What the data for this category looks like
The main entities in this category are enterprises producing coatings, resins, additives, agrochemical intermediates, and specialty chemicals. Daily documents include batch production instructions, feeding verification sheets, reaction process records, cleaning records, test reports, deviation change forms, and similar materials. Data is sourced from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS systems, and warehouse management systems. Updates are triggered per batch production or each key process step. Documents contain fields such as process parameters, batch information, and operation records. Units include industrial standard units like temperature, pressure, and feeding quantity. Each batch of data is linked to the complete process chain.
What constraints these characteristics impose on the vector models and indexes link
Data for this category comes from dispersed sources and has mixed formats, including structured process parameters and unstructured operation records. This requires support for multi-source data access. High-frequency updates triggered per batch require indexes to support incremental synchronization, avoiding excessively long full reconstruction times. In scenarios where production switches between multiple product varieties, vector data must be isolated by batch or product type to prevent cross-batch contamination. The characteristic that documents are linked to the full process chain requires chunking logic to retain the contextual integrity of process steps, avoiding splitting critical parameter combinations. Additionally, due to formula confidentiality requirements, vector storage must meet private deployment standards, and cross-domain data transmission is prohibited. This page is compatible with FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the complete context of a single batch’s process steps, avoiding splitting critical reaction parameter combinations |
| Overlap Length | 100–150 characters | Retains associated information between adjacent process steps, preventing chunking from breaking reaction logic chains |
| Similarity Threshold | 0.75–0.85 | Matches the precision requirements of fine chemical process parameters, filtering historical data from unrelated batches |
| Single Search Limit | Top 10 results | Controls retrieval scope, adapting to the need for precise positioning during multi-batch switching |
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. |
defaultToken | 150 token | Matches the typical length of a single process record, optimizing vector generation efficiency for small documents |
Three common configuration mistakes
- Retrieval results return cross-batch process data, leading to incorrect judgments about cleaning residuals. The cause is failure to isolate vector indexes by batch tags, with process vectors from different products stored mixed together.
- Context truncation errors occur during document parsing, making it impossible to fully load reaction process records. The cause is failure to adjust the
maxTokenconfiguration based on document length, exceeding the preset threshold. - Incremental synchronization delay is too high to match the update rhythm of each batch production. The cause is failure to enable incremental index mode, with full vector reconstruction executed for each synchronization.
How to confirm the configuration is correct
- Upload the complete process documents for a single batch, and verify that vector chunking results retain continuous process step descriptions with no critical parameters split.
- Trigger an incremental synchronization task, and confirm that only documents from newly added batches have generated corresponding vectors, with no full reconstruction triggered.
- Input a process verification query, and confirm that retrieval results only return documents associated with the current batch, with no cross-batch data included.
- Test batch upload of multi-format documents, and confirm that all documents have completed vector generation and index binding with no abnormal errors.
The values listed above are common starting points and should be validated against your own specific samples.