What the data for this category looks like
Typical business stakeholders for this category include project procurement teams, integrator commercial teams, and warehouse administrators. Daily processed documents include purchase orders, arrival acceptance slips, serial number lists, and invoice materials. Data sources typically include ERP systems, supplier shipping documents, logistics tracking information, and equipment manufacturer backend management systems. Data is updated per order or weekly. Individual data entries include fields such as purchase order number, equipment model, serial number, arrival date, and acceptance status. Some documents also include logistics tracking numbers, amounts, and other information.
How these characteristics impose constraints on context and token handling
The per-order or weekly update rhythm requires context to be bound to associated data for a single batch of arrival projects. This avoids redundant recall across projects. Field differences across multiple data sources require context matching to prioritize associating all documents under the same purchase order number. This prevents misalignment of information across sources. Individual data entries include short fields such as model and serial number. Bulk Excel files with over 10,000 rows are also common. If chunk size is too large, irrelevant fields will consume extra tokens. If chunk size is too small, the association between equipment model and serial number will be broken. Additionally, the strong binding relationship between logistics information and arrival acceptance slips requires context recall to retain the complete association logic of associated documents. This avoids losing critical business associations in recalled fragments.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Aligns with the context capacity limit of FastGPT v4.17.0. A single batch of arrival data for this category includes multiple associated documents. Total token count typically falls within this range, avoiding exceeding model limits. |
quoteMaxToken | 300–500 characters | Individual business data entries, such as equipment model + serial number combinations, typically do not exceed 500 characters. This value preserves complete business associations while controlling token consumption per fragment. |
| Chunk Length | 200–300 characters | Adapts to bulk Excel files with over 10,000 rows. Balances recall accuracy and token consumption. Avoids splitting critical business association fields. |
| Single Search Limit | Top 6 entries | A single batch of arrival data typically associates with no more than 6 core documents. Recalling too many entries increases invalid token consumption. This value covers all associated data. |
Three Common Misconfigurations
- Phenomenon: The console displays Reached the max retries per request limit, or single-turn conversation token consumption exceeds interface quota limits. Cause: The
quoteMaxTokenvalue was not adjusted. With default parameters, single recalled fragments are too long. Total token count for a single batch of data exceeds model and interface limits, triggering the retry limit. - Phenomenon: Recalled results include equipment data across multiple purchase orders, with messy field matching. Cause: The purchase order number was not bound as a context association key. Recall logic does not filter arrival data not belonging to the current order, increasing invalid token consumption.
- Phenomenon: Serial numbers and models are separated after chunking Excel data, making direct association and verification impossible. Cause: Chunk length was set too large. The model and serial number for the same equipment were split into different fragments. This causes context recall to lose the association relationship.
How to Verify Correct Configuration
- Upload documents for a single batch of arrival data. View the context recall preview. Confirm that all recalled fragments belong to the same purchase order number.
- Run batch tests. Count single-turn conversation token consumption. Adjust the values of
maxContextorquoteMaxTokento fit the range of business data volume. - Trigger the retry logic. Check for max retries error messages. Confirm that parameter settings do not exceed model and interface limits.
- View chunked document fragments. Confirm that model and serial number appear in the same chunk, and are not split apart.
The values provided are common starting points. They should be evaluated against the applicable sample datasets.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.