What the data for this category looks like
Ledger data for urban gas comes primarily from SCADA monitoring systems, frontline patrol mobile terminals, and internal enterprise ledger management systems. Patrol records are updated daily. Monthly district summary ledgers are generated each calendar month. Annual special reports are submitted in batches quarterly. Individual structured ledger records include fields such as patrol point number, equipment code, gas pressure value, patrol time, and patrol staff number. Pressure values use kilopascals (kPa) as the unit, and the time format is YYYY-MM-DD HH:mm:ss. Some patrol records include on-site equipment photos, which are unstructured multimodal data bound one-to-one with their corresponding structured text.
What constraints these characteristics impose on the vector models and indexing link
The large number of structured fields with clear units requires vector models to retain field metadata during encoding, to avoid confusing similar text from different points. The coexistence of multimodal data and structured text requires index systems to support mixed vector storage and retrieval. There are wide variations in data update rhythms: real-time patrol data requires incremental indexing, monthly summary ledgers require full index rebuilds, and index sharding should be divided by district to avoid excessive load on single indexes. There are many long-text ledger records, so adaptation to the model's maximum input length limit is needed to prevent loss of key associated information during chunking.
How to set the configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | A single gas ledger contains multiple associated fields; overly long chunks will break the correspondence between points and equipment, while overly short chunks will lose context information |
| Overlap Length | 100–200 characters | Retain the point and equipment association information of adjacent patrol records, avoiding loss of critical data across chunks |
| Similarity Threshold | 0.75–0.85 | Gas ledgers have high requirements for data accuracy; a threshold that is too low will introduce irrelevant records, while a threshold that is too high will fail to retrieve valid data |
| Reranking | Enabled | The semantic similarity of patrol records is easily disturbed by point names; reranking can correct sorting deviations from initial retrieval |
maxToken | 4000 token | Adapt to the long-text generation requirements of gas ledger summary reports, avoiding context truncation |
defaultToken | Calibrated based on actual measurements | Adapt to the average text length of ledgers in different districts, preventing model call timeouts |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common mistakes
- Phenomenon: An error is returned when calling a multimodal vector model, with a prompt indicating that the current input type is not supported. Cause: The multimodal embedding model was not configured separately, and the call chain of the text embedding model was used instead, causing the model to fail to recognize image inputs.
- Phenomenon: The number of index retrieval results is far lower than expected, and some patrol records are not indexed. Cause: The chunk length was set beyond the maximum input limit of the model, causing long ledger records to be automatically truncated and discarded.
- Phenomenon: Field confusion appears in the generated summary report, with the pressure value of point A assigned to point B. Cause: Field metadata was not retained for vector encoding, causing similar text from different points to be incorrectly associated.
How to confirm the configuration is correct
- Upload a single complete gas patrol ledger record, check whether the index generation status shows success, with no truncation or format errors.
- Enter a specific patrol point query term, verify that all relevant records for that point are included in the retrieval results, and that the sorting meets expectations.
- Try uploading a photo of a gas equipment site, confirm that the corresponding multimodal vector data is generated in the index library, with no format error prompts.
- View the knowledge base configuration panel, confirm that the settings for
maxTokenanddefaultTokenmatch the supported parameters of the currently used embedding model.
The values given 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.