Internal Office and Employee Services for Power Generation Groups: Knowledge Base Retrieval and Recall

What Data Looks Like for This Category

Data for internal office and employee services of power generation groups mainly comes from OA systems, production operation and maintenance manuals, safety regulations, employee position manuals, internal notices and other sources. Data update rhythm has two categories: fixed cycle and real-time. Safety regulations are updated quarterly, operation and maintenance manuals are updated monthly, employee manuals are updated annually, and temporary notices are released in real time. Documents include structured fields such as device number, operating parameters, job permissions, and unstructured content such as operation logs, training courseware. Units include MW, hours, person-times and other power industry-specific units.

What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall

Mixed-structure documents require support for both precise field matching and semantic retrieval, to avoid missing structured device parameters or unstructured operation experience content. Long documents such as complete operation regulations can cause excessive token usage after chunking, so adjust chunking and retrieval parameters to control context length. Real-time updated safety notices require the retrieval link to support incremental synchronization, to prevent recalling outdated content. Scenarios with many professional terms need optimized semantic matching accuracy, to avoid mistakenly recalling irrelevant documents.

Configuration Settings

Three Common Misconfigurations

• Phenomenon: Knowledge base semantic retrieval returns multiple results, but the model prompts that no relevant answers were found. Cause: The limit parameter is not restricted, or usingReRank is not enabled, resulting in too many recalled results, context token exceeding the large model's carrying limit, unable to fully load valid content.
• Phenomenon: AI reply results are inconsistent with knowledge base content. Cause: datasetSearchUsingExtensionQuery is configured incorrectly, excessive expansion of user questions causes retrieval intent to deviate from original requirements, or the similarity parameter is set too low, recalling irrelevant documents.
• Phenomenon: Retrieval response time is too long. Cause: No reasonable limit parameter is set, too many long documents are returned in a single retrieval, increasing the time for the large model to load context, triggering timeout errors.

How to Verify Correct Configuration

• Enter the knowledge base retrieval test panel of FastGPT v4.17.0, input a power generation professional question such as "Safety requirements for steam turbine sliding pressure operation", check that the number of returned results matches the limit parameter setting.
• Trigger a complete retrieval process, view the background logs, confirm there are no context token over-limit errors, and the response time meets expectations.
• Adjust the similarity parameter to 0.8, retrieve questions containing professional terms, confirm that the semantic matching degree of returned results meets business requirements.
• Check if usingReRank is enabled, view whether the sorted results prioritize core business content.

The values given 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.