What this category of data looks like
This category covers multiple types of documents related to incoming material inspection and supplier quality for manufacturing enterprises. These documents include inspection specifications, sampling plans, incoming material reports, supplier audit reports, exception contact sheets, 8D reports, and concession acceptance approval documents. Data sources include purchase orders, product drawings and specifications, supplier quality certificates, inspection equipment records, historical quality data, and supplier portal information. New data is generated with each batch of incoming materials. Supplier evaluation updates are completed monthly or quarterly. Audit-related data iterations are carried out irregularly. Document fields include batch number, AQL sampling value, non-conforming item classification, supplier qualification number, and other items. Some files require version number and batch information matching verification. The quality control objects of this category come from external suppliers. The data source logic differs from internal process inspection, so the exclusive data scope of external suppliers must be strictly distinguished.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data for this category is scattered across multiple sources such as purchase orders and supplier portals. Update frequency changes dynamically with each batch of incoming materials and quarterly evaluations. Multi-turn dialogue must associate context parameters to anchor exclusive data for the current batch or supplier. Documents contain exclusive fields such as batch number and AQL value. The dialogue must clearly match version and batch information to avoid confusion across batch rules. The field logic of multiple document types varies greatly. Prompts must guide users to specify the queried document type. The number of documents recalled in a single search must be limited to prevent redundant information from interfering with dialogue judgment. The need to associate historical quality data with current inspection results requires dialogue context to retain sufficient historical interaction information for traceability.
Configuration Settings
This configuration is written for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of documents such as inspection specifications and sampling plans, avoid splitting key fields |
| Overlap Length | 100–150 characters | Retain cross-block associated information such as batch numbers and version numbers, prevent fields from being split and broken |
| Single Search Limit | Top 6 entries | Match the number of documents related to single-batch inspection for this category, avoid recalling too many unrelated historical data |
| Maximum Context | 4000 tokens | Carry batch information and historical inspection results in multi-turn dialogue, ensure coherent interaction logic |
chatId | Bind to the unique identifier of the current inspection batch | Distinguish dialogue contexts of different incoming material batches, avoid cross-batch data confusion |
variables | Preset batch number and supplier name variables | Automatically bring these into the dialogue context, reduce the operational cost of repeated user input |
Three Common Misconfigurations
- After uploading inspection specifications or supplier quality certificates, the data processing link returns empty results. The cause is that the chunk length is set too large or too small, causing key paragraphs containing batch numbers and AQL values to fail to be fully indexed.
- The knowledge base search debugging works normally, but the AI dialogue response does not associate the uploaded incoming material report content. The cause is that the
chatIdparameter is not bound, causing the dialogue context to fail to associate the knowledge base data of the current batch. - After logging into the database, the specified batch's dialogue history cannot be queried. The cause is that the dialogue history storage rule is not configured correctly, causing the
messagesparameter to fail to be correctly written to the corresponding collection.
How to Confirm the Configuration Is Correct
- Upload an incoming material report file with a clear batch number, initiate a dialogue to query the sampling rules of this batch, and verify that the returned result contains the AQL value in the report.
- Initiate more than two rounds of dialogue, mention different incoming material batches each time, and verify that the system can correctly distinguish the contexts of the two dialogues and return the inspection rules corresponding to each batch.
- Call the dialogue interface to send a request, check whether the returned result contains the
chatIdfield, and that the field value matches the identifier of the current inspection batch. - View the data processing log, confirm that the uploaded documents have been successfully split and index entries have been generated, and there are no empty index records.
The values given are common starting points and should be measured against the reader's own samples.