What the Data for This Category Looks Like
Education service intelligent due diligence report data mainly comes from school qualification documents, teacher filing materials, course syllabus documents submitted independently by educational institutions, as well as public disclosure information from education authorities and third-party compliance audit reports. The data update rhythm is adjusted according to institutional qualification changes or annual compliance inspections, with a regular update cycle of once per quarter. Most documents are multi-page PDF files, and some annual compliance reports can reach hundreds of pages. They contain standardized fields such as school operation license number, number of teachers, course duration (unit: class hours or semester), charging standards (unit: yuan/semester), alongside complex layout elements such as official seals, tables, and annotations.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The long documents and complex layout features of education service due diligence reports require the tool calling link to support large-volume file parsing and multi-layout adaptation. The need to obtain data from multiple sources requires plugins to support two channels: docking with public interfaces of education authorities and uploading local files from institutions. The presence of standardized fields and specific units requires parsing plugins to have field recognition and unit verification capabilities, to avoid extracting vague numerical values without units. The requirement for regular updates requires plugins to be configured with a timed synchronization mechanism, to ensure that due diligence data in the knowledge base always matches the latest institutional qualification and compliance status. The chunking processing of long documents also requires balancing context association and single-segment information density, to avoid losing field association relationships due to overly fragmented chunks, or exceeding the model's processing limit due to overly large chunks.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600-900 seconds | Education service due diligence reports are mostly hundreds of pages of PDFs, and the default timeout is insufficient to complete full parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single due diligence report may contain multiple attachments, and the total file volume exceeds the conventional knowledge base upload limit |
PARSE_CHUNK_SIZE | 1500-2000 characters | Balance context association and single-segment information density after chunking long documents, to adapt to model input limits |
Similarity threshold | 0.75-0.85 | Filter low-match irrelevant text to ensure accurate extraction of fields such as school qualifications and charging standards |
RECALL_TOP_N | Top 8-12 entries | Due diligence report fields are scattered across different chapters, requiring recall of a sufficient number of relevant fragments to cover complete information |
PLUGIN_SCHEDULE_INTERVAL | 86400 seconds | Match the quarterly update rhythm of education service qualifications; daily synchronization can cover temporary qualification changes |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- The phenomenon is a failure when calling the parsing tool to process hundreds of pages of education due diligence PDFs, while documents with dozens of pages can be parsed normally. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the long document parsing time exceeds the default configuration threshold. - The phenomenon is a
Cannot read properties of undefinederror popping up on the interface when calling the locally deployed pdf-marker 4.9.0 plugin. The cause is that the plugin dependency package version is incompatible with the FastGPT 4.9.0 runtime environment. - The phenomenon is that fields such as school charging standards and course duration are missing corresponding units in the due diligence content recalled by the knowledge base. The cause is that a reasonable
Similarity thresholdis not set, and low-match irrelevant text covers valid field content with units.
How to Confirm Configuration Is Complete
- Upload a 200+ page education due diligence PDF, verify that the parsing task is completed within the configured timeout period.
- Call the configured parsing plugin, check whether the returned document fragments include preset fields such as school qualification number and course charging standards, along with their corresponding units.
- Trigger the timed synchronization task, confirm that the latest public disclosure data from education authorities is successfully pulled and updated to the knowledge base.
- Adjust the
RECALL_TOP_Nparameter, verify that the number of context fragments returned by the knowledge base meets the configuration requirements.
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.