What the Data for This Category Looks Like
Data sources include publicly available policy documents from education authorities, publicly available enrollment and discipline construction materials from educational institutions, education industry research reports, standardized exam proposition specifications, and performance analysis data. Policy documents are updated quarterly or annually. Institutional data is updated with enrollment cycles. Industry reports are released as needed. Document structures are mostly long-text reports, structured tables, and policy clauses. Fields include discipline codes, enrollment numbers, qualified exam candidates, budget amounts, semester duration, and more. Units include people, ten thousand yuan, semester, and class hour.
Constraints Imposed on Model Access and Configuration
Educational service investment research data uses mixed formats including long-text reports, structured tables, and policy clauses. Long texts account for a large share, and field units vary widely. This requires model access to support multi-format parsing. Data update rhythms are inconsistent. Policy documents have long update cycles, while institutional data changes with enrollment cycles. Trigger logic for incremental synchronization must be configured. Fields contain multi-dimensional information. Field standardization mapping rules must be configured before model access to prevent mismatches between units and fields. Additionally, many referring expressions appear in this scenario. A pre-processing referring expression resolution and question expansion workflow must be configured to adapt to common abbreviations and cross-context referring expressions in education scenarios.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
parse_chunk_size | 800–1200 characters | Educational service data mostly consists of long-text reports and structured tables. This chunk length balances context integrity and retrieval accuracy, avoiding logical breaks caused by over-splitting long texts |
recall_top_k | Top 8–12 entries | Educational investment research data contains multi-dimensional fields including disciplines, policies, and enrollment. A sufficient number of candidate documents must be recalled to cover query needs across different scenarios |
similarity_threshold | 0.72–0.78 | Terminology and policy expressions in education scenarios have high similarity. This threshold filters low-relevance results while retaining valid matching content, avoiding omission of key information |
max_context | 12000 characters | Long-text reports have strong context dependencies. A sufficient context window is required to support the model in understanding complete policy logic and discipline connections |
field_standardization | Automatically map per preset field rules | Educational data includes fields with multiple units. Automatic mapping avoids mismatches between units and numerical values, ensuring the accuracy of model calculations |
incremental_sync_interval | Every 24 hours | Educational policy data has a long update cycle, and institutional data is updated with enrollment cycles. This interval balances synchronization efficiency and data timeliness |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Phenomenon: After data query, the model summary has numerical deviations, and the returned results do not match the original data. Cause: Field standardization mapping rules are not configured. Units from different sources are not unified, leading to unit confusion during model calculations.
- Phenomenon: The interface shows third-party large model access failure, returning
500 Internal Server Error. Cause: The model's API key and request timeout are not configured correctly. Long-text requests for educational service data exceed the default timeout threshold. - Phenomenon: Retrieval results contain unclear references, such as only mentioning "this institution" without specifying the target object. Cause: The pre-retrieval referring expression resolution and question expansion workflow is not enabled. Common abbreviations and cross-context referring expressions in education scenarios are not processed.
How to Verify Proper Configuration
- Upload a long-text report from the education industry, check the parsed chunked results. Confirm that the chunk length matches the preset configuration, with no logical breaks caused by forced truncation.
- Submit a query containing referring expressions, such as "Last year's enrollment numbers". Check if the retrieval results automatically supplement the referring object. Confirm that the referring expression resolution and question expansion workflow is active.
- Submit test data containing multi-unit fields. Check if the model's returned summary results unify the unit format. Confirm that the field standardization mapping rules are active.
- Manually trigger an incremental synchronization. Check if the knowledge base only updates newly added data. Confirm that the incremental synchronization logic is working correctly.
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.