What This Category's Data Looks Like
Industrial metal financial reports and industry data come from public reports of domestic and overseas futures exchanges, regular annual reports of listed companies, and monthly supply and demand survey data from industry associations. Update cycles include annual full financial reports, quarterly operating data, and monthly industry dynamic updates. Available document formats are PDF annual reports, Excel industry statistical tables, and structured API data. Fields include output, inventory, import and export volume, spot average price, production cost proportion, and more. Units include tons, yuan/ton, ten thousand yuan, and others. Some subcategories also include special parameters such as smelting recovery rate and energy consumption indicators.
Constraints for Deployment and Upgrade
The multi-source, heterogeneous nature of industrial metal data requires compatible parsing and adaptation for three or more data sources during deployment: PDF annual reports, Excel statistical tables, and structured APIs. During upgrade, synchronously update parsing rules to adapt to annual adjustments to exchange disclosure formats. The multi-cycle update demand requires configuring layered scheduled pull tasks during deployment. During upgrade, adjust the incremental synchronization time window to match the update rhythm of monthly and quarterly data. The large number of special fields requires custom knowledge base field mapping rules during deployment. During upgrade, support new special statistical parameters added by industry associations. The near-real-time spot data requirement needs low-latency recall logic configured during deployment. During upgrade, optimize incremental update processing efficiency.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Segment Length | 800–1200 characters | Field descriptions in industrial metal financial reports are mostly coherent long texts. Excessive segmentation breaks parameter associations, while insufficient segmentation loses contextual connections |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | PDF or Excel files of industrial metal financial reports contain multiple sets of tabular data. Complete parsing requires a long processing time |
RECALL_LIMIT | Top 8–10 entries | Industrial metal data has strong field correlations. Too many recalled entries introduce redundant information, while too few recalled entries omit key industry parameters |
UPLOAD_FILE_MAX_SIZE | 500 MB | Some annual industry summary report PDF files have large file sizes. This setting adapts to large file upload requirements |
VLLM_API_BASE | http://localhost:8000/v1 | Matches the port of the vLLM service deployed via docker compose, and complies with official custom model access specifications |
REDIS_SYNC_INTERVAL | 300 seconds | Balances the update delay of monthly industry data and server load, and adapts to multi-cycle data synchronization requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, 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 Mistakes
- Phenomenon: When
segment_lengthis set to 3000 characters, text blocks are lost after knowledge base parsing, and some industry parameter fields are not included. Cause: Industrial metal financial reports have many long text paragraphs. The 3000-character segmentation length exceeds the default block processing limit of the underlying parsing engine, causing edge blocks to be truncated and discarded. - Phenomenon: When deploying on an Alibaba Cloud server, pulling Redis dependent images fails. The interface prompts image pull timeout or permission errors. Cause: The cloud server's image accelerator is not configured, or the outbound access permission for the corresponding port is not open, resulting in failure to pull the official Redis image normally.
- Phenomenon: After configuring the custom model call address, model inference has no response, and returns status code 500. Cause: The port of the custom model is not aligned with the FastGPT container port mapping, or network access for the corresponding port is not opened in the docker compose configuration.
How to Verify Proper Configuration
- Upload an industrial metal enterprise annual report PDF, check the number of parsed text blocks and field integrity, and confirm whether the configuration matches the actual text length and field structure.
- Run the docker compose up command, check whether there are Redis connection timeout or image pull failure errors in the container logs, and confirm that network and image configurations are correct.
- Initiate a financial report analysis query, check whether the returned results include correct industrial metal special parameters, and confirm whether the recall rules and context configurations are reasonable.
- Test the custom model call interface, use the official test script to verify the response status and returned content, and confirm that the model access configuration is correct.
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.