the platform.

Upon successful upload, your document will be listed within the project, typically showing basic information like its filename and size.

Supported Document Types

omkarspace/synthesis is designed to process a wide range of research-oriented documents. While the exact list may evolve, you can confidently upload:

PDF files (.pdf): The most common format for academic papers and reports.
Microsoft Word documents (.docx): Frequently used for drafts and reports.
Plain Text files (.txt): Simple text-based content.

The system will automatically detect the file type and use the appropriate method for text extraction.

Understanding Document Processing & AI Readiness

After uploading, your documents undergo a crucial background process to make them accessible for AI analysis:

Text Extraction: The system first extracts all readable text content from your uploaded file. This converts documents like PDFs and DOCX into a format that the AI can understand.
Metadata and Storage: The extracted text, along with essential metadata (filename, size, type), is securely stored and associated with your project.
Vector Indexing: The extracted text is then chunked into smaller, meaningful segments, and each segment is transformed into a numerical representation called a "vector embedding." These embeddings are indexed into a specialized vector store.

Why this matters: This vector indexing process is what enables omkarspace/synthesis's AI agents and chat features to quickly and intelligently search, retrieve, and synthesize information from your documents.

Asynchronous Processing and Availability

Important: Document processing, particularly vector indexing, happens asynchronously in the background. This means that immediately after uploading:

Your document metadata will appear in the project.
However, the extracted text may not be instantly available for AI chat or agent runs. It takes a short period for the text extraction and vector indexing to complete.

If you attempt to chat with the AI about a very recently uploaded document, you might receive a message indicating that the document is still being processed or that its content is not yet available. Please allow a few moments for the processing to complete. The system is continuously working to make your data AI-ready.

Organizing Your Research with Projects

Projects are the fundamental organizational unit in omkarspace/synthesis.

Centralized Research Hub: Each project acts as a dedicated workspace for a specific research topic, paper, or idea.
Document Association: All uploaded documents are directly associated with the project you're currently working on. This ensures that your AI analysis, generated papers, and insights are always derived from the relevant source material.
Overview and Analytics: The dashboard and project detail views provide an overview of your documents, including counts and contributions to project-level analytics (e.g., total documents, word count trends).

By grouping your documents into well-defined projects, you maintain clarity, focus your AI's attention, and streamline your research workflow.