To initiate a DeepResearch query, you first define your parameters and authenticate. Start by logging into the platform using API keys, OAuth, or credentials. Next, specify your research scope: input keywords, date ranges, data sources (e.g., academic databases, news APIs), and filters like language or region. For example, a query about "AI ethics trends 2020-2023" might include sources like arXiv, PubMed, and Twitter. Format the query using the platform’s schema, often JSON or a structured form, ensuring required fields like query_id or priority are set. Validate the syntax to avoid errors before submission.
After submitting, the system parses and queues the query. It checks permissions, validates parameters against available sources, and routes the request to appropriate backend services. For instance, a query targeting scientific papers might trigger a search across JSTOR or Crossref APIs. The platform often processes data asynchronously, especially for large-scale requests, using distributed systems or serverless functions. If the query involves analysis—like sentiment scoring or trend mapping—it may run machine learning models or aggregation pipelines. Errors here (e.g., rate limits, invalid sources) trigger retries or user notifications via email/webhook.
Once processed, results are compiled into structured formats (CSV, JSON) or visual dashboards. You might receive a download link, email notification, or a callback via webhook. For example, a completed query could generate a report with top citations, trend graphs, and source metadata. The system logs the query’s status, execution time, and data usage for auditing. Users can then refine parameters, rerun the query, or export results for further analysis. Post-processing steps like data cleanup or temporary storage deletion may occur based on retention policies.