What This Category of Data Looks Like
Grain reserve business data sources primarily include daily inventory ledgers of national and local reserve depots, rotation inbound and outbound documents, quality inspection reports, and officially released reserve management policy documents. Data update rhythm is not fixed: Ledger details are updated in real time during daily inventory rotation, overall inventory data is updated in bulk after quarterly stocktakes, and latest policy documents are pushed immediately during emergency reserve adjustments.
Document structure falls into three categories: Process documents contain complete application, approval, and execution steps. Ledger documents include fields such as reserve grain variety, storage quantity, and affiliated depot. Policy documents include management specifications and execution requirements.
Quantity fields use tons and kilograms as unified units. Process fields include information such as approval nodes and execution time limits.
Constraints on Retrieval and Recall
Grain reserve data characteristics impose multiple constraints on retrieval and recall: First, the data includes a large number of numerical fields with clear units. Retrieval requires precise matching of values and units to avoid irrelevant inventory data being included in results via fuzzy matching. Second, process documents are lengthy. When chunked, sufficient context must be retained to fully convey handling logic, to avoid losing key approval steps after splitting. Third, update rhythms differ significantly between daily ledgers and policy documents. Recall must balance incremental updates and full data timeliness to ensure returned content meets current business requirements. Fourth, format differences across document types are obvious. Different parsing rules must be adapted to fully extract valid information, to avoid missing key values in ledgers or node requirements in processes.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Matches unit-attached inventory values and process keywords in grain reserve data, avoids irrelevant results from too low a threshold, and misses key handling steps from too high a threshold |
limit | Top 8 entries | Covers rotation processes and inventory ledgers from different depots in single-batch recall results, avoids too many results exceeding the context window |
searchMode | semantic + keyword | Matches both semantic and keyword content, adapts to mixed policy texts, practical ledgers, and quality inspection report data |
usingReRank | Enabled | Rearranges recall results by relevance to business handling queries, prioritizes displaying frontloaded approval nodes and execution steps |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands industry abbreviations in business handling, such as expanding "outbound" to "reserve grain outbound approval process", to adapt to reserve industry-specific terminology |
collectionIds | Specify dedicated grain reserve dataset ID | Avoids recalling knowledge base content from other categories, ensures retrieval scope only covers documents related to reserve business |
Three Common Mistakes
- Phenomenon: After importing Markdown files with local image links, images cannot be displayed in search results, or an error prompt "resource does not exist" is returned. Cause: Local images were not uploaded to the online storage service supported by FastGPT, and direct use of local paths caused loading failures.
- Phenomenon: The exported knowledge base file is only a few hundred kilobytes, much smaller than the total size of actually uploaded documents. Cause: Full data export configuration was not enabled, only cached data from the current session was exported, or documents were exported before upload parsing was completed.
- Phenomenon: When calling the API to add content to knowledge base chunks, an "invalid parameter" error is returned. Cause: The dedicated dataset ID corresponding to
collectionIdswas not specified correctly, or business handling process text was not encapsulated into formatted chunk content as required.
How to Confirm Configuration Is Correct
- Upload a test document containing reserve grain inventory values (such as "500 tons of wheat"), initiate a retrieval request, verify that the proportion of content in returned results that precisely matches values and units meets expectations.
- Initiate a query containing industry abbreviations (such as "rotation process"), verify that
datasetSearchUsingExtensionQueryautomatically expands to complete process keywords and recalls relevant results. - Check the order of retrieval results, confirm that after
usingReRankis enabled, approval node content is displayed before non-relevant policy texts. - Call the export interface, verify that the size of the exported knowledge base file matches the total size of actually uploaded documents, with no missing content.
The configuration values listed above are common starting points, and should be evaluated against specific samples relevant to the target use case.
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.