What does the data for this category look like?
Data sources for this category include passenger flow, revenue, and business operation reports exported from scenic spot operation management systems, policy announcement documents from cultural and tourism authorities, announcements published on official scenic spot accounts and official websites, and third-party cultural and tourism public opinion monitoring data. Update frequencies vary: operational data such as passenger flow and revenue is updated daily; dynamic data such as business format adjustments and facility upgrades is synced immediately upon change; policy documents are collected immediately upon release; public opinion data is updated hourly. Document structure is divided into three categories:
- Structured operational reports, which include fields such as date, visitor reception volume, ticket revenue, business format efficiency, and others.
- Policy documents, which include fields such as document number, issuing authority, effective date, and others.
- Public opinion texts, which include fields such as mention volume, core keywords, communication channels, and others.
What constraints do these characteristics impose on deployment and upgrade?
Differing update rhythms across multi-source data require configuring differentiated scheduled synchronization tasks to adapt to daily, immediate, and hourly update frequencies respectively. Mixed data sources of structured operational reports and unstructured public opinion and policy documents require two sets of parsing rules: one for structured field extraction, and another for unstructured text vectorization. High-frequency updated public opinion data creates write pressure on the vector database, so the number of vector shards must be adjusted during deployment to distribute load. Temporarily modified business format data requires the upgrade phase to support incremental parsing and upload, avoiding service interruptions caused by full index reconstruction. Additionally, cross-source synchronization requirements from scenic spot intranet operation systems require configuring dedicated network access permissions.
How to set the configurations?
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
REDIS_IMAGE | registry.cn-hangzhou.aliyuncs.com/library/redis:7.0.15 | Pulling official Redis images in domestic environments is slow, using Alibaba Cloud official mirrors accelerates deployment |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Scenic spot monthly operation summary reports include multi-business type detailed data, which takes a long time to parse; the default timeout cannot cover the complete parsing process |
EMBEDDING_BATCH_SIZE | 32–64 | The volume of high-frequency updated public opinion data is large, adjusting the batch size balances vector write speed and server memory usage |
RECALL_TOP_N | Top 8 entries | Scenic spot investment research needs to cover multi-dimensional information such as passenger flow, business formats, and policies, appropriately increasing the number of recalled entries to cover more effective reference content |
SYNC_CRON_EXPR | Configured per data source: 0 1 * * * (operational data), */30 * * * * (public opinion data) | Matches the update rhythms of different data sources, avoids repeated synchronization or data lag |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Scenic spot large annual passenger flow analysis reports have large file sizes, supporting large file uploads to fully include historical data |
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, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Symptom: An error of
pull access denied for redisor image pull timeout occurs when executingdocker-compose up, with status codes 500 or 404. Cause: No domestic mirror source is configured, and official Redis images cannot be pulled normally in domestic network environments. - Symptom: After configuring the
qwen3-embedding-8bmodel in FastGPT v4.9.11, index construction fails, and the interface displays model loading timeout. Cause: The model's API address or port is not configured correctly, or insufficient GPU video memory is allocated to the model. - Symptom: After uploading a scenic spot operation report, the parsing task status remains "pending" and eventually times out and fails. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout is insufficient to complete the complete parsing of multi-business format reports.
How to confirm the configuration is correct
- Execute the
docker-compose pullcommand to confirm that all configured images can be pulled normally, with no timeout or permission errors. - Upload a small scenic spot operation report, check the completion status of the parsing task, and confirm that the parsing rules can correctly extract target fields.
- Configure a test synchronization task, manually trigger it, and check whether corresponding data entries are generated in the vector database to confirm that the synchronization process is normal.
- Check system logs to confirm that scheduled synchronization tasks are executed on time according to the preset
SYNC_CRON_EXPRexpression, with no abnormal interruptions.
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.