What the data for this category looks like
The data for professional services funding daily reports primarily comes from local financial regulatory disclosure platforms, official corporate disclosure announcements, and financing tracking databases from third-party credit reporting agencies. The update schedule consists of a full refresh of the previous day’s financing events each day, with some segmented tracks adding supplementary disclosure entries from midday of the current day. Each daily report is presented as a structured table or JSON array. Each record includes seven core fields: full entity name, financing round, financing amount (unit: ten thousand yuan or hundred million yuan), investor list, disclosure date, affiliated sub-industry, and registered region. There are no redundant nested levels.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The daily updated data source requires multi-turn dialogue to call the latest interface in real time to pull data, avoiding the return of expired entries. Duplicate disclosed financing events exist across multiple data sources, so context-based deduplication logic must be added to the dialogue workflow. The financing amount field uses different units, so the prompt must explicitly require unified conversion to a specified unit for output. The structured single-record format requires multi-turn dialogue to retain the user’s filtering conditions such as industry and region as context, avoiding repeated questions. Additionally, the investor list is in array format, so the prompt must specify that only core investor names are extracted, with no redundant descriptions attached.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the storage needs of daily financing event volume and multi-turn historical records, avoiding context overflow |
recall count | Top 8–12 entries | The number of daily disclosed events for professional services funding daily reports typically does not exceed 50. An appropriate recall volume balances information completeness and context usage |
json_schema | {"type":"object","properties":{"主体全称":{"type":"string"},"融资轮次":{"type":"string"},"融资金额":{"type":"number"},"投资方名单":{"type":"array","items":{"type":"string"}},"披露日期":{"type":"string"}},"required":["主体全称","融资金额"]} | Strictly matches the core field requirements of professional services funding daily reports, ensuring output format can be parsed by downstream systems |
maxResponseTime | 600 seconds | Pulling data from multiple sources requires calls to multiple data source interfaces, reserving sufficient timeout time to avoid mid-process interruptions |
contextMemoryType | fullHistory | Must retain the user’s filtering conditions such as industry and region as context, supporting reuse of historical parameters during multi-round follow-up questions |
The parameter values provided on this page are conventional recommendations used as a starting point 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 settings.
Three Common Misconfigurations
- Phenomenon: After configuring
json_schema, the returned results still contain undefined extra fields. Cause: The prompt does not explicitly require strict adherence to thejson_schemaformat, relying only on automatic system format validation. - Phenomenon: After the user adds filtering conditions in a multi-turn dialogue, the system does not perform secondary filtering combined with historical parameters. Cause:
contextMemoryTypeis not set tofullHistory, only summary memory is enabled or context is cleared. - Phenomenon: The deployed dialogue workflow response is several times slower than local debugging. Cause: No data pre-pulling logic is configured. Each dialogue pulls full funding daily report data in full, without optimizing the interface call chain.
How to Confirm Proper Configuration
- Enter the test query "Pull today’s TMT industry funding daily report", check if the dialogue’s returned context retains the "TMT industry" filtering condition.
- After configuring
json_schema, enter a test query, check if the returned result fields fully match the presetjson_schema. - Simulate multi-round follow-up questions: first ask "Pull financing events in the Beijing region", then ask "Filter for the biopharmaceutical industry", check if the system automatically combines the two filtering conditions to generate results.
- Check interface logs to confirm no context overflow errors are triggered, and response times fall within the preset timeout range.
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.