What Data Looks Like for This Category
Inspection and process record audit data for batch fine chemical synthesis comes from the formula and process library, raw material batches, reactor DCS, weighing system, LIMS, and warehouse management system. Updates occur per batch and each critical step. Daily processed documents include batch production instructions, material reconciliation sheets, reaction process records, cleaning validation records, test reports, and deviation and change orders. Data fields include feed quantity (kilograms/liters), reaction temperature (degrees Celsius), reaction duration (hours), batch number, and others. Some formula fields have confidentiality requirements. Cleaning records must include quantitative residue test data.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Dialogue must be bound to a single batch’s unique identifier to handle batch-updated data, avoiding cross-batch data confusion. Prompts must clearly specify extraction formats and units to align with multiple types of specialized documents and fields with units, preventing audit deviations. Production processes that switch between multiple products require strict filtering of non-current batch information in the context, while limiting the output scope of sensitive formula information. Teams integrate fields from different modules during dialogue when working with data from multiple systems; prompts must clearly correlate corresponding data items from each system to ensure the integrity of audit logic.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to a single batch’s unique number | Ensure independent context for each batch audit, avoid cross-batch data confusion |
variables | Pass batch number, raw material batch number | Correlate single-batch data from multiple systems, accurately match corresponding documents |
| Timeout duration | 300–600 seconds | Adapt to the full duration of a single batch audit process, prevent mid-process interruptions |
| Context window | First 1000–1500 characters | Match the data volume of critical steps for a single batch, filter redundant information |
| File link variable | Associate corresponding document paths per batch | Adapt to the batch-updated document structure, quickly retrieve single-batch production records |
| Maximum dialogue turns | 8–12 turns | Cover the full audit workflow of material reconciliation, reaction monitoring, and cleaning validation |
Three Common Misconfigurations
- Issue: After deploying via image packaging, uploaded attachments cannot be recognized by the system, with no error prompt. Cause: No file storage mount path was configured during deployment, so uploaded attachments cannot be read by the system.
- Issue: After calling the file link variable in dialogue, the audit content of the corresponding document is not returned. Cause: No batch-related parameters were passed via
variables, so the document path for the corresponding batch cannot be matched. - Issue: In advanced orchestration, the AI dialogue after the judge node cannot obtain the initial user’s audit request content. Cause: The initial user question was not passed to the AI dialogue node via
messagesorvariables, resulting in missing original input context.
How to Verify Proper Configuration
- Initiate a single-batch audit dialogue, check that
chatIdis bound to the correct batch number, and that the context only includes relevant documents and data for that batch. - Upload the material reconciliation sheet for the corresponding batch, call it via the file link variable, and confirm that correct content for specialized fields such as feed quantity, reaction temperature, etc., can be extracted.
- Configure a judge node in advanced orchestration, after triggering the AI dialogue, check that the initial user’s audit request content can be obtained.
- Test consecutive multi-turn dialogue, confirm that the order of
messagesis correct, and that irrelevant data from other batches is not mixed into the context.
The values provided here are common starting points and should be measured against the reader’s own samples.