What data for this category looks like
Data for hotel and catering investment research comes from multiple channels: regional foot traffic and per-unit-area revenue statistics reports released by industry associations, store reviews and rating data from third-party review platforms, daily price lists from ingredient suppliers, excerpts from brand public quarterly financial reports, and internal operational data such as menus and daily business reports uploaded by stores. Update rhythms vary significantly: ingredient prices update daily, foot traffic statistics update weekly, financial reports update quarterly, and reviews and daily business reports update in real time. Data structures include structured tables (such as individual store revenue details, with fields including store ID, city, per-unit-area revenue, average customer spend, and others), unstructured text (review summaries), and images (store actual scenes, menu images). Field units mostly use standard metrics such as yuan, square meters, and people.
What constraints do these characteristics impose on database and operations
Multi-source data with inconsistent update frequencies requires database operations to support incremental synchronization and multi-source adaptation, to avoid excessive resource usage from full synchronization. Mixed structured and unstructured data types require the database to support both document storage and binary file storage, and a separate storage path must be configured for binary data such as images. Real-time data and historical query requirements require operation strategies to retain full data snapshots for at least 90 days, while optimizing indexes to speed up historical investment research queries. Data scale expansion from growing store counts requires the database to be configured with a sharded cluster to handle increasing storage and query pressure, avoiding single-node overload.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
MONGODB_CONNECT_TIMEOUT_MS | 30000–60000 milliseconds | Adapt to delay fluctuations in multi-source data synchronization, avoid frequent timeouts interrupting synchronization tasks |
DATA_SYNC_INTERVAL | 15 minutes–1 hour | Cover different update rhythms of daily ingredient price updates and weekly foot traffic updates, balance real-time performance and resource usage |
UPLOAD_ALLOWED_MIME_TYPES | image/jpeg,image/png,text/plain,application/json | Adapt to upload needs of menu images, business reports, and structured revenue data |
PARSE_DOCUMENT_MAX_LENGTH | 50000 characters | Handle long-text industry financial reports or multi-store review summary data |
DB_BACKUP_RETENTION_DAYS | 90 days | Meet historical data query and compliance retention requirements for investment research |
ENABLE_INCREMENTAL_SYNC | Enabled | Reduce resource consumption of full synchronization, adapt to incremental update characteristics of multi-source 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 case-by-case analysis, and it is recommended to test against your own samples before finalizing.
Three common mistakes
- Phenomenon: Running
pnpm devin a local development environment prompts a MongoDB connection timeout, and the console returns theETIMEDOUTstatus code. Cause: TheMONGODB_CONNECT_TIMEOUT_MSparameter was not adjusted, and the default value is too short to adapt to the delay of multi-source data synchronization. - Phenomenon: When calling the model to query the database, the token consumption displayed on the platform does not match the statistics from the API backend, with the difference concentrated in image query scenarios. Cause: The
INCLUDE_IMAGE_TOKENparameter was not configured, and the token overhead from base64 encoding of images was not included in the token statistics range. - Phenomenon: When trying to return jpg images via database queries, the model output only displays text links and cannot render image content directly. Cause: The
DB_IMAGE_RESPONSE_ENABLEparameter was not enabled, and the base64 encoding conversion logic for images was not configured.
How to confirm the configuration is complete
- Run the local development environment startup command, check the console logs to confirm there are no connection errors such as
ETIMEDOUTorECONNREFUSED. - Upload a jpg-format menu image to the knowledge base, check that the image content can be previewed normally.
- Initiate an investment research query that includes images, compare the token consumption displayed on the platform with the statistics from the API backend.
- View the data source list in the database connection module, confirm that all configured data sources are in a normally connected state.
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.