What the data for this category looks like
Core data for active pharmaceutical ingredient and pharmaceutical intermediate manufacturing comes from synthesis, fermentation, purification, quality and registration support departments. This data includes batch production records, material balance sheets, reaction and purification records, cleaning records, impurity profile reports, deviation and change documents, and more. Data sources cover registered process documents, raw material batch ledgers, DCS systems, weighing systems, LIMS, chromatography data systems, and QMS. Update frequency is once per production batch or each process step. Documents are mostly long-form professional text, with dedicated fields such as reaction parameters, impurity content, solvent residue levels, and batch numbers. The core focus is multi-step chemical routes and impurity profile control, with significant differences from dosage form manufacturing categories.
What constraints do these characteristics impose on vector models and indexing
Multi-source heterogeneous data sources require indexing systems to support importing different text formats, and avoid losing formatting for dedicated professional fields. High-frequency batch updates require flexible switching between incremental indexing and full index rebuilding, to reduce resource consumption from repeated calculations. Long-form professional documents need reasonable chunking logic, to avoid splitting complete contexts of reaction steps or impurity analyses. Semantic matching for dedicated fields and professional terminology requires vector models adapted to pharmaceutical and chemical domain vocabulary, to improve retrieval accuracy. The severe consequences of errors also require index recall results to cover sufficient relevant information, avoiding omission of key process parameters or deviation records.
How to set configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the length of single-paragraph professional text in batch production records, avoid logical breaks across process steps |
| Overlap Length | 150–200 characters | Retain contextual connections between reaction steps, ensure complete process chains can be retrieved during searches |
maxToken | 8192 token | Adapt to the total length of multi-step reaction records, comply with input limits of mainstream embedding models |
defaultToken | Calibrated based on actual measurements | Adapt to token counting rules of different embedding models, avoid abnormal text truncation |
| Single Search Limit | Top 8 entries | Cover relevant information for multi-step processes, meet retrieval needs for process changes and impurity tracking |
| Similarity Threshold | 0.75–0.85 | Match semantic accuracy of professional terminology in the pharmaceutical and chemical field, filter low-relevance retrieval results |
Three Common Errors
- Phenomenon: Chunk previews show reaction steps are split, and complete feeding parameters and reaction conditions cannot be retrieved. Cause: Reasonable overlap length was not set, causing contextual information from adjacent paragraphs to be disconnected. The maximum paragraph depth was also incorrectly set to 3, which does not cover the hierarchical structure of long reaction flows.
- Phenomenon: An Invalid error is returned when accessing a multimodal embedding model, and index creation cannot be completed. Cause: The multimodal input adaptation switch was not enabled in FastGPT v4.17.0, or input files were not converted to a format supported by the model.
- Phenomenon: After performing a vector database migration, impurity profile reports from old batches cannot be matched by the new retrieval logic. Cause: Original batch-associated metadata was not retained during migration, leading to loss of semantic context for dedicated fields and failure to form associations with new process parameters.
How to Confirm Proper Configuration
- Upload a complete batch production record document, view the chunk preview interface, and confirm that reaction steps and impurity analysis content are not unreasonably split.
- Enter a process-related query, verify that the number of returned results matches the single search limit setting, and that the semantic matching of retrieval results meets expectations.
- Perform a vector database migration operation, confirm that old data after migration can be normally retrieved by the current vector model, with no format or content loss.
- Trigger a full index rebuilding task, check that the task progress log has no errors, and that all documents have completed index updates.
The values provided are common starting points and should be measured against the reader's own samples.