Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
Data related to document and material drafting for grain reserve mainly comes from inbound and outbound ledgers, rotation plan documents, quality inspection reports, and inventory statistical reports from daily grain depot operations. Data updates fall into two categories: real-time and periodic. Daily inbound and outbound data updates in real time as business occurs. Monthly inventory reports and quarterly rotation plans synchronize at fixed intervals. Most documents have fixed headers, including fields such as grain type, inbound time, storage quantity, quality inspection grade, and bin number. Common units are tons, kilograms, and batch numbers. Supported document formats include Word official documents, Excel spreadsheets, and PDF inspection reports.
Constraints on Knowledge Base Retrieval and Recall
The coexistence of structured ledgers and unstructured official documents requires retrieval systems to support both precise keyword matching and semantic vector retrieval. This avoids missing relevant data when using only a single retrieval method. Real-time updated inbound and outbound data requires incremental synchronization support, to prevent excessive resource consumption from full refreshes. Fixed field structures require retaining header information during chunking, to prevent retrieval deviations caused by semantic fragmentation after splitting. Exclusive industry terms such as "rotation plan" and "aged grain disposal" need dedicated semantic matching rules, to avoid mixing irrelevant content in general retrieval results.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Grain reserve data mostly uses structured fields and professional terms. A threshold that is too low will mix in irrelevant inventory data. A threshold that is too high will miss matching quality inspection reports and rotation plans. |
limit | Top 12 entries | A single batch of document drafting requires covering multiple content types, such as rotation plans, inventory data, and quality inspection information. 12 entries balances data completeness and retrieval efficiency. |
searchMode | hybrid | Supports both vector retrieval and keyword matching. This adapts to precise field matching for structured ledgers and semantic retrieval for unstructured official documents. |
usingReRank | Enabled | Many exclusive industry terms exist for grain reserve. Reranking filters low-relevance general inventory data, improving the targeting of recall results. |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands industry terms such as rotation standards and quality inspection grades. This avoids recall failures caused by differences in professional term expressions. |
collectionIds | Fill in according to the grain reserve knowledge base collection bound to the business | Limits the retrieval scope to only specified grain depot data, avoiding mixing in knowledge base content from other categories. |
Three Common Mistakes
- Symptom: The expected
collectionIdsfield is not returned after calling the retrieval API. Cause: The bound grain reserve knowledge base collection ID is not correctly specified in API parameters, so the interface cannot associate the specified business data. - Symptom: Garbled characters appear during knowledge base parsing after uploading CSV inventory ledgers exported from WPS. Cause: The CSV file was not saved with UTF-8 encoding, causing a character set mismatch during knowledge base parsing and resulting in garbled characters.
- Symptom: The number of retrieval results is far lower than the set
limitvalue. Cause: Thesimilaritythreshold is set too high, filtering out a large number of semantically matching grain reserve-related documents and leading to insufficient recalled data.
How to Confirm the Configuration Is Correct
- Upload a test grain reserve CSV ledger and PDF quality inspection report. Check whether the original field structures such as grain type and inbound time are retained after knowledge base parsing.
- Call the retrieval API, pass the specified
collectionIdsparameter, and check whether the corresponding file collection ID field is included in the returned results. - Adjust the
similaritythreshold, compare the number of recall results under different thresholds, and confirm that the threshold setting meets business requirements. - After enabling
usingReRank, check whether the reranked results prioritize content related to grain reserve document drafting.
The values provided are common starting points and should be measured against the reader's own samples.
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.