What the Data for This Category Looks Like
The data for education service intelligent due diligence reports mainly comes from school operation license documents, teacher qualification files, course teaching syllabi, student survey records, annual financial audit reports, and public information released by local education regulatory authorities. The data update rhythm is adjusted according to the effective cycle of corresponding documents. School qualification data is updated once a year, course syllabi are adjusted per semester, and student survey records are summarized monthly. Most document structures are a mix of structured forms and unstructured text. Fields include the unified social credit code of the school operator, years of teacher experience, number of course hours, and revenue amount in audit reports, with corresponding units: string, year, class hour, yuan.
What Constraints These Characteristics Impose on Deployment and Upgrade
The multi-source, mixed structure and differentiated update rhythm of education service due diligence data impose multiple constraints on deployment and upgrade workflows. Mixed-format documents require adaptation to different parsing rules, so parsing and extraction rules for each file type must be configured in advance. Differentiated update rhythms require layered scheduled synchronization tasks to avoid resource occupation from high-frequency pulls of regulatory public data. Uploading and parsing large unstructured audit reports requires adjusting file size limits and timeout thresholds to prevent upload exceptions. Compliance check configurations must be updated alongside local regulatory requirements to ensure data check logic aligns with latest standards.
How to Set Configuration Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1500–2000 MB | Education service due diligence reports often include multi-page audit documents and collections of teacher files, resulting in large single-file sizes |
PARSE_FILE_TIMEOUT_SECONDS | 600–1200 seconds | Parsing long documents requires traversing large amounts of text, extending the timeout avoids task interruptions |
RECALL_TOP_N | Top 8–12 results | Relevant information for education service due diligence reports is scattered across multiple documents, so more relevant fragments must be retrieved to cover complete check logic |
Segment Length | 800–1200 characters | Education service documents include structured forms and long text passages, this segment length balances parsing efficiency and context completeness |
SYNC_TASK_INTERVAL | Layered configuration by document type: qualification data 86400 seconds, course data 604800 seconds | Different types of data have different update rhythms, layered configuration optimizes resource usage |
The parameter values provided on this page are general recommendations for starting point configuration. Actual values are affected by material format, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After deploying via Docker, calling the built-in GPT interface returns an error, and the interface prompts that the model is unavailable. Cause: The large language model API key or access address is not configured correctly, so the platform cannot call the built-in model service.
- Phenomenon: When uploading a due diligence document larger than 100 MB, the interface shows a timeout error, but the background task is still running. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to a threshold suitable for long documents. The default timeout value in version v4.8.10 is insufficient to complete parsing. - Phenomenon: Workflows cannot trigger multi-turn conversations, but simple applications support normal multi-turn interactions. Cause: The workflow does not enable the context transfer switch, or the
maxContextparameter is not configured to retain conversation history, so each request only carries a single input.
How to Confirm Configuration is Correct
- Upload a typical education service due diligence document, check whether the upload progress and parsing results are normal, and confirm that the
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSconfigurations match the current document size. - Initiate a multi-turn conversation test, check whether the conversation history is retained, and confirm that the context transfer configuration and
maxContextparameter values match business requirements. - After configuring the scheduled sync task, check the task scheduling logs to confirm that the sync intervals for different types of data align with the expected update rhythm.
- Call the application interface to verify that the returned results include the key fields required for the due diligence report, and confirm that the retrieval configuration and similarity threshold values cover the core check logic.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.