What This Category's Data Entails
This category’s data sources include cell banks and seed lots, process analytical systems, clean zone monitoring data, MES, LIMS, and equipment integrity test records. Update frequencies cover each production batch cycle, real-time monitoring nodes, and fixed monitoring plans. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring reports, media fill simulation results, sterility test reports, and batch release documentation. Fields include batch number, culture temperature, clean zone settleable microbial count, equipment runtime, and similar items, with units mostly ℃, cfu/㎡, hours, and other standard units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data for this category is updated by batch, in real time, or per fixed schedule. Multi-turn dialogue must retain context information for the corresponding production batch to avoid mixing audit data across production batches. Daily documents contain multi-dimensional process and test records. Prompts must explicitly limit retrieval scope to documents associated with the current audit batch to prevent irrelevant data from interfering with audit logic. Fields include specialized units. Multi-turn dialogue must automatically identify and match field units to avoid retrieval errors caused by unit mismatches. Aseptic manufacturing audits involve high-risk scenarios. Multi-turn dialogue must fix the context window size to prevent critical historical audit records from being pushed out by new dialogue content. Additionally, multi-turn dialogue must maintain the order of the messages array to ensure logical coherence of historical conversations and avoid deviations in audit judgments.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Maximum context window | 8000–12000 token | Adapts to the total character size of single-batch audit documents for this category, preventing critical historical dialogue content from being truncated |
| Associated knowledge base limit | Top 6 | Matches the number of core document types required for this category's audits, preventing retrieval resource overload |
| Single input character limit | 2000–3000 characters | Adapts to the typical input length of single-batch audit questions, preventing input over-limit errors |
chatId binding rule | Bind by production batch | Ensures multi-turn dialogue for the same batch audit shares a unified context, preventing cross-batch data mixing |
variables preset parameters | Fill in current batch number, production workshop number | Provides fixed retrieval basis for multi-turn dialogue, simplifying repeated input for subsequent questions |
messages array maintenance | Retain historical content in conversation order | Ensures logical coherence of multi-turn dialogue, avoiding disordered historical conversation sequence |
Three Common Misconfigurations
- An error occurs where the model automatically switches to a non-target model in the debug preview interface. The cause is that the binding rules for LLM calls are not correctly configured, leading the system to call the preset public model by default.
- Audit documents fail to load after the associated knowledge base count exceeds the limit. The cause is that the associated knowledge base limit configuration is not adjusted, exceeding the preset retrieval quantity threshold.
- A character over-limit error is triggered when inputting JSON-formatted audit document fragments. The cause is that the single dialogue input limit value is not adjusted, failing to adapt to long-fragment audit data input.
How to Confirm Proper Configuration
- Initiate consecutive multi-turn questions for the same batch, verify that the dialogue interface retains the previous batch number and audit context.
- Adjust the associated knowledge base count, then test loading all core documents for the corresponding batch to confirm that retrieval results cover all required document types.
- Input long JSON-formatted audit data fragments, verify that the system does not trigger input over-limit error prompts.
- Check the FastGPT v4.17.0 LLM call logs to confirm that the preset local model is called for each dialogue, with no automatic switching occurring.
Additional Note
The configuration values listed above are common starting points. They should be evaluated against specific samples and operational scenarios of the target use case.