The following configuration is based on FastGPT v4.17.0.
What the Data for This Category Looks Like
Cosmetics manufacturing quality traceability data sources include registered or archived formulations, raw material safety documents, weighing and emulsification equipment logs, ERP/MES systems, laboratory test reports, and packaging material version records. Data updates are triggered per production batch or each line change. Core documents include formulation process sheets, weighing and feeding records, emulsification or filling records, cleaning and sanitization records, semi-finished and finished product test reports, and label review documents. Data fields include formulation component proportions, restricted substance content, production batch number, emulsification temperature, homogenization duration, packaging material version number, and others. Some data includes physical units or version identifiers.
What Constraints These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
Cosmetics manufacturing quality data is strongly linked to individual batches. Each batch’s data is independent and updated frequently. This requires multi-turn dialogue to accurately bind context information for a single batch, avoiding confusion across batch data. Dispersed, multi-source document types require prompts to clearly define retrieval scopes, only calling feeding, test, and other records for the current batch. Fields restricted by regulations such as restricted substances and label claims require prompts to accurately match data fields, avoiding generalized retrieval. Additionally, each line change or formula change alters production status. Dialogue must track historical line change information to ensure analysis coherence.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the first 2 rounds of historical dialogue for the current batch, filter out context from unrelated batches | Cosmetics production batch data is independent, avoiding interference from cross-batch historical information with current analysis |
variables | Bind 3 core variables: batch number, raw material code, production equipment number | Each batch’s formulation and records are strongly linked to the batch, requiring accurate matching of corresponding data |
| Single search limit | Top 5–7 results | The core data volume for a single cosmetics production batch is limited; excessive results will dilute valid information |
| Maximum context length | 800–1200 token | Balance completeness of context information and model inference efficiency, adapting to the scale of single-batch data |
| Dialogue timeout | 600 seconds | Cosmetics manufacturing quality analysis requires retrieving multi-source dispersed data; sufficient timeout ensures complete acquisition of batch information |
chatId | Bind dialogue sessions for a single batch’s production process | Fully associate dialogue records across feeding, emulsification, testing, and other links, avoiding session confusion |
Three Common Mistakes
- Phenomenon: Multiple rounds of AI dialogue output results are displayed simultaneously after the workflow runs. Cause: No context filtering rule for
messagesis configured, and all historical dialogue outputs are retained. - Phenomenon: When connecting to Feishu or WeChat channels, dialogue results carry a large number of punctuation marks such as # and *. Cause: The channel’s format display rules are not adapted, causing format markers in the prompt to be returned directly.
- Phenomenon: When calling the dialogue interface, no knowledge base information identifier referenced in this dialogue is returned. Cause: The knowledge base association record configuration for dialogue logs is not enabled, and the retrieval scope parameter of
variablesis not bound.
How to Confirm the Configuration Is Correct
- Initiate a quality traceability dialogue for a single batch, check that the output only includes relevant data for the current batch, with no cross-batch historical information.
- Connect to Feishu or WeChat channels, send a test dialogue, and confirm that the output result has no redundant punctuation marks such as # and *.
- Call the dialogue interface, check that the returned result includes the knowledge base association identifier for this retrieval.
- Upload a knowledge base document with an image, initiate a relevant query, and confirm that the model can correctly return the image’s URL link.
The values provided here are common starting points and should be measured against the reader's own samples.