Data Profile for This Category
The data for this category comes from resin batches and property sheets, injection molding machine parameters, mold temperature machines, MES systems, and dimensional and appearance inspection equipment. Daily generated documents include injection molding process cards, raw material drying records, mold history logs, first-piece inspection reports, patrol inspection forms, and material change and machine cleaning records. Data is updated per production batch, each mold change or material swap, and once per shift. Data fields include resin batch number, injection pressure, mold temperature, production cycle, raw material moisture content, and accompanying testing units and process parameter thresholds.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
This category’s data has a high update frequency and changes dynamically based on production batches, mold adjustments, material swaps, and work shifts. Multi-turn dialogue must bind to the current production batch context to prevent mixing data across batches. There are multiple types of daily documents, so prompts must clearly distinguish field rules for different document types such as injection molding process cards and raw material drying records to ensure accurate parameter extraction. Data sources are scattered, covering both process parameters and inspection results. Multi-turn dialogue must guide the model to integrate multi-source data, and exclusive prompt rules must be set for sensitive parameters such as material moisture content and mold temperature to avoid summary errors caused by parameter deviations. Context length must also be limited to prevent redundant old data from interfering with current batch ledger summaries.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current production batch ID | Distinguish ledger data across different batches to avoid cross-batch context confusion |
variables | Bind resin batch number, injection pressure MPa, mold temperature ℃ | Inject dynamic production parameters into the dialogue context to ensure summarized data matches the current production stage |
messages | Retain the most recent 3 production-related dialogues | Control context length to avoid redundant old data interfering with current ledger summaries |
| Single recall limit | Top 6 matching documents | Balance data completeness and response speed, adapt to scattered multi-source ledger data |
| Document chunk length | 800–1200 characters | Adapt to the field density of documents such as injection molding process cards and patrol inspection forms, avoid parameter truncation |
| Question optimization | Calibrate based on actual testing | Adapt to the dialogue logic of multi-step processes in this category, optimize prompt guidance accuracy |
Common Misconfigurations
- Phenomenon: All document content can be viewed in the knowledge base page, but the model prompts that no available knowledge base data is available during dialogue. Cause: The production batch parameter in
variablesis not bound, so the model cannot match the ledger documents corresponding to the current batch. - Phenomenon: After uploading an XLSX-format injection molding patrol inspection form, the dialogue cannot extract table field content. Cause: Chunking rules adapted to multi-source documents are not set, resulting in truncated or unrecognizable table data.
- Phenomenon: Data across batches is called during multi-turn dialogue, leading to mixed materials or incorrect process parameters. Cause:
chatIdis not used to bind the current production batch, and the context does not isolate ledger data from different batches.
How to Verify Proper Configuration
- Initiate a dialogue related to the current production batch, check whether the model can automatically associate the ledger documents of the corresponding batch.
- Upload different types of ledger documents, verify whether the dialogue can accurately extract exclusive fields for each document.
- Initiate comparative queries for different production batches, check whether dialogue data from different batches is isolated from each other.
- View the dialogue context log to confirm that the dialogue context does not include irrelevant historical batch data.
The values provided here are common starting points and should be measured against the reader's own samples.