What the Data for This Category Looks Like
Health diagnostic reports originate from documents exported by industrial equipment inspection systems and offline testing terminals. They are generated when a single inspection task completes, or updated at fixed inspection cycles. Most documents are semi-structured, containing unique device identifiers, inspection timestamps, multi-dimensional detection parameters such as temperature, vibration amplitude, supply voltage, preset abnormal thresholds, manual analysis notes, and alarm level fields. In some scenarios, text annotations for on-site captured infrared thermal imaging images are included. All fields have clear physical units.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Health diagnostic reports include multi-dimensional structured detection parameters and unstructured analysis text, with all fields carrying clear physical units. This means indexes must support vectorization of both semantic text and numerical features with units. A single inspection-generated report has a short document length, but dense inspection cycles will produce batch documents. Indexes must adapt to small-batch, high-frequency write scenarios. Some reports include text annotations for infrared thermal imaging, requiring additional compatibility with text vectorization that includes scene descriptions. It is also necessary to prevent incorrect weighting of parameters with different units in the vector space, and ensure full retention of the semantic association between classification labels and corresponding detection data.
Configuration Settings
This document adapts to the vector index configuration logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single health diagnostic report documents, avoids semantic fragmentation from overly short chunks, and reduces excessive vector computation load from overly long chunks |
| Overlap Length | 100–200 characters | Retains semantic association between adjacent chunks, prevents key detection parameters in inspection records from being truncated during chunking |
| Similarity Threshold | 0.75–0.85 | Meets the precise recall requirements for alarm analysis, filters low-correlation historical diagnostic reports |
| Maximum Single Recalls | Top 10 entries | Controls the number of retrieval results, avoids redundant information interfering with subsequent alarm analysis |
maxToken | 4096 token | Adapts to the input length limit of mainstream embedding models, prevents text truncation that loses key analysis content |
defaultToken | Calibrated based on actual measurements | Adapts to the index write quota for batch inspection reports, prevents single requests from exceeding interface limits |
Three Common Mistakes
- Symptom: Index creation fails, and the log returns a connection timeout error. Cause: Network access permissions for the PG database are not correctly configured. When deployed via Docker, the database port is not mapped to the host machine, or no firewall whitelist is added.
- Symptom: Embedding model calls return an error stating the model does not support the provided format. Cause: The index model is not configured separately. A non-embedding model bound to the dialogue model is used by force, or the dedicated embedding model interface is not switched in the FastGPT backend.
- Symptom: Index progress for batch inspection reports stalls. Host machine resource usage is too high. Cause: The number of documents written in a single batch is not limited. An 8-core 16-gigabyte virtual machine without a GPU does not reserve enough memory to handle vector computation tasks.
How to Verify Proper Configuration
- Upload a single health diagnostic report, check if the vector generation progress bar completes normally with no error prompts.
- Initiate a retrieval request based on historical diagnostic reports, confirm that the number of returned results matches the configured maximum single recall limit.
- Check storage usage in the index backend, verify that the length of chunked documents falls within the preset chunk length range.
- Call the embedding model test interface, confirm that the returned vector dimensions match the configured embedding model dimensions.
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.