This page is written based on the configuration guidelines for FastGPT v4.17.0.
What the Data for This Category Looks Like
Data sources for this category include purchase orders, drawings and specifications, supplier quality certificates, inspection equipment, historical quality data, and supplier portals. Daily produced documents cover inspection specifications, sampling plans, incoming material reports, supplier audit reports, exception contact sheets, 8D reports, and concession approval forms. Data updates trigger per incoming material batch. Supplier overall evaluations are conducted monthly or quarterly. Special audits are carried out irregularly. Document fields vary by type, including batch number, sampling quantity, non-conformity item type, supplier code, and other relevant content.
Constraints for Multi-turn Dialogue and Prompt Engineering from This Category
Scattered data sources and high update frequencies require multi-turn dialogue to gradually retrieve documents from corresponding sources. This avoids mixing information across data sources. Differences in fields across documents require prompts to match field rules for each document type. For example, incoming material reports must verify batch number and sampling quantity. 8D reports must guide users to fill in root cause analysis and corrective actions. Short-cycle dialogue requirements for each incoming batch require timely filtering of expired data in context. Context must also bind batch-specific unique identifiers. This prevents context interference across inspection dialogues for different batches. Strict quality risk requirements require real-time verification of required fields during dialogue. This avoids missing fields that cause errors in subsequent processes.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind a unique value per incoming material batch | Distinguish dialogue contexts for different incoming batches to avoid mixing cross-batch data |
messages | Retain the most recent 20 dialogue entries | Adapt to short-cycle dialogue needs for single incoming batches, avoid context overload |
variables | Pass batch number, supplier code, sampling plan version | Bind category-specific fields to allow prompts to call document data for the corresponding category |
| Single Maximum Matching Documents | Top 3 entries | Incoming material inspection document volume is concentrated, excessive matches will interfere with core information extraction |
| Maximum Context Duration | 7200 seconds | Covers the full dialogue cycle for inspection of a single incoming material batch |
Three Common Misconfigurations
- Phenomenon: When streaming output, link clicks only cover the current page and cannot jump to a new page. Cause: No correct jump attribute configured in the links returned by dialogue, only page anchors bound to the current context.
- Phenomenon: Dialogue log details do not match actual response content, and details remain the same after multiple dialogue initiations. Cause:
chatIdparameter not correctly bound, leading different dialogues to reuse the same context cache. - Phenomenon: Streaming output return interval is fixed at 4 seconds and cannot be adjusted. Cause: No segmented output interval parameter configured for dialogue responses, using the platform's default fixed interval.
How to Verify Proper Configuration
- Initiate a new incoming material inspection dialogue, check the
chatIdparameter via logs to confirm it updates with each batch, verifying that dialogue contexts for different batches are independent. - Enter category-specific fields (such as a specific batch number), check if the prompt calls the inspection specification document for the corresponding batch, and confirm response content matches field requirements.
- Test streaming output, adjust the segmented output interval parameter, observe changes in front-end data segment loading speed to confirm the interval can be modified.
- Initiate multi-turn dialogue, check if the context retains recent inspection records, and confirm no expired data interferes with the current dialogue.
The values provided here are common starting points, and should be evaluated against the samples relevant to the actual deployment.