What the data for this category looks like
Internal office data for grain reserve management is primarily sourced from daily grain depot operation logs, grain rotation plans, incoming quality inspection reports, inventory dynamic reports, emergency dispatch notices, and similar materials. Data update cadences cover multiple dimensions: daily logs are updated each day, rotation plans are released quarterly, individual batch quality inspection reports are updated alongside the incoming goods process, and annual summary documents are updated each calendar year.
Documents fall into two categories: structured and unstructured. Structured data includes fields such as warehouse number, stored grain type, inventory weight, moisture content, and shelf life, with units of tons, percentage, and days. Unstructured data is mostly long-form text, such as regional grain condition analysis and emergency response plans.
What constraints these characteristics impose on the knowledge base retrieval and recall link
The precise matching requirement for structured fields means retrieval must support filtering by field name, to avoid irrelevant historical inventory data being included in results. After segmented recall for long-text documents, secondary sorting is needed to screen core information; otherwise, employees will spend extra time identifying valid content.
The multi-dimensional update cadence requires retrieval results to distinguish currently valid inventory from historical archived data, to prevent outdated information from being recalled first. Specific unit fields must be automatically associated and matched during retrieval. For example, when querying "grain types with excessive moisture", the system must automatically associate the field rule "moisture content > 14%".
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Matches the structured field precision requirements for grain reserve management, filters low-relevance non-inventory documents |
limit | Top 8 entries | Controls the number of documents recalled per round, adapts to the scenario where internal employees quickly browse business documents |
usingReRank | Enabled | Performs secondary sorting on segmented recall results for long documents, elevates the priority of core business information |
datasetSearchUsingExtensionQuery | Enabled | Automatically supplements extended query terms related to grain types and warehouse numbers, meets the retrieval requirements for structured fields |
searchMode | Hybrid retrieval | Supports both full-text retrieval and structured field filtering, adapts to different types of employee queries |
Three Common Misconfigurations
- Symptom: A
Cannot convert undefined or null to objecterror pops up during workflow runtime. Cause: ThecollectionIdsparameter is not configured correctly, and the associated grain reserve knowledge base collection is not specified, so the system cannot generate a valid context object during retrieval. - Symptom: A 1500-character reference limit is set, but document blocks exceeding this length are still included in the context. Cause: No block length verification rule is associated, or the configured
limitparameter is not bound to the segment length limit, causing ultra-long blocks to be recalled directly. - Symptom: After the retrieval results are processed by the HTTP node, no valid reference data is sent to the AI chat node. Cause: Structured fields returned by retrieval (such as warehouse number, inventory tonnage) are not encapsulated into a standard context format according to business scenarios, so the AI cannot recognize the associated reserve data.
How to Confirm Proper Configuration
- Upload a grain reserve incoming quality inspection report document, initiate a query that includes "warehouse number" and "moisture content", and check whether the retrieval results include structured data for the corresponding warehouse number.
- Adjust the value range of the
similarityparameter, observe changes in the relevance of retrieval results, and confirm that the value meets business requirements. - Trigger a complete workflow test, check whether the knowledge base retrieval results can be correctly passed to subsequent HTTP and AI chat nodes.
- Upload a new rotation plan document, wait 10 minutes, then initiate a relevant query, and confirm that the new document has been included in the retrieval scope.
The configuration values listed above are common starting points. Adjust them based on your own specific samples and business needs.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.