What Data for This Category Looks Like
Data for this category originates from synthesis, fermentation, purification, quality control, and regulatory support departments at active pharmaceutical ingredient manufacturers. Daily documents include batch production records, material balance sheets, reaction and purification records, cleaning records, impurity profile reports, and deviation and change documents. Data sources cover regulatory process documents, raw material batch ledgers, DCS systems, weighing systems, LIMS, chromatography data systems, and QMS. Updates occur once per production batch and after each process step is completed. Fields include batch number, reaction temperature, impurity content, solvent residue level, and similar metrics, with corresponding units such as ℃, ppm, and %.
Constraints on Multi-turn Dialogue and Prompt Engineering
The batch-based, multi-step chemical route data characteristics of this category require multi-turn dialogue to bind the variables parameter for the current production batch, to avoid mixing cross-batch data. Impurity traceability for multi-step reactions requires maintaining dialogue context via the messages parameter, to gradually break down questions about reaction links, material sources, and other topics. Data sources are scattered and have significant format differences, so prompt engineering must explicitly specify priority retrieval of structured test data from LIMS and chromatography data systems. Frequent batch updates require the dialogue link to automatically associate the latest process step records, while also controlling context length to prevent irrelevant historical data from disrupting analysis.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single-step process records, avoids splitting complete process data across steps |
| Similarity Threshold | 0.75–0.85 | Filters irrelevant non-batch documents, focuses on core business data such as impurity profiles and batch records |
| Single Search Limit | Top 6 entries | Covers core records and test reports for a single batch, avoids result overload that reduces analysis efficiency |
variables | Bind current batch number, production step | Limits retrieval scope to all associated documents for the current batch, prevents interference from cross-batch data |
messages | Retain the most recent 3 rounds of dialogue context | Maintains logical consistency for multi-turn traceability, avoids model misunderstanding caused by redundant context |
| Dialogue Context Window | 4000 token | Adapts to the total length of core data for a single batch, complies with input limits of general large language models |
Three Common Misconfigurations
- Symptom: No knowledge base retrieval results are returned during dialogue, only generic responses are output. Cause: The prompt does not explicitly specify using documents associated with the
variablesparameter bound to the current batch, resulting in an unrestricted retrieval scope. - Symptom: Parallel multi-task questioning and result merging cannot be implemented. Cause: The
messagesparameter is not used to pass intermediate results of multi-turn questions in sequence, or no variables are configured to receive the output of a single execution. - Symptom: Program execution results cannot be connected to the dialogue module. Cause: The program output is not mapped to the
variablesparameter of the dialogue, resulting in background knowledge not being correctly loaded into the dialogue context.
How to Verify Correct Configuration
- Manually enter the batch number of the current batch as a query term, verify that retrieval results only include associated documents such as batch records and impurity profile reports for that batch.
- Initiate multi-turn follow-up questions, such as first asking "What are the main impurities for this batch?" then asking "What reaction step corresponds to this impurity?", verify that the dialogue context retains the previous batch information.
- Configure a program execution module and output test results, verify that the results can be automatically bound to the
variablesparameter of the dialogue and participate in subsequent analysis. - View the dialogue log, verify that the
messagesparameter only retains the most recent 3 rounds of context and does not load irrelevant historical batch data.
The values provided are common starting points and should be measured against the reader's own samples.