What Data for This Category Looks Like
Internal office data for power generation equipment manufacturing comes from five main sources: equipment operation and maintenance manuals, parts ledgers, production process specifications, employee training documents, and spare parts lists. Data update schedules align with new model launches and process iterations. New model manuals are updated in batches every quarter. Process specifications receive fine-tuning each month. Spare parts lists update in real time as inventory changes. Document formats include long-form operation and maintenance steps, ledger content with structured fields, and PDF work instructions with vector graphics. Common fields include professional attributes such as equipment code, material number, inventory quantity (unit: piece/set), and replacement cycle (unit: hour).
Constraints on Knowledge Base Retrieval and Recall
Long-form operation and maintenance document chunk length directly impacts recall contextual completeness. Chunks that are too long use excessive token window space. Chunks that are too short break semantic links between professional terms. Structured spare parts ledgers have clear unit and number fields. Relying only on vector recall cannot accurately match these structured attributes. This leads to retrieval results that include spare part data from unrelated units. PDF documents with vector graphics may have incomplete text extraction after parsing. This leaves some semantic segments unable to generate valid question-answer pairs. Non-fixed-cycle batch updates require the knowledge base to support incremental synchronization. This avoids the time costs of full database reconstruction.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Similarity threshold (similarity) | 0.75–0.85 | Power generation equipment documents are dense with professional terms. A low threshold includes irrelevant results. A high threshold misses critical maintenance step recalls. |
Single recall limit (limit) | Top 8 entries | Long documents have many chunks. Too many recall results exceed the context window. This harms the coherence of final responses. |
Search mode (searchMode) | Hybrid vector + keyword retrieval | Balances long-text semantic matching and precise structured field matching. This fits the mixed data structure of operation and maintenance manuals and spare parts ledgers. |
Enable re-ranking (usingReRank) | Enabled | Filters redundant chunks with overly high similarity. This improves result relevance ranking. |
Enable query optimization (datasetSearchUsingExtensionQuery) | Enabled | Most employee questions use colloquial language. Optimization helps match professional terms. This boosts recall accuracy. |
Collection ID (collectionIds) | Bind to the dedicated power generation equipment knowledge base collection | Isolates data from other business lines. This prevents result interference from cross-category retrieval. |
Three Common Misconfigurations
- Phenomenon: When uploading a large steam turbine operation and maintenance PDF, the parsing log repeatedly reports slow operations, and MongoDB responses are delayed. Cause: Chunk parameters are not adjusted for long documents. Excessive single-block character count causes long parsing times.
- Phenomenon: When retrieving a spare parts list, returned results include the same model spare parts with both "piece" and "set" units. Field matching fails. Cause: Hybrid vector + keyword retrieval mode is not enabled. Relying solely on vector recall cannot accurately match material number and unit fields.
- Phenomenon: Some training PPT documents with vector graphics do not generate question-answer pairs. The original text is imported directly. Cause: Text extraction optimization is not enabled for PDF/PPT documents with vector graphics. Only a small amount of plain text is extracted, leading to incomplete semantic segments.
How to Confirm Proper Configuration
- Upload one typical steam turbine operation and maintenance PDF. Check if the number of parsed chunks matches the expected single-block length range.
- Input "XX model steam turbine's first maintenance cycle". Verify that the similarity scores of returned results fall within the 0.75–0.85 range.
- Retrieve "#1 unit's standby pump inventory quantity". Confirm that returned results include material number, inventory quantity, and corresponding unit fields.
- Enable re-ranking. Compare result rankings before and after enabling. Confirm redundant results have been filtered.
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.