This configuration is based on FastGPT v4.17.0, and involves core parameters including chatId, messages, and variables.
What the data for this category looks like
Data sources include cell banks and seed lots, process analytics, cleanroom monitoring, MES, LIMS, and equipment integrity testing. Update frequency is per batch, real-time, and per monitoring plan. Daily documentation includes cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring, media fill simulation, sterility testing, and batch release materials. Fields include batch number, culture temperature, cleanroom settle plate count, equipment runtime, and others. Units include degrees Celsius, colony-forming units, hours, and additional relevant units.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data has strong batch correlation. Multi-turn dialogue must retain batch identifiers using the messages context to avoid mixing cross-batch process and monitoring data. Real-time cleanroom monitoring data must be quickly incorporated into the dialogue context. Prompts must clearly distinguish between historical data and current monitoring values. Many document fields include specialized units. Prompts must require the model to match corresponding fields and units to avoid parameter confusion. Batch release materials must link to multi-stage data. Multi-turn dialogue must support gradual retrieval of linked records to ensure coherent Q&A logic.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | Previous 20 turns of dialogue + latest 10000 characters | Matches the context length for multi-stage linked data, prevents loss of critical batch information |
| Single Search Limit | Top 8 results | Covers core records across culture, filling, testing and other stages, avoids interference from redundant data |
| Query Optimization | Enabled | Standardizes professional terms such as batch numbers and parameter units to improve retrieval accuracy |
| Reranking | Sorted by batch relevance | Prioritizes linked documents from the same batch, aligns with batch management logic for biologics manufacturing |
variables | Predefined batch number, cleanroom number | Quickly locates process specifications and monitoring data for the corresponding batch |
| Context Storage | Grouped by batch | Isolates dialogue data across different batches, prevents cross-batch contamination |
Three common configuration errors
- Issue: After configuring English prompts, the model only outputs Chinese content and does not follow the English output requirements specified in the prompt. Cause: The prompt does not explicitly bind language priority, and the higher proportion of Chinese content in the knowledge base causes the model to prioritize matching Chinese contexts.
- Issue: Parsing errors occur when uploading LIMS-exported documents such as media fill simulation records, and nested table fields cannot be extracted. Cause: No parsing rules for specialized documents are configured, so nested formats cannot be correctly identified.
- Issue: After calling the application via API, retrieval and invocation step records within the application cannot be viewed in the conversation details page. Cause: The conversation log storage configuration is not enabled, or logs are not synchronized to the corresponding storage path.
How to confirm the configuration is complete
- Initiate a multi-turn dialogue that includes a specific batch number, and verify whether the model can link records from the culture, filling and other stages of the corresponding batch.
- Upload a document with specialized units, and test whether fields and corresponding values can be correctly extracted after parsing.
- Initiate a test dialogue with an English prompt, and verify whether the model output language meets the preset requirements.
- Call the application interface, and check whether the conversation details page displays complete internal processing steps and data invocation records.
The values provided here are common starting points and should be evaluated against the reader's own samples.