What the Data for This Category Looks Like
The data for refined oil sales document and material drafting mainly comes from sales ledgers in gas station operation systems, petroleum product quality inspection reports, internal compliant sales specifications of state-owned and public enterprises, customer invoicing archives, and promotional activity plans. Data update cadence: sales ledgers are synced daily, quality inspection reports are updated with each oil product batch, and compliance documents are adjusted quarterly or per regulatory requirements. Most documents are structured tables and standard texts, with fixed fields including oil product grade, transaction time, sales unit (liter/ton), unit price, batch number, sulfur content (ppm), and other standard fields.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Structured sales ledgers mostly consist of batch entries, requiring precise matching by fields such as oil product grade and transaction time to avoid irrelevant data being included due to generalized recall. Daily updated sales data requires the retrieval pipeline to adapt to high-frequency synchronized datasets, preventing the recall of expired same-day transaction information. Compliance documents and quality inspection reports have strong professionalism, requiring priority recall of content that meets regulatory standards. The recall scope must also be narrowed by oil product category to avoid cross-interference between diesel and gasoline documents. State-owned and public enterprise internal approval templates have fixed formats, requiring retrieval to match the field requirements of the templates to ensure recalled content can be reused directly.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Refined oil sales compliance documents and business data have strong professionalism. This range balances recall precision and coverage, filtering irrelevant content with low matching degrees |
limit | Top 10–15 entries | Reference content for sales-related documents does not need to be excessive. 10-15 entries can cover common promotional, invoicing, and compliance scenarios, avoiding context overload |
searchMode | vector | Structured sales data and professional compliance documents are more suitable for vector retrieval, which can accurately match keywords and semantic associations, adapting to the fielded data characteristics of refined oil sales |
usingReRank | Enabled | Semantic similarity of professional documents is easily disturbed by terminology. Reranking filters recall results with semantic deviations, improving the matching accuracy of compliance content |
datasetSearchUsingExtensionQuery | Enabled | Can automatically expand the query scope of business terms, for example, associating "92 octane gasoline" with compliant expressions such as "motor gasoline", adapting to the professional terminology system of refined oil sales |
collectionIds | Bind exclusive refined oil sales dataset ID | Need to limit the retrieval scope to exclusive collections such as gas station ledgers, quality inspection reports, and compliance documents, avoiding invalid data from other categories being included |
Three Common Misconfigurations
- Phenomenon: Retrieval results deviate significantly from the theme of the currently drafted document. For example, recalling diesel quality inspection reports when drafting a sales notice for 92 octane gasoline. Cause: Failed to limit the exclusive dataset via
collectionIds, or set thesimilaritythreshold too low, resulting in documents from unrelated categories being recalled. - Phenomenon: An error of
insufficient_quota Current group upstream load is saturated, please try again lateris returned when calling. Cause: Did not configure a reasonablelimitparameter, with too many single recall entries causing upstream vector service load overload, or did not enableusingReRankresulting in too many returned results occupying quota. - Phenomenon: Drafting a single document takes too long, token consumption exceeds expectations, or sales ledger files larger than 100MB cannot be imported. Cause: Did not limit the value of the
limitparameter, recalling too much redundant data, or did not enabledatasetSearchUsingExtensionQuerycausing expanded queries to introduce additional invalid context. Large-sized files need to be split before import.
How to Verify Proper Configuration
- Enter the FastGPT dataset management page, check whether the currently bound
collectionIdsonly include refined oil sales-related ledgers, quality inspection reports, and compliance documents. - Initiate a test retrieval, input "92 octane gasoline sales compliance requirements", and check whether all returned results are professional documents of the corresponding category, with no cross-category content included.
- View the retrieval log to confirm that the actual values of parameters such as
similarityandlimitmatch the configured values, with no overwriting by default parameters. - Initiate multiple high-frequency retrieval tests, observe the load status of the vector service, and confirm that no upstream load saturation errors occur.
The values provided here are common starting points and should be evaluated 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.