What the Data for This Use Case Looks Like
The data for duty-free research reports primarily comes from industry reports published by securities research institutes, public survey documents from duty-free industry associations, and operational disclosure information released by brands. Updates are triggered by industry events: temporary updates are made at nodes such as policy adjustments or new store openings, while regular quarterly and annual reports are pushed on a fixed cycle. Individual document lengths vary widely, ranging from several thousand-word industry briefings to tens of thousands-word in-depth research reports. Document fields include off-island duty-free sales revenue, average customer spending, in-store passenger flow, policy effective dates, and more. Some documents include segmented data such as store location and category proportion, with units mostly being RMB yuan, passenger trips, and ten thousand yuan.
Constraints on Deployment and Upgrade
The wide range of document lengths for duty-free research reports requires deployments to support long document parsing and segmented storage, to avoid parsing timeouts or content truncation. The non-fixed update cycle requires upgrades to include configurable custom data source synchronization rules, to accommodate updates triggered by temporary events. The diversity of segmented fields requires enabling structured field extraction during knowledge base configuration, while adjusting matching rules for recall strategies to ensure accurate matching of industry-specific data. Public documents from multiple sources have format differences, so deployments must enable general format parsing adaptation to prevent failed parsing of some documents.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Duty-free in-depth research reports have large individual file sizes, so the upload limit needs to be raised to support importing complete documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long document parsing requires sufficient time to prevent parsing failures due to timeout |
Similarity threshold | 0.75–0.85 | Duty-free research reports contain a large number of segmented industry terms, so matching accuracy needs to be adjusted to filter low-relevance search results |
Recall count | Top 8–12 results | A single research report covers multi-dimensional information, so enough segments need to be recalled to support complete question answering |
aiproxy configuration | Bind model invocation channels | In version 4.9.1, aiproxy is used to uniformly encapsulate model invocation logic and simplify the configuration process for multi-vendor models |
LOCAL_MODEL_API_URL | Fill in according to the actual address of the locally deployed model | The new version of FastGPT supports local model access, so the correct API endpoint needs to be configured to complete invocation |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Unable to access the service via a custom domain after deployment, with logs returning a 404 status code. Cause: Domain resolution and reverse proxy binding were not completed in the server configuration, or the
DOMAINparameter of FastGPT was not set correctly. - Symptom: Model invocation fails after configuring aiproxy in version 4.9.1, returning a
model not founderror. Cause: The model channel of aiproxy was not bound to the actual invoked vendor interface, or permissions for the corresponding model were not enabled. - Symptom: Local research report directory mounted via Docker volumes cannot be automatically synchronized and updated. Cause: FastGPT's automatic synchronization configuration was not enabled, or the mount path did not match the path configured in the knowledge base data source.
How to Verify Successful Configuration
- Upload a test duty-free research report document, and verify that the parsed segmented content is complete, with no truncation or garbled text.
- Submit a search request targeting duty-free industry-specific terms, and verify that the number of returned recall results matches the set recall count.
- Check the model invocation logs to confirm that the aiproxy or local model invocation link is normal, with no error messages.
- Manually trigger a data source synchronization, and verify that new documents in the mounted directory are automatically imported 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.