What the data for this category looks like
Intelligent due diligence data for professional chains comes primarily from public financial report disclosures, in-store POS systems, supply chain management platforms, and membership management systems. There are two update frequency categories: in-store operations and membership data are updated weekly; annual expansion and financial report data are updated quarterly. Document structures are primarily structured tables, with fields including store ID, store name, city of location, monthly per-store revenue, per-square-meter efficiency, supply chain fulfillment rate, member in-store visits, repeat purchase visits, and more. Units are respectively: stores, ten thousand yuan, yuan/square meter, ten thousand yuan, times, times.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Multi-source data is updated on different cycles. Multi-turn dialogue must switch context scope based on data update nodes to avoid referencing expired annual expansion data. There are many structured fields with mixed units. Prompts must clearly specify unit mapping rules to prevent unit confusion during dialogue. A single due diligence report contains detailed data for dozens of stores. Multi-turn dialogue must limit the length of document fragments recalled in a single round to avoid context overload. Membership data is closely linked to store data. Multi-turn dialogue must support cross-collection associated queries to ensure matching accuracy between store and membership data in responses.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recallCount | Top 8–12 entries | Professional chain due diligence reports include detailed data for multiple stores. Too many single-round recalls will cause context overload, while too few will fail to cover key store data |
similarityThreshold | 0.72–0.80 | Structured fields have high matching accuracy requirements. A threshold that is too low will introduce irrelevant store data, while a threshold that is too high may miss valid information |
maxContext | 6000–8000 characters | A single due diligence report contains many document fragments. Limiting context length prevents the model from exceeding its token limit |
systemPrompt | Specify that the output format includes store ID, corresponding fields and units, and prioritize data from the latest cycle | There are many structured fields with mixed units. Clear prompts reduce unit confusion and expired data issues |
pluginAutoTrigger | Trigger based on keywords in user questions | Multi-turn dialogue requires on-demand calls to database plugins to supplement real-time store data, avoiding unnecessary plugin calls |
maxToken | 16000 characters | Responses need to include summary and detailed data for multiple stores. Sufficient token length ensures response completeness |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: An error occurs when starting a conversation after configuring a database connection plugin, with the prompt "Plugin configuration invalid". Cause: The database plugin is not bound to the corresponding knowledge base collection, or the plugin's connection parameters do not match the data source format of professional chains.
- Symptom: Documents in the specified store collection cannot be searched during multi-turn dialogue, and the returned results include irrelevant store data. Cause: The collection name to be retrieved is not clearly specified in the prompt, or the retrieval scope is not limited to the specified collection.
- Symptom: The model temperature parameter cannot be set in variable reference mode, and the interface settings button disappears. Cause: In FastGPT V4.9.3, the advanced parameter settings are hidden by default in variable reference mode. Advanced mode must be manually enabled before adjusting the temperature.
How to confirm the configuration is complete
- Initiate a query that includes a specified store ID, check whether the returned results only include field data for the corresponding store, and adjust configuration parameters until the results meet expectations.
- Trigger a database plugin call, check whether the data format returned by the plugin matches the data source of professional chains, and verify that the connection parameter configuration is correct.
- Initiate a query that includes a summary of multiple stores, check that the context length of the response does not exceed the model's limits, and adjust parameters to adapt to content requirements.
- View the conversation history, confirm that the system prompt clearly specifies unit mapping rules, and check that no unit confusion or expired data appears in the response.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.