Data Profile for This Category
The core business departments of this category include synthesis, fermentation, purification, quality control, and registration support modules. Daily generated documents include batch production records, material balance sheets, reaction and purification records, cleaning validation records, impurity profile reports, deviation and change documents. Data sources cover registered process documents, raw material batch ledgers, DCS systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Update frequency is once per production batch and per process step. Document fields include professional content such as batch numbers, reaction parameters, impurity content, and solvent residue levels. Formats include structured tables and unstructured text.
Constraints for Vector Models and Indexes
This category’s data is multi-source, heterogeneous, and updated frequently. Each production batch generates new documents and data. The system must support batch incremental indexing. Documents include long-text reaction records and highly specialized impurity profile reports. This places high requirements on text splitting coherence. Most fields are professional parameters closely tied to quality. Vector indexes must accurately match key identifiers such as batch numbers and impurity content. This prevents irrelevant results from generalized retrieval. Data from multiple systems has inconsistent formats. Unified field mapping and format verification must be completed before indexing.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to long-text reaction records and impurity profile reports in this category, ensuring splitting coherence for professional content |
| Overlap Length | 100–150 characters | Preserves contextual continuity for multi-step reaction records, preventing loss of process association logic after splitting |
maxToken | 8192 token | Adapts to long document parsing dense with technical terms, meeting vector encoding requirements for long texts such as impurity profile reports |
| Similarity Threshold | 0.75–0.85 | Accurately matches key quality fields such as batch numbers and impurity content, filtering low-relevance retrieval results |
| Single Search Limit | Top 10 results | Controls the number of returned results across multi-source data, ensuring retrieval efficiency for quality traceability |
| Reranking | Calibrated based on actual testing | Adjusts ranking rules based on batch association and process step priority for this category, aligning with business traceability logic |
Three Common Misconfigurations
- Phenomenon: Retrieval speed slows after creating an index via the knowledge base interface, and the number of returned results exceeds expectations. Cause: Configuration settings for chunking and search limits are not adjusted for this category’s multi-source heterogeneous data, leading to excessive index shards and redundant returned data.
- Phenomenon: After replacing the embedding model, the existing imported file set cannot regenerate indexes quickly, requiring full re-import of all files. Cause: The batch reindexing function is not used, and only the single-file import process is applied, which does not match the batch update business requirement per production batch for this category.
- Phenomenon: Retrieval result ranking does not align with business priorities, and it is impossible to accurately associate excessive impurity levels with corresponding production steps. Cause: Ranking logic is not adjusted based on professional fields for this category such as batch numbers and reactor numbers, relying only on general similarity calculations.
How to Confirm Proper Configuration
- View the index generation logs in FastGPT v4.17.0, confirm that the split text length falls within the configured chunk length range.
- Call the knowledge base interface to perform a test retrieval, verify that the number of returned results does not exceed the configured single search limit.
- Compare retrieval results before and after replacing the embedding model, confirm that batch reindexing operations have been completed, and no file re-import is required.
- Initiate a retrieval for a simulated excessive impurity scenario, verify that results first associate with reaction and purification records for the corresponding batch, aligning with business traceability logic.
The values provided are common starting points and should be measured against the reader's own samples.