What Data for This Category Looks Like
Carbon steel investment research data originates from steel mill monthly production reports, industry association public weekly reports, real-time quotes from spot trading platforms, futures market trends, and downstream manufacturing demand survey documents. Spot quotes update daily. Industry association statistical data updates weekly or monthly. Internal enterprise inventory and product yield data syncs weekly. Document formats include structured Excel spreadsheets, with fields such as grade, yield strength, tensile strength, thickness, price. Units are mostly MPa and yuan/ton. It also includes semi-structured PDF research reports. The number of fields per structured spreadsheet and pages per research report vary widely. Calculation or measurement based on internal samples is recommended.
Constraints Imposed on Deployment and Upgrade by These Characteristics
The multi-update frequency, mixed document formats, and structured field properties of carbon steel data create clear constraints for deployment and upgrade. First, daily-updated spot quote data requires incremental sync tasks to avoid full crawling that consumes excessive storage and computing resources. Second, structured Excel spreadsheets need support for multi-sheet parsing and field mapping. During upgrades, existing parsing templates must be retained to prevent invalidation of stored data. Third, multi-dimensional structured fields require enabling field filtering functions in the vector database. This ensures retrieval can narrow recall scope by grade, region, and other dimensions to improve investment research accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Carbon steel research report PDFs can be up to 50 pages long. Parsing requires extended time to avoid task timeouts and interruptions. |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single batch upload of steel mill inventory Excel spreadsheet collections may exceed 1000 MB. This value reserves sufficient space for batch upload content. |
PARSE_CHUNK_SIZE | 800–1000 characters | Matches the average length of paragraphs and structured fields in carbon steel research reports. Prevents overly fragmented or overly long segments that harm vector recall performance. |
Recall count | Top 10 results | Carbon steel investment research requires comparing core data across multiple regions and grades. 10 recall results cover basic comparison needs. |
Incremental Sync Cycle | Every 6 hours | Spot price volatility peaks during morning and evening trading sessions. Syncing every 6 hours balances timeliness and resource usage. |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance non-carbon steel data, retaining content highly matched to investment research topics. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific situations require individual analysis. It is recommended to conduct measurements on internal samples before finalizing values.
Three Common Errors
- Symptom: Retrieval results are not filtered by carbon steel grade or region, returning large volumes of irrelevant steel category data. Cause: The structured field filtering switch in the vector database is not enabled, and only plain text vector recall is used.
- Symptom: All custom investment research applications are lost after upgrading from open source version 4.8.20 to 4.8.21. Cause: The ./data/app directory mounted by the container was not backed up. Incorrect container volume mapping during upgrade led to loss of application configuration data.
- Symptom: API call responses are slow, and memory usage remains high. Cause: The memory usage of the large model and vector database is incorrectly confused. The cache strategy of the vector index was not adjusted for carbon steel's highly structured data.
How to Confirm Proper Configuration
- Upload a carbon steel spot price Excel spreadsheet. Check if the parsed fields include core information such as grade, price, and unit. Verify that the parsing configuration adapts to structured tables.
- Review the running logs of scheduled sync tasks. Confirm that incremental sync tasks execute per the preset cycle, with no timeout or interruption errors.
- Initiate an investment research retrieval. Verify that recall results include carbon steel data from different regions and grades, matching preset recall rules and similarity thresholds.
- Check the docker-compose.yml network configuration. Confirm that the vector database, cache service, and FastGPT container are in the correct network topology, or that local ports are properly mapped.
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.