What the Data for This Category Looks Like
Carbon steel data primarily comes from steel mill production ledgers, weekly and monthly statistical reports from industry associations, real-time quotes from spot trading platforms, and procurement requirement documents from downstream manufacturing enterprises. Update cadence: spot quotes are updated daily, production capacity and inventory data are updated weekly, marketing script packs and application case documents are updated quarterly. Document structures include structured tables (such as material specification sheets, quotes), long text passages (such as downstream application analysis). Structured fields include steel grade, nominal thickness (mm), yield strength (MPa), ex-factory unit price (yuan/ton), delivery cycle (days), and others. Some documents include attached technical parameter attachments.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
There are many structured fields with clear units, so retrieval must match both field names and units to avoid confusion between different steel categories. Frequently updated spot and capacity data require the knowledge base to support incremental synchronization, otherwise retrieval results will have data lag. A mix of long-text application cases and short-text quotes requires adapting segment strategies of different lengths to ensure semantic integrity. Downstream applications of carbon steel cover multiple fields such as construction, machinery, automotive, so retrieval demands are scattered, requiring matching results across more dimensions.
Configuration Settings
| Configuration Key | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Carbon steel documents contain long technical analyses and multiple sets of specification parameters, requiring sufficient space to hold complete segmented semantic content |
recall_count | Top 8 results | Downstream application scenarios for carbon steel are scattered, requiring matching results across multiple dimensions |
similarity_threshold | 0.72–0.78 | Precision requirements for carbon steel industry terminology are high, preventing low-relevant non-target steel category content from being recalled |
chunk_length | 600–800 characters | Balances the integrity of technical parameters and semantic coherence, avoiding splitting that breaks parameter associations |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Carbon steel documents include bulk specification data, leading to longer parsing times, preventing premature termination of parsing |
rerank_top_k | Top 3 results | Focuses on high-match core marketing and technical content, simplifying user filtering workflows |
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 issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Failed to configure
document_tagfiltering rules. The symptom is that retrieval results include multiple unrelated document sets, and accurate recall of the specified carbon steel marketing script packs is not possible. The cause is that tag filtering was not set for the multi-document scenario of carbon steel, resulting in the vector database returning all matching content. - Configured a similarity threshold lower than 0.7. The symptom is that retrieval results mix documents from non-target carbon steel categories such as stainless steel and galvanized steel. The cause is that the threshold was not adjusted to match the precision requirements of carbon steel terminology, resulting in misjudgment of low-relevant content as valid results.
- Did not adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. The symptom is that bulk carbon steel specification document parsing fails, with timeout errors shown in logs. The cause is that the parsing time of carbon steel documents was not accommodated, and the default timeout value caused tasks to terminate prematurely.
How to Confirm Configuration Is Complete
- Enter the FastGPT knowledge base management page, check the parsing task list for carbon steel documents, and confirm that all task statuses are successful, with no timeout or format errors.
- Initiate a test retrieval, enter a query containing specific carbon steel specifications and terminology, verify the field matching degree and unit consistency of returned results, and confirm that no unrelated steel category content is mixed in.
- Trigger an incremental synchronization task, upload a new carbon steel spot price document, and confirm that the data is synchronized within 1 hour and can be retrieved.
- Check the parameter items in the AI configuration interface, confirm that settings such as similarity threshold and recall count have been configured as required. For the open source version V4.8.22, confirm whether custom reference logic is implemented via workflows.
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.