What the data for this category looks like
Rolling stock equipment ledger data mainly comes from equipment operation and maintenance management systems, monthly maintenance record documents, spare parts inventory ledgers, and factory quality inspection reports. Data update rhythms fall into three categories: real-time updates (synchronized after a single maintenance is completed), daily updates (spare parts inventory changes), and monthly updates (full equipment summary reports).
Document structure includes structured fields and unstructured attachments. Structured fields include equipment number, maintenance date, fault code, and replaced spare part model. Units include sets, operating hours, pieces, yuan, and others. Attachments are mostly dozens of pages of on-site maintenance photos and written descriptions.
What constraints these characteristics impose on the vector models and indexing link
Rolling stock equipment ledger data mixes structured fields and long document attachments, with diverse update rhythms. This requires indexes to support flexible switching between incremental synchronization and full reconstruction.
The length of individual documents varies widely, from short field entries to dozens of pages of maintenance reports. This demands adaptation to text chunking requirements for different lengths. Fields such as equipment numbers and fault codes have fixed professional semantics. Vector models must balance coding consistency between structured fields and unstructured text, to avoid semantic deviation of professional terms.
A large number of documents are bound to the same equipment ID. Indexes must support filtering and recall by equipment ID, to prevent ledger content from unrelated equipment from interfering with retrieval results.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of single sections of rolling stock maintenance reports, avoid splitting critical fault descriptions and processing steps |
| Overlap Length | 100–150 characters | Retain contextual connection between chunks, prevent cross-chunk semantic breakage in long documents, and ensure complete recognition of professional terms |
| Similarity Threshold | 0.72–0.85 | Adapt to the semantic similarity of professional terms in rolling stock equipment ledgers, filter low-relevance non-target equipment ledgers |
| Single Search Limit | Top 10 entries | Cover the recall requirements for multiple ledgers of a single equipment, avoid missing critical maintenance records and spare part replacement information |
| maxToken | 4000 token | Match the context window limit of the large language model in FastGPT v4.17.0, ensure that recalled content can be fully sent to the model |
| defaultToken | 2000 token | Serve as the default recall context length, adapt to the information requirements of conventional ledger aggregation, balance retrieval efficiency and content completeness |
Three common mistakes
- The number of recalled documents during retrieval does not match the set single search limit. The interface shows 10 recalled documents but only 4 are returned. The cause is that the real-time synchronization switch for incremental indexing is not enabled, so recently updated maintenance documents are not included in the current index pool.
- The large language model output does not reference the recalled ledger content, and instead prompts that no relevant information is available. Logs show that 5 maintenance documents were recalled, but the model reply does not mention the fault processing steps in the documents. The cause is that the
maxTokensetting for recalled content exceeds the model context window, and valid content is truncated and cannot be read correctly. - Index construction takes too long. Progress stalls for more than 600 seconds when importing ledger data in batches. The cause is that the chunk length is set too large, which increases the time required for single-block vector calculation, and the parallel index construction configuration is not enabled.
How to confirm the configuration is correct
- Upload a standard rolling stock equipment maintenance ledger, check the chunk preview interface, confirm that the chunk length and overlap length fall within the preset value range.
- Initiate a retrieval request for a specific equipment number, verify that the equipment numbers of the recalled documents match the retrieval conditions, and confirm that the similarity threshold is active, filtering out ledger content from unrelated equipment.
- Check the index construction logs, confirm that incremental update tasks run at the preset rhythm, with no failed error messages.
- Test the large language model's reply, confirm that the recalled ledger content is fully referenced, with no prompts of context truncation.
The values provided 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.