Internal Office and Employee Services for Refinery and Chemical Production: Knowledge Base Retrieval and Recall

What the data for this category looks like

Data primarily comes from internal process specifications, equipment ledgers, inspection records, safety operation guidelines, and shift team training materials for refinery and chemical production. Frontline production lines provide real-time operation and maintenance records and management documents as data sources. Teams update inspection records daily. Administrators synchronize updates after formal process specifications are revised. Most documents are structured long texts, containing fields such as equipment ID, operating pressure, temperature thresholds, and inspection cycles. Units mostly use international standards and industry common units including megapascals, degrees Celsius, and hours. The corpus includes a large number of refinery-specific professional terms.

What constraints these characteristics impose on the knowledge base retrieval and recall link

Long-text structured documents and frequently updated frontline data from refinery and chemical production impose multiple constraints on the retrieval and recall process. A high proportion of long texts exists. If individual chunks are too long, embedding vectors lose local details. If chunks are too short, the system damages the semantic integrity of professional terminology. Dense specialized professional terms make generic retrieval models struggle to match industry-specific semantics. Users deploy specialized vocabulary lists to optimize recall logic. Daily updated inspection records and real-time operation data require incremental synchronization mechanisms to avoid data lag. The system supports targeted recall by fields such as equipment ID and operating parameters, to match the precise query needs of frontline employees.

Configuration Recommendations

Reference only; verify against the target FastGPT release and deployment documentation.

| limit | Top 8–12 entries | Covers common single-round query needs of frontline employees, avoids excessive results that increase filtering costs |

| searchMode | Specialized semantic matching | Optimizes recall logic for refinery-specific terminology, adapts to retrieval scenarios for long texts and structured data |

| usingReRank | Enabled | Performs secondary sorting on initial recall results by semantic relevance, corrects semantic bias of generic models, and improves matching accuracy for specialized content |

| datasetSearchUsingExtensionQuery | Disabled | Prevents extended queries from introducing non-specialized semantics, retains the precision of original queries, and meets the specialized terminology requirements of refinery scenarios |

| collectionIds | Configured by business domain grouping | Only retrieves knowledge base documents corresponding to production shifts and equipment types, narrows the retrieval scope, and improves recall accuracy |

Three Common Errors

• Phenomenon: Returns "Request failed with status code 400" when adding knowledge base documents. Cause: API permission scope of the document source is not configured correctly, or the document link format does not comply with FastGPT's import verification rules.
• Phenomenon: The API knowledge base automatically carries the Bearer request header, and custom request header configuration is unavailable. Cause: The automatic Bearer supplement function in FastGPT's API knowledge base settings is not disabled, causing request header format conflicts.
• Phenomenon: Retrieval results do not match professional terminology, and a large number of irrelevant generic documents are retrieved. Cause: usingReRank is not enabled, or the set similarity threshold does not meet the requirements of refinery and chemical production scenarios, resulting in insufficient semantic matching accuracy.

How to Confirm the Configuration Is Correct

• Initiate a query targeting refinery-specific professional terms, and check whether the retrieved results include documents from the corresponding domain.
• View the knowledge base synchronization logs to confirm whether daily updated inspection records are executed via the incremental synchronization mechanism.
• Check the request header configuration of the API knowledge base to confirm whether the automatic Bearer supplement function is enabled or disabled as needed.
• View the sorting logic of retrieval results to confirm that the secondary reordered results meet semantic relevance 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.