What the data for this category looks like
Typical entities in this category are enterprises engaged in coatings, resins, additives, agrochemical intermediates and specialty chemicals. Daily generated documents include batch production instructions, feeding verification sheets, reaction process records, cleaning records, inspection reports, deviation and change orders. Data sources cover formula and process libraries, raw material batch information, reactor DCS systems, weighing systems, LIMS systems, and warehouse systems. Data update frequency is once per batch production or each key process step. Fields include feeding amount, reaction temperature, batch number, cleaning verification results, and others. Units are mostly kilograms, liters, and degrees Celsius.
Constraints Imposed on Vector Models and Indexes
Because this category requires production switching by batch and data updates per batch or key step, vector indexes must support incremental updates to accommodate high-frequency, small-batch data writes. Daily documents are mostly short texts for single steps or single batches, and contain professional process terms and clearly specified parameter units. Vector models must adapt to industrial process-specific semantics, and the defaultToken and maxToken parameters must be set appropriately. Additionally, retrieval must associate fields such as batch number and process step, so indexes must support multi-dimensional filtering to avoid interference from results across batches or incorrect process steps. Some documents involve confidential formula content, so indexes must strictly control access permissions and only allow authorized access.
How to Set the Configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single-batch process documents for this category, avoiding splitting critical process parameters |
| Overlap Length | 100–150 characters | Retains associated semantics of adjacent process steps, preventing chunking from breaking coherent logic such as feeding sequence |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-process results, adapting to semantic matching accuracy for professional terms |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Single Search Limit | Top 5 results | Controls the number of returned results, avoiding redundant information interfering with process judgment |
This configuration is based on the feature design of FastGPT v4.17.0.
Three Common Mistakes
- A "No available index model detected" error pops up after refreshing the page, while the agent test previously returned normal responses. The current index is not bound to the corresponding Q&A agent, or the index's vector model configuration is not synchronized to the production environment.
- When calling knowledge base retrieval via API, the returned result chunks do not match the interface settings. The chunk configuration parameters are not correctly passed in the API request, or the parameter naming does not meet FastGPT's interface requirements.
- After upgrading the FastGPT version, existing vector library data cannot be recognized by the new index, and retrieval returns empty results. Vector library format conversion is not performed, as there are differences in vector embedding dimensions or storage structures between the old and new versions.
How to Confirm the Configuration is Correct
- View the vector model configuration items on the index management page, confirm that the model adapted to industrial process semantics has been selected, and that the configuration matches the current Q&A scenario.
- Initiate a simulated process query, and verify that the returned result chunk length and overlap length match the preset configuration.
- Check the API call logs to confirm that request parameters have been correctly passed, with no missing parameters or format errors.
- Verify the incremental update function: upload a new process document, and confirm that the index can automatically synchronize the new data and return corresponding retrieval results.
The values provided are common starting points and should be measured against the reader's own samples.