What the Data for This Category Looks Like
Data sources for optical optoelectronics intelligent due diligence reports include public reports from optical optoelectronics industry associations, regular disclosure documents from listed entities, parameter documents from third-party testing agencies, and public quotation sheets from supply chain upstream and downstream partners. Update cadences vary: regular disclosure documents update quarterly and annually, industry association data updates monthly, and test reports update with production line batches. Document structures include modules such as enterprise production capacity parameters, supply chain proportion, technical indicator details, and patent information. Fields include "etching accuracy", "substrate thickness", and "production beat", with units nanometers, millimeters, and pieces per hour respectively. Unstructured sections include technical white papers and production line test records.
What Constraints These Characteristics Impose on Deployment and Upgrade
Optical optoelectronics due diligence data contains a large number of high-precision technical parameters. Sufficient vector database storage space and index precision configuration must be reserved during deployment to avoid service lag caused by excessive data volume. Data update cadences differ across multiple sources. Incremental sync task scheduling rules must be adapted during upgrades to avoid excessive server resource usage from full sync operations. There are many structured fields with high unit standardization requirements. Field mapping rules must be preset during deployment to prevent field parsing errors after upgrades. Unstructured production line test documents have long lengths. File parsing timeout settings and segmentation strategies must be adjusted to ensure complete parsing.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600-900 seconds | Optical optoelectronics due diligence reports include numerous long documents and technical parameter tables. Insufficient timeout settings will cause parsing failures |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single due diligence report may include multiple attached documents, resulting in a large overall volume |
MAX_CONTEXT | 8000-12000 characters | Due diligence reports have many structured fields and unstructured content, requiring sufficient context to complete information integration |
RECALL_TOP_K | 10-15 entries | Optical optoelectronics technical parameters have many granular dimensions, requiring sufficient recalled entries to cover core indicators |
MONGO_CONNECTION_POOL_SIZE | 20-30 | Optical optoelectronics due diligence data has high query frequency. Insufficient connection pools will cause request blocking |
SYNC_INCREMENTAL_INTERVAL | 3600 seconds | Balances the update frequencies of different data sources, and balances real-time performance and resource usage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Scenario: After upgrading FastGPT from 4.8.20 to 4.8.21, all custom intelligent due diligence analysis applications disappear, and no corresponding entries appear in the application management page. Cause: Persistent storage volumes were not mounted during the upgrade. Application configuration data was only stored in the container's local file system, and data was cleared after container reconstruction.
- Scenario: When deploying, after removing the default networks configuration in docker-compose.yml, connection to locally deployed Redis and MongoDB services fails, and due diligence data synchronization fails. Cause: After removing the networks configuration, the FastGPT container uses the default bridge network, which is isolated from the local service network and cannot be accessed via container name resolution.
- Scenario: When initiating an optical optoelectronics due diligence query, the returned results include a large number of irrelevant technical parameters, and accuracy decreases. Cause: The similarity threshold was not adjusted for optical optoelectronics high-precision technical parameters. Generic thresholds cannot match the parameter precision requirements of specialized segments.
How to Verify Configurations Are Correct
- Upload a typical due diligence report for the optical optoelectronics industry, confirm that the parsing status shows completed and no corresponding error logs are present, to verify that the file parsing configuration is effective.
- Manually trigger an incremental data sync, check the newly added structured data entries in the vector database, to verify that the sync scheduling configuration is effective.
- Initiate a query for optical optoelectronics technical parameters, confirm that the returned results include preset core fields, to verify that the context and recall configurations are effective.
- Restart the FastGPT service container, check that the custom application list has no missing entries and administrator configurations have not been reset, to verify that the persistent storage configuration is effective.
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.