What the Data for This Category Looks Like
Education service investment research data mainly comes from industry research reports, education policy documents, institutional operation data, course product documents, and teacher qualification materials. Data sources cover official regulatory agencies, third-party industry consulting institutions, and internal systems of education institutions. Update frequency varies by data type: policy documents are updated quarterly, industry research reports are synced in real time, and course and teacher data is updated weekly. Documents include structured fields such as publishing institution, release date, covered educational stages, revenue scale, etc. Units include ten thousand yuan, person, and semester. A large amount of unstructured professional analysis text and course detail pages are also included.
What Constraints Do These Characteristics Impose on Context and Token Configuration
The characteristics of education service investment research data directly restrict context and token configuration logic. First, the length of unstructured analysis text varies significantly. A single in-depth research report may use thousands of tokens. Multi-source recall may easily exceed the upper limit of the context window. Second, structured fields and unstructured text are stored together. Format distinction is required during context splicing to avoid token waste. Third, frequently updated data requires incremental synchronization. If full synchronization has no token quota limit, context cache overflow will occur. Finally, the education field has dense professional terminology. Context recall requires precise matching. Otherwise, irrelevant data will be introduced, increasing token consumption.
How to Set Configuration Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000-16000 token | The average token usage of a single education investment research report is 3000-5000 tokens. This range supports recall of 3-5 documents while reserving token space for model responses |
Max knowledge base citations | 3-5 entries | Education investment research needs to associate multi-source data including policies, courses, and research reports. Excessive entries will exceed the context token upper limit, while insufficient entries will fail to cover all information required for analysis |
Chunk size | 800-1200 characters | Education documents contain a large number of professional long sentences and terminology. Too long segments will cause uneven token allocation, while too short segments will destroy semantic coherence |
Max Response Tokens | 2000-3000 token | Investment research responses need to include detailed content such as policy interpretation and course analysis. Too small a value will truncate key conclusions, while too large a value will exceed model output limits |
Similarity threshold | 0.75-0.85 | The education field has dense professional terminology. A threshold that is too low will introduce irrelevant content and increase token consumption, while a threshold that is too high will result in insufficient recalled documents |
Rerank result count | 2-4 entries | Multi-source data requires precise matching to avoid redundant content occupying too many tokens, while ensuring information coverage required for analysis |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Phenomenon: After calling the
tokenLogininterface, when switching teams, the knowledge base context fails to load the investment research data of the corresponding team, and the return fieldteam_contextis empty. Cause: TheTEAM_CONTEXT_BIND_TOKENparameter is not configured, so the association between tokens and team data is not correctly established. - Phenomenon: The model has low accuracy in understanding the context of education investment research documents, with problems including confusion of educational stages and deviations in policy interpretation. Cause: The
Chunk sizesetting is too short, splitting complete professional term paragraphs and destroying semantic coherence. - Phenomenon: The console returns the "context window exceeded" error, and cannot generate a complete investment research analysis report. Cause: The values of
Max knowledge base citationsandmaxContextare not matched, and the total token count of multiple long education documents exceeds the upper limit of the context window.
How to Confirm the Configuration is Correct
- Upload a deep education industry research report, check the parsed segment results, and confirm that each segment's length falls within the 800-1200 character range.
- Initiate an investment research query that requires multi-source information, check the number of knowledge base documents referenced in the returned results, and confirm it matches the 3-5 entry setting.
- Switch teams, call the
/api/chat/completionsinterface, and check whether the context fields in the return result include exclusive investment research data for the current team. - Adjust the
Similarity thresholdto 0.7 and 0.8, initiate test queries separately, and confirm that the number of recalled documents changes as expected with the threshold.
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.