What this category’s data looks like
White goods investment research data primarily comes from public industry association reports, official brand financial reports, offline store monitoring data, e-commerce platform sales data, patent technical documents, and supply chain quotation databases. Update frequencies vary across sources. Core product parameter documents are updated quarterly. Store sales data is updated daily. Supply chain quotations are updated weekly. Patent documents are updated irregularly alongside application progress. Document structures include structured financial report tables, semi-structured store monitoring reports, and unstructured technical analysis reports. Fields include product SKUs, energy efficiency ratings, core parameters (cooling capacity/volume/washing ratio), sales channel proportions, supply chain cost proportions, and competitor benchmarking data. Units include watts, kilowatt-hours per year, dimensionless values, and other types.
What constraints these characteristics impose on database and operations workflows
Multi-source heterogeneous data formats require databases to support unified access and storage for structured, semi-structured, and unstructured data. Data sources with different update frequencies need customized synchronization strategies. These strategies prevent excessive resource consumption from high-frequency data synchronization and avoid delayed updates for low-frequency data. Inconsistent field units require standardized mapping at the database or parsing layer. This ensures accuracy for investment research analysis. Longer industry report documents need larger storage shards and longer parsing timeouts. High-frequency sales data needs incremental synchronization to reduce operational costs. Investment research scenarios require retaining historical data versions. Database operations must support data rollback and version management.
How to configure settings
This configuration is compatible with FastGPT v4.15. Specific parameters are listed below:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Single large industry report documents can be up to 50 pages long. Parsing takes a long time. This value avoids interrupting parsing tasks due to timeout |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading complete annual industry white papers and large financial report documents. Meets full data import requirements for white goods investment research |
MONGO_CONNECTION_POOL_SIZE | 50–80 | Multi-source data synchronization has concurrent connection requirements. This value balances connection resource usage and synchronization efficiency |
RECALL_TOP_N | 10–15 | Investment research analysis requires multi-dimensional parameter comparison. Too many recall results increase screening costs. This value covers core reference information |
CHUNK_SIZE | 800–1200 characters | White goods parameter documents contain multi-field associated content. Segment length adapts to context association between parameters. Avoids information fragmentation |
DB_VERSION_RETENTION_DAYS | 365 days | Investment research requires comparing cross-quarter and cross-year industry data. Retaining one year of version records meets historical rollback needs |
The parameter values provided on this page are all common 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. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Issue: Connection to the FastGPT local MongoDB database via MongoDB Compass fails. A connection timeout or authentication failure prompt appears. Cause: The
MONGO_AUTH_SOURCEparameter is not correctly configured in the FastGPT environment variables, or MongoDB port 27017 permissions are not enabled. - Issue: FastGPT fails to start when deployed via Docker. Logs prompt a database connection error. Cause: The local MongoDB connection address is not correctly written to the FastGPT
.envconfiguration file, or database username and password configurations are incorrect. - Issue: After importing white goods sales data in Excel format, some fields are empty or have incorrect formatting. Cause: Correct field mapping rules are not specified in the FastGPT table parsing configuration, or merged cells in the Excel file cause parsing failures.
How to confirm configuration is complete
- Run the FastGPT built-in database connectivity detection tool. Verify whether the MongoDB connection is normal. Confirm basic configuration is correct based on the tool's success prompt.
- Upload a white goods Excel test file with multiple fields. Check if the parsed field mapping matches preset rules. Adjust configuration until it meets business requirements.
- Trigger a scheduled synchronization task. Check if the latest industry data is included in the synchronized database. Confirm the update frequency matches the preset rhythm.
- View the database version management logs. Confirm that the historical data retention duration meets investment research rollback needs. Check that version records are complete and traceable.
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.