What data for this category looks like
Data for integrated coal and power internal office and employee services comes primarily from enterprise production logs, equipment operation and maintenance systems, safety management platforms, human resources document libraries, and office OA systems. There are three update schedules: production logs are updated daily, compliance safety specifications are updated quarterly, and office processes and employee handbooks are added in real time as approvals or personnel changes occur. Document structure is divided into four categories by position and business: post operation guides, equipment operation and maintenance manuals, safety production specifications, and internal attendance and reimbursement processes. Fields include equipment model, operation standards, compliance clause numbers, and position levels. Units include tons, megawatts, hours, person-times, and others.
What constraints these characteristics impose on knowledge base retrieval and recall
Production logs contain a large number of structured numerical values and professional terms. Retrieval must balance precise matching of industry-specific vocabulary and numerical features to avoid generalized recall. Daily updated production data needs an incremental synchronization mechanism to ensure retrieval results cover the latest content. Post-based documents are divided by role. The retrieval system must filter recall results based on the user’s department and position permissions to prevent cross-permission access to internal sensitive documents. Long documents such as equipment operation and maintenance manuals have a large amount of segmented content. Reasonable chunk length control is required to retain semantic integrity. Documents from multiple sources have different formats. The retrieval system must support parsing logic for multiple document types.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Matches the semantic matching accuracy required for coal and power professional terms, and avoids recalling irrelevant general office documents |
limit | Top 12 entries | Covers the retrieval result volume for three core document types: post operations, production data, and processes, and avoids result overload |
searchMode | hybrid | Combines keyword matching and semantic retrieval to adapt to the mixed retrieval needs of professional terms and structured numerical values |
usingReRank | Enabled | Reorders initial recall results by relevance to correct deviations in semantic matching of professional terms |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands synonymous expressions for professional terms. For example, expanding "raw coal transportation" to "coal transfer" to adapt to the multiple naming conventions of the coal and power industry |
collectionIds | Bound by business domain | Only retrieves document collections corresponding to the target position. For example, operation and maintenance posts only retrieve equipment operation and maintenance and safety specification collections, which complies with internal permission requirements |
Three common mistakes
- Phenomenon: A large number of general office documents unrelated to the target post appear in retrieval results. The similarity threshold shows normal values, but result deviation is obvious. Cause: The
similarityparameter was not adjusted for coal and power professional terms. General semantic matching cannot distinguish term differences between "power generation" and "office" scenarios. - Phenomenon: When using semantic retrieval mode, the semantic scores of some unrelated documents are abnormally high. Cause:
datasetSearchUsingExtensionQuerywas not enabled, and no synonymous expansion was performed for professional terms. This leads to deviations in semantic matching of industry-specific vocabulary, and irrelevant content is incorrectly judged as highly relevant. - Phenomenon: Equipment schematic diagrams embedded in the knowledge base cannot be displayed normally in retrieval results. Only placeholders or failed to load content are shown. Cause: Image resources were not uploaded to the storage bucket associated with the FastGPT knowledge base. Direct use of local paths or unauthorized external links causes resources to fail to load.
How to confirm the configuration is properly set
- Initiate a retrieval for "equipment inspection process" using the account of a front-line operation and maintenance post. Verify that recall results only include equipment operation and maintenance manuals and safety specification documents, with no general office documents.
- Upload a production log document updated on the current day, initiate a retrieval with the corresponding keyword, and confirm that the new document appears in the
limitnumber of results. - Check the reordering marker in retrieval results to confirm that
usingReRankis active, with highly relevant documents ranked at the top of results. - Enter the professional term "raw coal transportation", confirm that retrieval results include documents related to "coal transfer", and verify that
datasetSearchUsingExtensionQueryis active.
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.