Database and Operations for Carbon Steel Investment Research

What This Category of Data Looks Like

Carbon steel data primarily comes from steel mill ex-factory price reports, commodity exchange spot price systems, industry association monthly monitoring reports, and customs import and export statistics. Update cycles vary: spot prices are updated daily, steel mill ex-factory prices are mostly updated weekly or every 10 days, overall industry data is updated monthly, and import and export data is updated every 10 days. Documents include structured detailed tables (such as grade, specification, price, inventory), semi-structured industry analysis documents, and unstructured research reports. Core fields include carbon steel grades (such as Q235, HRB400), specifications (thickness, width), origin, transaction price (unit: yuan/ton), inventory (unit: 10,000 tons), and statistical date.

Constraints Imposed on Database and Operations Workflows

The multi-source nature and differentiated update cycles of carbon steel data require database operations to adapt to different synchronization cycles. This avoids excessive resource usage from high-frequency synchronization or data lag from low-frequency synchronization. There are many structured fields with specific units. Data validation must be completed during data ingestion to prevent retrieval errors caused by unit confusion. Storage and parsing of long research report documents require the database to support large field storage. Workflow queries must handle complex multi-table join logic. This places higher requirements on database connection pools and timeout settings. Accurate retrieval of carbon steel data relies on fast filtering by dimensions such as grade and specification. Targeted index creation is needed to optimize query efficiency.

Recommended Configuration Settings

Configuration Item	Recommended Value	Rationale
`RECALL_COUNT`	Top 10-15 entries	Carbon steel data has many structured fields per entry. Too many recalled entries increase context processing load. Too few will lose critical associated information such as grade and specification.
`SIMILARITY_THRESHOLD`	0.72-0.80	Text similarity between similar grades and specifications of carbon steel is high. A threshold that is too low will include irrelevant data. A threshold that is too high will miss valid matching results.
`DB_QUERY_TIMEOUT`	300 seconds	Multi-table join queries for historical inventory and production volume of carbon steel take a long time. An overly short timeout setting will interrupt normal queries.
`DB_INDEX_FIELDS`	`["steel_grade", "specification", "trade_date"]`	High-frequency query dimensions for carbon steel data are grade, specification, and trade date. Creating indexes can significantly improve retrieval speed.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Carbon steel industry research reports are mostly long documents. Parsing takes a long time. Extending the timeout can avoid parsing interruptions.
`UPLOAD_FILE_MAX_SIZE`	200 MB	Single files such as carbon steel industry white papers and steel mill ex-factory reports have large volumes. Large file upload support is required.

The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Mistakes

The symptom is a 504 Gateway Timeout error returned when a workflow calls the specified database for a query. The cause is failure to adjust DB_QUERY_TIMEOUT to a reasonable duration suitable for carbon steel multi-table join queries.
The symptom is that the knowledge base cannot support follow-up questions. Answers become irrelevant starting from the second question. The cause is failure to configure MAX_CONTEXT_TOKENS or using an overly small value, which fails to retain multi-field associated context information of carbon steel data.
The symptom is a database connection error Unsupported database type. The cause is failure to use the adapted driver for localized databases, and failure to specify database connection parameters for localized replacements in FastGPT configuration.

How to Verify Correct Configuration

Execute a single multi-table join query that includes grade and specification. Check the query duration, and adjust DB_QUERY_TIMEOUT to a value greater than the actual duration.
Initiate 3 consecutive progressive questions related to carbon steel data. Verify that the context is correctly retained, and confirm the validity of the context configuration.
Check the database index status. Confirm that indexes have been created for the steel_grade, specification, and trade_date fields. Verify that retrieval speed meets expectations.
Upload a single standard-volume carbon steel industry research report. Check that the file can be parsed and uploaded normally, and confirm the correctness of the large file upload configuration.

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.