What this type of data looks like
The data for cement-related marketing content primarily comes from internal enterprise product and technical manuals, regional supply guidance price documents, past project bidding documents, compliance quality inspection reports, and customer inquiry logs. Data update rhythm follows business adjustments: immediate updates when product parameters change, quarterly synchronization for regional guidance prices, and monthly supplements for project cases as they are implemented. Most individual documents are structured text, containing fields such as product model, strength grade, packaging specification, compressive and flexural strength indicators, applicable construction scenarios, and more. Most indicator fields use units such as MPa, tons, and cubic meters, and some documents include compliance standard numbers.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
There are many structured fields with precise indicators and classification labels, requiring field-level precise matching during retrieval to avoid fuzzy recall of mismatched product parameters. Regional guidance prices are updated quarterly, requiring the knowledge base synchronization mechanism to support incremental pulling within a time range, preventing expired data from being included in recall results. Documents are supplemented monthly as projects are implemented, with large differences in individual document lengths; some project case documents are long, requiring retrieval to support filtering by document type while controlling the truncation length of single recalled texts. Fields include clear units and compliance numbers, requiring retrieval logic to associate field and unit matching, avoiding invalid recall caused by unit mismatches.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 8-12 | Cement marketing content needs to cover product information for different strength grades and applicable scenarios; 8-12 entries balances information completeness and retrieval efficiency |
similarity threshold | 0.72-0.85 | Cement product parameters have high precision requirements; a threshold that is too low will recall mismatched product models, while a threshold that is too high may miss eligible documents for specific application scenarios |
chunk length | 800-1200 characters | Most cement documents include structured indicators and long-text scenario descriptions; this chunk length can retain complete parameter groups and scenario descriptions, avoiding semantic fragmentation |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Some large project bidding documents have long lengths; parsing timeout will prevent documents from being added to the knowledge base, and 120 seconds covers the parsing needs of most long documents |
metadata filtering rules | Configured by product model, applicable scenario, update time | Cement marketing content needs to accurately match user regional and strength grade requirements; metadata filtering can quickly narrow the recall scope |
incremental synchronization interval | 7200 seconds | Regional guidance prices are updated quarterly; a 7200-second interval ensures data synchronization frequency adapts to business update rhythms while avoiding excessive server resource usage |
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, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- The phenomenon is that after upgrading Docker to version 4.8.20, a
text index required for $text queryerror is triggered during knowledge base retrieval. The cause is that the MongoDB text index rebuild operation was not performed after the upgrade. FastGPT's knowledge base retrieval relies on MongoDB text indexes, and version upgrades may reset or change index configurations. - The phenomenon is that when configuring the Knowledge Base Search node in a workflow to use a variable as the knowledge base selection, the returned result is empty or has no matching content. The cause is that the variable was not bound to an authorized knowledge base ID in advance; the variable does not carry a valid knowledge base identifier, causing the retrieval node to fail to locate the target knowledge base.
- The phenomenon is that knowledge base retrieval takes more than 4 seconds; when using FastGPT version 4.8.17, the average retrieval time is 4-5 seconds. The cause is that no metadata filtering rules were configured, causing the recall scope to cover all knowledge base documents, and the chunk length was set too long, increasing the time required for single-segment text parsing and matching.
How to confirm the configuration is complete
- Run a single-keyword test retrieval using a query term that includes cement strength grade and applicable scenario, then check the field matching degree and unit consistency of the recall results.
- View the knowledge base synchronization records, confirm that the incremental update task triggers at the preset interval, and that the latest regional guidance price documents have been synchronized to the knowledge base.
- Call the API to trigger the workflow and pass the knowledge base ID variable, then verify that the retrieval node can normally return matching content from the corresponding knowledge base.
- Monitor single retrieval time data, then adjust configuration items to meet business time requirements.
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.