What the data for this category looks like
Coal chemical industry service inquiry and response data mainly comes from internal service guidelines, approval process specifications, equipment operation and maintenance manuals, and on-site operation records. There are two update rhythms for the data. Fixed service documents are updated quarterly. On-site operation and real-time approval data are updated daily. Each single document structure includes four types of fields: process steps, application material list, compliance requirements, and parameter indicators. Parameter indicators often carry industrial units such as MPa, ℃, and tons. Some documents include multi-page nested process descriptions.
What constraints these characteristics impose on the vector model and indexing link
Coal chemical industry service data contains a large number of parameter indicators with industrial units and nested process structures. Basic chunking can cause unit splitting and process logic breakage. This requires chunking to preserve field integrity. Real-time updated operation data requires indexes to support incremental synchronization, avoiding performance loss caused by full reconstruction. Multi-field documents need to distinguish core inquiry fields from auxiliary fields, preventing irrelevant data from interfering with vector recall accuracy. It also requires adaptation to different lengths of inquiry questions and document content.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Basis for This Value |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the nested process and parameter field lengths of coal chemical industry documents, avoiding logical breakage after splitting |
| Overlap Length | 100–150 characters | Retains cross-chunk parameter units and process connection information, preventing semantic breaks |
| Similarity Threshold | 0.75–0.85 | Filters false recalls from industrial parameter matching, meeting the semantic relevance requirement for business inquiries |
| Single Search Limit | Top 8 entries | Covers multi-step associated information of coal chemical industry service processes, avoiding omission of key approval nodes |
maxToken | 4096 token | Adapts to the vector encoding length of long documents, meeting complete semantic extraction for nested process documents |
defaultToken | 1024 token | Used for vector matching of short inquiry questions, balancing encoding efficiency and semantic integrity |
Three common mistakes
- Phenomenon: The interface displays an error message similar to "undefined model must match ^(text", and vector embedding cannot be completed. Cause: The API service for the selected vector model is not connected, or a third-party vector model that has not been configured is selected.
- Phenomenon: The semantic retrieval score is high, but the returned results do not match the correct service process. Cause: The chunk length is too short, splitting the logical connection of nested processes, leading to deviation in semantic recall.
- Phenomenon: A single piece of business data generates multiple vector entries, and cannot correctly associate with the original document. Cause: A reasonable overlap length is not set, or chunk parameters do not match the document field structure, leading to repeated data splitting.
How to confirm the configuration is correct
- Upload a single coal chemical industry document containing equipment parameters and nested processes, check if the chunk preview retains complete parameter units and process connection steps.
- Initiate a service inquiry containing specific equipment parameters, check if the similarity score of the recall results is within the set threshold range.
- Upload an on-site operation record updated on the same day, wait 10 minutes then initiate the relevant inquiry, confirm that the index has completed synchronous update.
- Test vector recall for multi-field documents, confirm that the recall priority of core service process fields is higher than that of auxiliary parameter fields.
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.