What the Data for This Category Looks Like
Construction machinery party building learning data originates from internal party building ledgers of state-owned equipment manufacturing enterprises, monthly updated industry party building standard documents, and learning records from frontline teams. Most documents are PDF-format policy originals, PPT-format learning courseware, and structured Q&A manuals. These materials include fields such as document number, issuing unit, release date, and applicable job scenarios. Courseware includes duration markers. Individual document sizes range from 10–500 MB. Updates occur once per month, with temporary additional updates when new policies are released.
What Constraints These Characteristics Bring to Knowledge Base Retrieval and Recall
Construction machinery party building data includes structured policy fields, image-text learning courseware, and Q&A manuals. Many individual documents have large file sizes. Retrieval systems must support multimodal content matching and structured field association. The monthly update frequency requires retrieval pipelines to support incremental synchronization. This eliminates resource overhead from full reindexing. Frontline team learning records use mostly colloquial language. Recall models must align colloquial questions with professional terminology. The system must also filter irrelevant job parameter content. This ensures retrieval results focus on party building topics.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Filters construction machinery job parameter content unrelated to party building themes, and focuses on results that meet semantic matching thresholds |
limit | Top 6 entries | Adapts to scenarios with large individual document sizes, and controls the total context length of a single recall |
searchMode | hybrid | Combines semantic retrieval and structured field matching, and adapts to structured party building documents that include policy numbers and release dates |
usingReRank | Enabled | Optimizes recall ranking for image-text mixed party building learning courseware, and improves content relevance |
datasetSearchUsingExtensionQuery | Enabled | Performs semantic expansion on colloquial questions from frontline teams, and matches recall results that better align with party building learning needs |
collectionIds | Only specify party building learning-specific collections | Isolates content from other business knowledge bases, and prevents non-party building documents from appearing in retrieval results |
Three Common Misconfigurations
- Issue: After importing an XLSX-format party building learning ledger, retrieval results cannot match structured entries in the table. Cause: Document extension parsing function is not enabled, and semantic chunking rules for table content are not configured correctly.
- Issue: After uploading an image-text party building learning courseware, text content associated with images cannot be recalled. Cause: Multimodal retrieval configuration is not enabled, and the image-text parsing switch is not turned on.
- Issue: An error occurs when switching embedding models, with a prompt indicating model matching exception. Cause: An embedding model not officially supported by FastGPT is used, or model binding configuration for the retrieval pipeline is not updated synchronously.
How to Verify Successful Configuration
- Upload a test XLSX party building ledger, submit a question that includes policy entries in the ledger, and check whether corresponding content is recalled.
- Upload a test image-text party building courseware, submit a question about the theme of an image in the courseware, and check whether associated content is recalled.
- Submit a party building learning question that uses colloquial language, and check whether recall results focus on party building themes and do not include irrelevant content.
- Adjust the citation display switch in conversation configuration, submit a question, and check whether returned results match the expected citation display state.
The configuration values provided here are common starting points. They should be evaluated against the reader'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.