What the Data for This Category Looks Like
Data sources for education service investment research primarily include public documents from education regulatory agencies, subject construction research materials, public teaching and research data from educational institutions, and teaching and research updates released by industry associations.
Update rhythm fluctuates based on document type. Policy updates have no fixed cycle. Institutional data updates align with academic year milestones. Industry updates occur at higher frequency.
Document structure covers three categories: long-form compiled files, structured statistical tables, and short-form industry news. Fields include publishing entity, effective scope, applicable education stage, corresponding subject category, and quantitative indicator items. Units include number of people, monetary amount, duration, and similar categories.
Constraints Imposed on Deployment and Upgrade by These Characteristics
Coexistence of multiple document types requires adapting differentiated parsing logic during deployment. Adjust segmentation parameters for long-form compiled files. Enable the structured parsing switch for structured tables.
Uncertain update rhythm requires supporting incremental synchronization mechanisms during upgrade, to avoid resource usage from full re-import operations.
Diversity of fields and units requires configuring custom field mapping rules during deployment, to adapt to format differences across data sources.
No fixed update cycle for policy documents requires upgrade workflows to support on-demand synchronization triggers, to fit this update scenario.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets parsing time requirements for long-form industry compilations and annual report documents, prevents premature parsing termination |
UPLOAD_FILE_MAX_SIZE | 1500 MB | Supports upload and parsing of large policy compilations and multi-volume teaching and research materials |
CHUNK_SIZE | 800–1200 characters | Balances contextual coherence for long documents and information integrity for short news, adapts to multiple document structure types |
RECALL_TOP_N | Top 8–12 results | Covers multi-dimensional data for education investment research, supports comprehensive investment analysis |
SIMILARITY_THRESHOLD | 0.72–0.80 | Filters low-relevance industry updates, retains content highly matched to investment research topics |
PARSE_STRUCTURED_TABLE | Enabled | Extracts structured statistical content from education investment research data, preserves original field and unit information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Phenomenon: Chinese garbled characters appear after importing the latest CSV file, and character encoding settings have been adjusted. Cause: The matching encoding format for the CSV file is not specified in FastGPT's file upload configuration, or the default encoding configuration was reset after an upgrade.
- Phenomenon: After upgrading to version 4.10.x, the previously indexed knowledge base cannot return vector search results, and no matching content is found in searches. Cause: The vector database index format changed between versions 4.9 and 4.10, and no index reconstruction or database migration operation was performed.
- Phenomenon: After upgrading from 4.9.13 to 4.10.1, existing knowledge base content is not displayed in the new environment. Cause: No database migration script was executed, and existing metadata and vector data were not synchronized to the new version's database path.
How to Verify Proper Configuration
- Upload a typical long education industry document, check the parsed segmentation results, confirm that the segment length matches the preset configuration.
- Import a structured CSV table, check that the extracted fields and units are complete, confirm that the structured parsing switch is enabled.
- Initiate a vector retrieval test, verify that the number and similarity of returned results fall within the preset threshold range.
- Perform an incremental synchronization operation, confirm that new data is correctly loaded into the knowledge base.
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.