What This Category of Data Looks Like
Data sources for internal office and employee services in coal mine production include pre-shift meeting minutes, equipment inspection logs, team shift handover records, safety regulation documents, employee attendance ledgers, and others. Update frequencies vary significantly by document type. Pre-shift meeting minutes and inspection logs update per team shift. Attendance ledgers update daily. Safety regulations are revised annually. Document structure includes fields such as team number, equipment code, inspection time, problem description, and processing result. Some documents use structured table formats, while others are plain text paragraphs. Field units include six-digit codes, standard time formats, and character-based descriptive text.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Multi-source heterogeneous data sources require indexes to support mixed embedding of structured and unstructured text, to avoid vector representation bias caused by differences in data formats. Differences in update frequencies require configuring incremental indexing logic to avoid resource consumption from full index rebuilding. The wide span of document lengths requires a flexible chunking strategy to adapt to text lengths ranging from dozens of characters for attendance entries to thousands of characters for pre-shift meeting minutes. Fields contain a large number of professional terms such as equipment codes and safety terminology, requiring vector models to adapt to industrial domain semantics to ensure retrieval accuracy.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average text length of internal office documents for coal mines, matches the input token limit of mainstream vector models |
| Overlap Length | 100–200 characters | Prevents chunk truncation of key associated information, such as the binding between equipment numbers and corresponding inspection issues |
maxToken | 16384 | Adapts to the maximum input limit of mainstream API vector models, covering embedding needs after long document chunking |
defaultToken | 0 | Prevents default placeholders from interfering with the vector semantic expression of professional fields |
| Similarity Threshold | 0.72–0.85 | Matches the precise matching requirements of coal mine safety-related retrieval, filters low-correlation inspection records |
| Single Search Limit | Top 10 entries | Focuses on core team-level retrieval needs, avoids redundant results interfering with employee judgment |
Three Common Misconfigurations
- Symptom: A collection is shown as created successfully, but the index status on the page never changes to ready. Cause: The trigger condition for incremental synchronization is not configured, and the full index task times out due to the large volume of historical data.
- Symptom: Vector index results perform normally when run locally, but after deploying a packaged Docker image, the vector scores for the same text are completely consistent. Cause: The vector model API call key is not fixed, resulting in each request being assigned to a different model instance, which causes deviations in embedding results.
- Symptom: The index building process gets stuck on processing the last set of data. Cause: The number of index shards is set too high, and no shard retry mechanism is configured, resulting in the inability to automatically recover after the last batch of data submission fails.
How to Confirm the Configuration Is Correct
- Upload a typical equipment inspection log, check whether the chunked text is correctly truncated, and whether the overlapping part contains key associated information.
- Initiate a retrieval containing the keyword "equipment fault", verify whether the similarity scores of the returned results fall within the preset threshold range.
- Trigger an incremental index task, check the index update log, confirm that only newly added documents from the current day are included in the index, and no full index rebuilding is triggered.
- View the vector retrieval time panel of FastGPT v4.17.0, confirm that single retrieval time meets business expectations.
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.