What the data for this category looks like
Core data for this category comes from several sources: formulation and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS laboratory management systems, and warehouse management systems. Daily generated documents include batch production instructions, material feeding review slips, reaction process records, cleaning validation records, inspection reports, deviation and change orders. Data updates follow a per-product-batch cycle. New records are generated for each critical reaction step. Document fields include batch number, feeding amount (unit: kilogram or liter), reaction temperature (degree Celsius), reaction duration (hour), cleaning inspection items, and some documents include screenshots of equipment operating parameters.
What constraints these characteristics impose on the vector model and indexing workflow
Multi-batch, multi-type document structures require vector indexes to support grouped retrieval by batch number and document type. This prevents records from different batches from being mixed. High-frequency incremental updates require indexes to support real-time or near-real-time document writing. This avoids delays caused by full index rebuilding. Documents contain numerical fields with units and process parameters. Vector models must retain these semantic associations. This ensures accurate matching of historical records with identical parameters during retrieval. Individual document lengths vary widely. Ranges run from short material feeding review slips to several pages of reaction process records. A flexible chunking strategy adapts to different text lengths.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the average paragraph length of single documents for this category, and avoids splitting critical process parameters |
| Overlap length | 100–150 characters | Retains contextual association between adjacent chunks, ensuring that continuous step information for feeding and reactions can be fully obtained during retrieval |
| Similarity threshold | 0.75–0.85 | Differentiates effectively related process records from irrelevant documents, and avoids mixing low-correlation batch data in retrieval results |
| Single search limit | Top 5 entries | Matches the number of associated documents that need to be checked during audit for this category, and avoids excessive results interfering with audit judgment |
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 | 1500 token | Adapts to the typical length of a single inspection report or cleaning validation record, ensuring complete semantics during single-document retrieval |
Three common configuration mistakes
- Phenomenon: The interface prompts "vector model connection failed", or empty results are returned during retrieval. Cause: The API key and access endpoint of the cloud vector model are not configured correctly, or the corresponding model is not enabled in FastGPT channel management.
- Phenomenon: Key numerical fields such as feeding amount and reaction temperature are lost in chunked text. Cause: The chunk length is set too small, splitting complete statements containing units and parameters into multiple independent chunks and destroying semantic associations.
- Phenomenon: Retrieval results mix process records from multiple batches, and cannot accurately match the target batch. Cause: Index grouping by batch number is not configured, causing documents from all batches to be retrieved mixed together.
How to confirm the configuration is correct
- Upload a typical material feeding review slip and reaction process record, and check whether the chunking preview interface fully retains key fields such as batch number and feeding amount.
- Enter a search term containing a specific batch number and process parameters, and check whether the returned results only include associated documents of the corresponding batch, with no records from irrelevant batches.
- Test using different vector models, retrieve cleaning validation records with equipment screenshots separately, and confirm that multimodal models can correctly associate image annotation content with text information.
- View the vector index monitoring panel of FastGPT v4.17.0, and confirm that incremental update tasks are running normally with no error logs.
The values provided are common starting points and should be measured against the reader's own samples.