What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
Oilfield engineering service ledger data creates multiple constraints for the retrieval and recall process. The fixed well ID format requires retrieval to support a combination of exact field matching and semantic retrieval, to avoid recall bias caused by well ID input errors. Individual document lengths vary widely. Long ledgers require proper splitting to maintain retrieval accuracy, while total character count of batch-recalled documents must be controlled to stay within context window limits. Daily updated real-time data requires the retrieval pipeline to support high-frequency knowledge base synchronization, and prioritize recalling construction ledgers from the past 7 days to ensure report generation timeliness. Diverse material usage units require associating unit fields during retrieval, to avoid result bias from semantic confusion.
How to set the configurations
| Config Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | High field precision is required for oilfield ledgers. This range filters low-correlation results while retaining valid recall entries |
limit | Top 8 entries | Single ledger lengths vary widely. 8 entries balance recall quantity and context window usage, adapting to content needs for report generation |
searchMode | Semantic Retrieval | Oilfield ledgers contain many technical terms. Semantic retrieval matches unstructured fields such as operation type and material usage, aligning with business scenarios |
usingReRank | Enabled | Long documents split after processing may have scattered semantics. Reranking improves ranking of high-correlation entries and optimizes content priority for report generation |
datasetSearchUsingExtensionQuery | Disabled | Ledger data has a fixed structure. No additional query expansion is needed, which avoids introducing irrelevant retrieval results |
collectionIds | Bind dedicated knowledge base collection for oilfield engineering services | Isolate ledger data from different business lines, avoid cross-category retrieval interference, and ensure retrieval accuracy |
Three common configuration errors
- Phenomenon: Retrieval time exceeds 60 seconds after consecutive questions in the same conversation window, and normal performance resumes when asking in a new window. Cause: No limit is set on historical information carried in conversation context, so each retrieval carries excessive redundant context, increasing processing load on the retrieval pipeline.
- Phenomenon: Semantic retrieval recall entries have extremely high similarity scores, but content does not match actual query requirements. Cause: The
usingReRankreranking link is not configured, or the similarity threshold is set improperly. This results in only retaining literal matching entries while ignoring valid content with semantic relevance. - Phenomenon: Retrieval results include ledgers not related to oilfield engineering services. Cause: No dedicated knowledge base collection is bound via
collectionIds, so the retrieval scope covers documents from other business lines, leading to result confusion.
How to confirm the configuration is correct
- Log in to the FastGPT configuration backend, verify that retrieval configuration items match the preset plan.
- Enter a query containing a specific well ID and operation type, confirm that retrieval results only return ledgers from the oilfield engineering services dedicated knowledge base.
- Launch 3 or more consecutive similar queries, confirm there is no abnormal fluctuation in single retrieval time.
- Modify the
similarityparameter range, verify that recall result correlation changes logically with parameter adjustments per business requirements.
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.