What the data for this category looks like
Engineering consulting supervision party building learning data comes from three main channels. These are party building learning documents issued by superior party committees, records of party member activities at supervision project sites, and internal party member learning ledgers of the company. Data update frequency follows this schedule: superior documents are synchronized once per month. Internal activity records are organized and archived every quarter. Supported document formats include Word learning manuals, PDF party lesson courseware, and Excel party member learning ledgers. Ledger fields include learning date, party member name, learning topic, assessment points, and attendance status. Units used include hours, person-times, and similar.
What constraints these characteristics impose on knowledge base retrieval and recall
Multi-channel party building data sources require strict limitation of retrieval scope to the supervision-specific party building knowledge base. This prevents irrelevant content from other domains from being included. The single-row record structure of Excel ledgers requires segmentation rules adapted for row-by-row splitting. Default logic will disrupt complete ledger records otherwise. Different document formats require matching parsing configurations. This ensures correct recognition of Word paragraphs, PDF chapters, and Excel row data. The monthly and quarterly update schedule requires knowledge base synchronization tasks to match the corresponding cycles. This avoids outdated or redundant data.
Configuration Settings
This document uses FastGPT v4.17.0 for configuration examples.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Party building content is mostly normative text. Low-match irrelevant results must be filtered to ensure question and answer accuracy |
limit | Top 3–5 results | Answers for party building learning Q&A must focus on core content. Too many citations will disrupt reading experience |
searchMode | Hybrid retrieval | Balances keyword matching and semantic matching. This covers topic terms and detailed content in party building documents |
usingReRank | Enabled | Re-ranks initial recall results. Prioritizes core exam points and assessment items for party building learning |
datasetSearchUsingExtensionQuery | Disabled | Party building content has strong normative requirements. No extra query expansion is needed. This avoids straying from preset learning topics |
collectionIds | Bound to supervision-specific party building knowledge base | Isolates knowledge base data from other categories. Ensures retrieval scope only covers party building materials in this domain |
Three Common Misconfigurations
- Issue: After automatic segmentation of Excel party building ledgers, retrieval results show messy content with spliced cross-row data. Cause: No row-by-row splitting segmentation rule was configured for Excel documents. Default automatic segmentation logic disrupted the single-row record structure of ledgers.
- Issue: Locally unuploaded party building documents are returned as citations. Cause:
collectionIdswas not limited to the supervision-specific party building knowledge base. Retrieval scope covered other unauthorized datasets. - Issue: The number of cited documents returned by retrieval exceeds the preset limit. Cause: The
limitparameter was not set correctly, or the re-ranking step did not retain the initial quantity restriction rule.
How to Verify Proper Configuration
- Upload a test Excel party member learning ledger. Perform segmentation, then check if each segment corresponds to a single row of learning records.
- Submit a test query related to party building. Verify that returned cited documents only come from the bound supervision-specific party building knowledge base.
- Adjust the
similarityparameter and submit the same query. Confirm that result matching accuracy changes as expected with the parameter adjustment. - Check the number of cited documents in retrieval results. Confirm that it matches the value set for the
limitparameter.
The values provided here are common starting points. They should be evaluated against the user’s own sample data.
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.