What the data for this category looks like
Data for this category comes from registered process documents, raw material batch ledgers, DCS production control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Data is updated per production batch and per process step. Core documents include batch production records, material balance sheets, reaction and purification records, cleaning validation records, impurity profile reports, and deviation and change documents. Data fields include batch numbers, reaction temperatures, feeding quantities, yield values, impurity contents, solvent residue levels, and more. It links real-time and historical records across multiple systems.
What constraints these characteristics impose on multi-turn dialogues and prompt words
Data for this category is linked by batch and process step. Multi-turn conversations must bind chatId to lock the context of the same batch review, to avoid mixing data across batches. Data sources are scattered and updated frequently. Prompt words must clearly specify the document type and batch range for review, to prevent calling irrelevant historical data. Field formats vary significantly across different document types. Prompt words must guide conversations to focus on core fields such as batch numbers and reaction parameters, to avoid vague questions. In addition, review results are tied to compliance risks. Multi-turn conversations must support step-by-step inquiries into data sources and testing basis, to ensure conclusions are traceable.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique number of the current production batch | Ensure multi-turn conversations lock all associated documents for the same batch, avoid mixing data across batches |
messages | Append content in conversation order, retain the first 20 rounds of history | Cover the full review process for a single batch from feeding to finished product, prevent loss of critical context |
variables | Preset current batch number and review document type variables | Quickly limit retrieval scope, reduce interference from irrelevant historical data |
| Maximum context length | 800–1200 characters | Match the total character count of core review documents for a single batch, avoid exceeding model processing limits |
| Question optimization switch | Enabled | Automatically break down complex compliance review questions into multiple small questions, adapt to the review logic for multi-step processes |
| Maximum matched documents per request | Top 5 | Focus on core documents, avoid interference from redundant information on review conclusions |
| Conversation timeout | 600 seconds | Match the retrieval and review duration for active pharmaceutical ingredient production documents, avoid interrupting conversations before review is complete |
This configuration logic is based on FastGPT v4.17.0.
Three common mistakes
- Conversations fail to trigger preset external data retrieval, and directly return built-in model results. The symptom is that review results do not associate LIMS test data or DCS production records corresponding to the batch. The cause is that the current batch number is not configured in
variables, so the prompt word does not correctly trigger the retrieval logic linked to external systems. - CORS block errors occur when calling the conversation interface from the front end. The symptom is that the browser console returns cross-domain related status code prompts. The cause is that the privately deployed service has not configured cross-domain rules to allow access from front-end domain names.
- Conversation context exceeds processing limits, leading to truncation of critical data. The symptom is that review conclusions lack validation content for some process steps. The cause is that a reasonable maximum context length is not set, causing earlier conversation content to be automatically cleared.
How to confirm the configuration is correct
- Enter the current production batch number, check if the conversation automatically links the preset document types and data scope for that batch.
- Launch multiple consecutive conversations, check if conversation records are appended in order, with no context loss or cross-batch binding issues.
- View interface return logs, confirm that the
chatIdparameter matches the current batch number, with no parameter binding errors. - Launch a conversation containing complex compliance questions, check if it is automatically broken down into multiple guided questions, gradually focusing on core review content.
The values provided are common starting points and should be measured against the reader's own samples.