What the Data for This Category Looks Like
Data sources for education service yield and market data include public disclosure documents from education-themed financial plan issuers, aggregated data from education industry financial service platforms, and education-focused financial product information filed with regulatory authorities. There are two update schedules: publicly traded education-themed financial products receive daily market data updates, while non-traded education savings products update yield data monthly. Data is structured as a table. Each entry includes the product’s unique identifier, full issuing institution name, product’s education scenario category, data statistics cycle, yield value, and net value change margin. Yield values use annualized percentage yield as the unit. Net value change margin uses basis points as the unit. Statistics cycles use natural days or natural months as the unit.
Constraints for Multi-turn Dialogue and Prompt Engineering
The scattered sources and differing update cadences of education service data require confirming the user’s desired product type and statistics cycle during multi-turn dialogue, to avoid returning expired or mismatched information. The structured nature of multiple data fields requires prompts to clearly preset field extraction rules, preventing the AI from generating unstructured redundant content. The large number of product categories for education-focused financial products requires adding scenario tag constraints in prompts to help the AI accurately match user needs. It also requires narrowing the matching scope incrementally during multi-turn dialogue to improve result accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Education service yield data documents typically include multiple sections of historical data and field descriptions, so sufficient context must be retained to avoid truncating critical information |
UPLOAD_FILE_MAX_SIZE | 20 MB | Disclosure documents for education-focused financial products are often multi-page PDF files, so this setting must accommodate larger file uploads |
PARSE_FILE_MAX_TOKENS | 16000 | When parsing education product disclosure documents, this setting must cover complete yield statistics cycles and all field descriptions |
Recall count | Top 7 entries | There are many categories of education-focused financial products, so retrieving a sufficient number of related data entries ensures coverage of potential user needs |
Similarity threshold | 0.75 | Differentiate education-themed financial product data from data from other sectors, to avoid retrieving irrelevant content |
Rerank result count | Top 3 entries | Focus on the most relevant education-focused product yield data, reducing redundant information in conversations |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Issue: Education product disclosure documents can be parsed normally in a local development environment, but cannot be recognized after mirror deployment with no error prompts displayed. Cause: File parsing dependencies were not installed correctly during mirror deployment, or the
PARSE_FILE_TIMEOUT_SECONDSparameter was set too short, causing parsing timeout without triggering an error. - Issue: After adding a judge in the advanced orchestration of version 4.6.9, the AI dialogue node cannot obtain the initial user question. Cause: The
user_queryfield was not passed along the orchestration link to the AI dialogue node, resulting in loss of the initial input content in the context. - Issue: Feishu usernames cannot be obtained in conversation logs, or return null values. Cause: User information acquisition interface permissions for Feishu integration were not configured, or the log collection rules did not include the user's unique identifier field.
How to Verify Proper Configuration
- Upload a public disclosure PDF document for an education product, check that the parsed data fields include the preset product information, and adjust related configurations until the target document is fully parsed.
- Initiate a test dialogue with multi-turn requirements, such as first asking about the yield of a certain type of education product, then following up on a specific statistics cycle, and check if the AI can associate context and return accurate results.
- Configure an advanced orchestration link, add a judge, trigger a test dialogue, check if the initial user question is correctly passed to the AI dialogue node, and verify the link transfer logic.
- Initiate a test dialogue containing images, check if the uploaded images are displayed normally in the dialogue interface, and confirm that the image parsing configuration has been enabled as required.
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.