What the data for this category looks like
Industrial park marketing-related data mainly comes from three channels: park operation management systems, settled enterprise ledgers, and offline event registration systems. There is no fixed cycle for data updates. Changes to settled enterprises, rent adjustments, and monthly event previews are updated alongside business progress. Overall park plans and policy documents are updated quarterly or annually. Document structure is divided into two categories: standardized fields and non-standardized text. Standardized fields include total park area (unit: square meters), per-workstation rent (unit: yuan/square meter/month), and the industry of settled enterprises, among others. Non-standardized text includes investment policy explanations, park location advantage descriptions, and past event reviews, among others. The length of single documents varies widely.
What constraints these characteristics impose on model access and configuration
Dispersed storage of multi-source data requires configuring cross-system access permissions and mapping rules to avoid incomplete data pulling. The mixed structure of non-standardized text and standardized fields requires configuring segmentation rules and field extraction parameters during document parsing, to ensure the model can recognize both numerical indicators and descriptive content. Uncertain update rhythms require configuring flexible adjustment mechanisms for scheduled synchronization tasks, to adapt to content update frequencies across different business scenarios. The wide variation in single document length requires configuring parsing timeout parameters for large files, to avoid interruptions during long document parsing.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Industrial park marketing content often includes multiple sections of policy text and enterprise directories. Excessively long context can trigger model truncation. This value range covers most content lengths in marketing scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large documents such as park investment brochures and planning drawings takes a long time. This value prevents timeout errors during standard large document parsing |
RECALL_SIMILARITY_THRESHOLD | 0.75–0.85 | Precise matching of user inquiries about park location, rent and other detailed needs is required. This threshold range filters low-relevance recall content |
UPLOAD_FILE_MAX_SIZE | 500 MB | The file size of large park planning drawings and complete investment brochures usually does not exceed this range. This value supports upload of most marketing materials |
Scheduled Sync Task Interval | Every 12 hours | Updates to park settled enterprise information and event information occur at a moderate frequency. This interval balances content timeliness and system resource consumption |
The parameter values provided on this page are all conventional recommendations used as 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 Configuration Mistakes
- A
do_request_failederror is returned when calling the model. The cause is that the intranet access whitelist for the park data source is not properly configured, preventing FastGPT from pulling business data from the park operation system. - Marketing content recalled by the knowledge base lacks settled enterprise-related fields. The cause is that the
company_namefield from the park backend was not mapped to the specified storage field in the knowledge base during field mapping configuration. - Parsed content from uploaded large investment brochures is truncated. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted to a value suitable for large documents, leading to timeout interruption during the parsing process.
How to Confirm Proper Configuration
- Run a manual synchronization task, check whether the latest park event information has been added to the knowledge base, and verify that the synchronized fields match the original data in the park backend.
- Initiate a simulated user inquiry, test whether the recalled content meets expected matching accuracy, and adjust the similarity threshold to adapt to specific inquiry scenarios.
- Upload the largest available park marketing material, check whether the parsing progress completes normally without errors or content truncation.
- Configure a test content trigger rule, verify that the model can normally pull the configured data source content during calls, and output expected marketing responses.
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.