What the data for this category looks like
Data comes from public industrial and commercial change records, industry media financing columns, and third-party corporate financing monitoring platforms. Updates release daily, and are delayed to the first working day if a statutory holiday falls on the scheduled update date. Each financing entry includes the full company name, financing round, financing amount, investor list, financing completion date, primary business subdivision track, and regional information. For field units, financing amounts use ten thousand RMB as the uniform marker. Date format follows YYYY-MM-DD. Investors appear as a comma-separated list.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The standardized financing date format requires strict adherence to the YYYY-MM-DD rule when parsing user time queries in multi-turn dialogue. This avoids range deviations caused by fuzzy matching. Investors are stored as a comma-separated list. This requires prompt engineering to configure entity extraction rules that accurately locate the financing entry linked to a single investor. The dairy industry subdivision track classification requires the dialogue system to first identify track keywords mentioned by users, then apply targeted filtering to the data. The daily updated data source requires multi-turn dialogue to call the latest knowledge base slice by default. This prevents returning outdated information. The ten thousand RMB unit convention requires clear unit conversion logic in the prompt to match the amount magnitude expressions used by users.
How to Set the Configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single dairy industry financing daily report entries have many fields. Multi-turn dialogue must retain sufficient historical context to match subsequent user follow-up questions, and avoid losing key filtering conditions |
recallCount | Top 8–12 entries | Financing daily report single entries have moderate information volume. Too many recalled entries create redundant context. Too few recalled entries fail to cover all required financing entries for the user |
similarityThreshold | 0.75–0.85 | Keywords for dairy industry subdivision tracks have high recognition. A threshold that is too low introduces irrelevant financing entries. A threshold that is too high may filter out content with slightly lower matching that still meets user needs |
chunkSize | 600–800 characters | A single financing daily report record contains multiple fields. Too long a segment prevents the model from accurately extracting key information. Too short a segment destroys the integrity of the fields |
promptTemplate | Please base your response on the provided dairy industry financing daily report data. Strictly follow the multi-turn questioning logic of the user. First identify the filtering conditions in the historical dialogue, then return matching financing entries. Clearly state if insufficient data is available. | Clarify the multi-turn dialogue processing rules for the model, and avoid deviating from the financing daily report theme |
apiTimeout | 60 seconds | Loading the financing daily report knowledge base slice takes a set amount of time. A timeout causes the dialogue to interrupt and fail to return complete results |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Configuration Mistakes
- Phenomenon: The dialogue returns irrelevant content unrelated to dairy industry financing. Cause: Failing to clearly limit the use of only the current financing daily report knowledge base data in the
promptTemplate, leading the model to call unassociated knowledge base slices. - Phenomenon: A
504 Gateway Timeouterror appears during dialogue. Cause: TheapiTimeoutsetting value is less than the total time required for knowledge base slice loading and model inference, or therecallCountvalue is too high, causing context overload. - Phenomenon: The dialogue system fails to accurately identify investors or dairy industry subdivision track keywords during multi-turn dialogue. Cause: Failing to select a text understanding model adapted for entity extraction tasks, and using a general dialogue model instead, leading to insufficient entity extraction accuracy.
How to Confirm the Configuration Is Correct
- Manually trigger a multi-turn dialogue. First query dairy industry financing within a specified time range, then follow up with questions about entries matching a corresponding subdivision track or investor. Verify that returned results meet the filtering conditions.
- View the dialogue system logs to confirm that the actual used value of
maxContextmatches the configured value, and no context truncation prompts appear. - Call the interface for obtaining dialogue records, pass in the current dialogue's
conversationId, and verify that the returned dialogue history fully contains all multi-round interaction content. - Adjust the value range of related configurations, test dialogue performance across different scenarios, and confirm that it meets business 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.