What Data for This Category Looks Like
The data for this category primarily comes from ERP/MES systems, component batch ledgers, tightening and refrigerant charging equipment, safety compliance testers, performance laboratory equipment, and after-sales fault databases. Daily documents include configured BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports, and rework records. Data update cycles follow individual product units, shift output, or spot check plans. Each individual document contains fields such as material codes, charging parameters, test results, and rework details.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Words
The data sources for this category are scattered, update cycles are flexible, and each individual data entry is tied to a single product. This requires multi-turn dialogue to bind to the batch information of the corresponding product, to avoid model confusion during mixed-line audits. There are many types of daily documents. Prompts must clearly distinguish the retrieval scope for different audit links such as assembly, testing, and rework, and only call knowledge base content of the corresponding type. The high-frequency update characteristic requires dialogue context to be limited to recently updated batch data, to prevent referencing expired assembly work instructions or test standards. Additionally, the direct consumer-facing attribute requires retrieval results to strictly match source data fields, to avoid outputs that do not match actual parameters.
How to Set Up the Configuration
This configuration is based on FastGPT v4.17.0, and is adapted for the inspection and process record audit scenario for complete household appliance manufacturing.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Allowed file types for knowledge base upload | Only .xlsx and .pdf formats | Matches the document formats commonly used for inspection records and BOMs in this category |
| Number of retrieval results returned per request | Top 3 entries | The volume of associated data for a single product is limited, to avoid interference from redundant information |
| Context window length | 800–1200 token | Adapts to the length of inspection records and assembly parameters for a single product |
| Maximum knowledge base upload size | 1000 MB | Matches the maximum volume of single-batch inspection records for this category |
chatId binding rule | Bind to product batch number | Ensures multi-turn dialogue is tied to audit data for the corresponding batch, to avoid mixed-line errors |
messages context truncation | Truncate historical conversations exceeding 10 turns | Prevents retrieval deviations caused by overly long context |
Three Common Mistakes
- Phenomenon: An error message appears in dialogue indicating that the uploaded .xlsx format inspection record file cannot be read. Cause: The allowed file types were not limited in the knowledge base configuration, or the uploaded file did not complete the text parsing process.
- Phenomenon: Retrieval results include assembly data for non-target household appliance models, and the number of results exceeds the preset range. Cause:
chatIdwas not bound to the product batch number, multi-turn dialogue did not limit the retrieval scope, and document content from other batches was mixed in. - Phenomenon: Dialogue response is delayed, or output content is unrelated to the current audit task. Cause: The
messagescontext was not truncated, and too much historical dialogue was retained, exceeding the carrying limit of the model context window.
How to Confirm the Configuration is Correct
- Upload a commonly used .xlsx format inspection record file for this category, and verify that the knowledge base parsing status displays success.
- Initiate a query containing the product model and batch number, and confirm that the retrieval results only return document content for the corresponding batch.
- Initiate multiple consecutive queries, and check that the context correctly retains batch information, with no cross-batch retrieval results appearing.
- Test a conversation exceeding the preset number of turns, and check that the system automatically truncates historical context to avoid interference from overly long content.
The values provided are common starting points, and should be evaluated against the reader's own samples.