What this category of data looks like
Data is primarily sourced from internal service guideline documents, historical customer service tickets, government approval and filing documents, and frontline operation and maintenance Q&A records. Update frequency aligns with business needs: policy documents are synchronized and updated quarterly. Real-time consultation tickets are added to the repository daily. Each data entry includes four core fields: question text, standard response, business classification tag, and effective date. The respective units for these fields are character, character, classification code, and date format.
What constraints do these characteristics impose on knowledge base retrieval and recall
Business classification tags require retrieval to target recall by business module. This prevents cross-domain irrelevant results from being included. Newly added real-time consultation tickets require the recall link to support incremental data synchronization. This ensures new questions can be retrieved quickly. The effective date field in policy documents requires retrieval to automatically filter expired response content. This ensures returned results comply with regulations. Dense professional terminology in text requires a reranking step after recall. This optimizes result ranking and improves matching accuracy.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Semantic similarity range matching professional text for power generation groups. Avoids irrelevant results from too low a value, and avoids insufficient recall from too high a value |
limit | Top 8 entries | Covers common multi-step response needs for power generation group service inquiries. Single recall volume adapts to conventional context windows |
usingReRank | Enabled | Optimizes ranking result relevance for power generation business text with dense professional terminology |
datasetSearchUsingExtensionQuery | Enabled | Improves recall breadth for long-text service guidelines of power generation groups via extended queries |
collectionIds | Specified by business module | Targets recall of knowledge base data for corresponding business lines. Avoids interference from cross-category content |
searchMode | Semantic recall | Adapts to text with dense professional terminology for power generation groups. Improves semantic matching accuracy |
Three common misconfigurations
- Phenomenon: Reranking model fails to run. The interface displays model call timeout or insufficient resource errors. Cause: Input limit for the reranking model is not configured based on the average length of knowledge base text for power generation groups. This causes the model to fail to process long text inputs.
- Phenomenon: Knowledge base retrieval results are incomplete. Core service questions are not recalled. Cause:
datasetSearchUsingExtensionQueryis not enabled, orsimilarityvalue is set too high. This filters out some semantically matching results. - Phenomenon: Single retrieval returns more results than the workflow's preset context window. Cause:
limitvalue is set too large. This fails to adapt to the conventional context length for power generation group service responses.
How to confirm configurations are correctly set
- In the test environment of FastGPT v4.17.0, upload a typical service question for a power generation group. Check if the
similarityscore of the returned results falls within the 0.72–0.80 range. - Check the source classification of retrieval results. Confirm only data from the business modules corresponding to the specified
collectionIdsis included. No cross-category irrelevant results should appear. - After enabling
usingReRank, compare result rankings before and after enabling. Confirm results with higher relevance appear first. - After triggering
datasetSearchUsingExtensionQuery, check if the number of recalled results meets expectations. No redundant irrelevant content should be present.
The values provided here 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.