What the data for this category looks like
Data for snack food financing daily reports comes from national equity trading platforms, local industry news aggregation platforms, and publicly disclosed financing announcements from enterprises. Updates follow the workday schedule: financing records completed on the current day will be included on the next workday. Each single record includes six core fields: subject name, financing round, financing amount, investor list, disclosure date, and affiliated subcategory. The financing amount unit is uniformly ten thousand RMB. The disclosure date format is YYYY-MM-DD.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
This category’s data characteristics impose three constraints on the multi-turn dialogue and prompt engineering link. First, due to the high update frequency, the time range of the data must be clearly limited in multi-turn dialogue to avoid returning outdated or unincluded records. Second, since the core fields include subcategory tags, the prompt must explicitly require only extracting financing records directly related to snack food, filtering data from other food and beverage categories. Third, due to scattered data sources and repeated disclosures, multi-turn dialogue should guide users to provide specific enterprise names or time ranges to narrow the query scope and reduce invalid recalls.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Snack food financing daily report single records have a relatively long average length. Multi-turn dialogue needs to retain complete historical queries, data results and user follow-up content to avoid context truncation |
systemPrompt | Fixed prompt that requires only returning financing records in the snack food field, limiting the time range, and clarifying field extraction rules | Matches this category’s data characteristics, filters financing data from unrelated categories, and standardizes dialogue output format |
recallTopK | Top 6–8 entries | Single financing daily report content is relatively lengthy. Too many recalls will cause context overload, while too few will fail to cover all relevant records required by the user |
similarityThreshold | 0.72–0.78 | There are many subcategories of snack food. A relatively high similarity threshold is needed to filter non-snack food financing records while retaining results with sufficient relevance |
customSessionFilter | Filter sessions by the customUid field | Implements obtaining exclusive historical dialogue records based on user-defined identifiers, avoiding returning full session data |
codeNodeTimeout | 600 seconds | Code nodes that process financing daily report data cleaning and format conversion require sufficient execution time to avoid timeout interruptions |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common errors
- A 404 error occurs when calling the embedding model. The cause is that the endpoint address of the
bge-large-zh-v1.5model deployed via Ollama connected through OneAPI is not configured correctly, resulting in failure to find the model service. - The function that returns only session history for the specified
customUidfails. The cause is that thecustomSessionFilterparameter is not enabled correctly, or the corresponding session identification field is not bound in the parameter configuration. - The logic to remove think tags in code nodes in the workflow does not take effect in the production environment. The cause is that the thinking content output switch of the AI dialogue node in the production environment is not turned off, causing the model to still generate content wrapped in think tags.
How to confirm the configuration is correct
- Initiate a multi-turn dialogue that includes a time range and enterprise name, verify that the returned results only include financing records in the snack food field, and that the time range meets the query requirements.
- Initiate a dialogue using the specified
customUid, then call the history record interface, verify that the returned results only include session content corresponding to this identification. - Add a chart generation node to the workflow, input financing daily report data, verify that the node can return formatted visualization configuration information.
- Run the workflow that includes the code node, verify that there is no content wrapped in think tags in the output results, and that the data format meets the preset requirements.
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.