Data Profile for This Category
Data sources for snack food intelligent due diligence include national market supervision administration inspection and public notices, monthly industry association supply chain reports, public product filing documents from brand owners, and quality inspection reports from third-party testing institutions. Regulatory data updates monthly. Enterprise public documents update quarterly. Supply chain data syncs weekly. Document structures typically include four modules: product basic information, raw material traceability, compliance inspection, and sales compliance. Fields include filing number, raw material name, purchase batch, number of qualified inspection items, shelf life (unit: days), sales coverage area, and additional relevant fields.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Differing update cycles for multi-source data create constraints. During multi-turn dialogue, clearly distinguish the timeliness of different data sources. Prompts must specify that latest regulatory inspection data takes priority. Modular document structures require multi-turn dialogue to guide users to specify the queried module. This avoids confusion between fields from different modules. Fields with clear units require prompts to mandate included units in returned results. This prevents ambiguity. Weekly synchronized supply chain data requires multi-turn dialogue to prompt users to confirm the data time range. This ensures the latest supply chain information is used.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | A single snack food due diligence document typically contains 5000–8000 characters of content, with reserved context buffer for multi-turn dialogue |
prompt_template | “Please answer based on the provided snack food due diligence data, following the user-specified module and time range. The result must include the units of corresponding fields, and clearly state if data is missing” | Clearly constrain the response scope and format, adapting to the characteristic that snack food fields include units |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Snack food due diligence documents may contain numerous supply chain details, leading to longer parsing time |
recall_top_k | Top 8 entries | Snack food due diligence has many data fields, requiring sufficient recalled entries to cover query needs across different modules |
similarity_threshold | 0.75 | Filter low-relevance non-due diligence data to avoid interference with core due diligence results |
enable_chat_history | Enabled | Support retaining context for multi-turn dialogue, enabling users to gradually refine query conditions |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: An empty string returns after calling the dialogue node, or the error message "model call failed" displays. Cause: No reasonable
similarity_thresholdis set. Large volumes of low-relevance non-due diligence data are recalled. Context overflow triggers empty returns or model errors. - Phenomenon: Dialogue records remain in the platform after a POST request to delete them. Cause: The request does not include the unique
chat_idfield for the dialogue record. The interface cannot locate the target record, so the delete operation does not execute. - Phenomenon: Dialogue cannot read Feishu online document content after configuring the document as a data source. Cause: The document’s shared link is not set to "Accessible via link". The platform cannot obtain document read permissions, leading to parsing failure.
How to Verify Correct Configuration
- Upload a public snack food due diligence document. Initiate a dialogue query for "number of qualified inspection items". Confirm the returned result includes the corresponding unit.
- Adjust the
similarity_thresholdvalue. Test the number of recalled entries across different thresholds. Confirm the setting meets the current scenario’s filtering requirements. - Initiate a multi-turn dialogue. Gradually refine query conditions. Confirm context is correctly retained.
- Initiate a complete due diligence dialogue. After completion, confirm a recommended question list matching the query topic is generated.
- Call the POST interface for deleting dialogue records. Include the correct
chat_idfield. Confirm the corresponding record is removed from the platform.
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.