What this category's data looks like
In the scenario of formulating talent training programs in vocational colleges, the business basis query involves three types of materials: talent training programs, course standards, and job competency analysis tables. Talent training programs are usually updated according to the academic year or the program revision cycle, and contain the program name, number of courses, total class hours, graduation requirements, formulation date, and signature fields; course standards are prepared course by course, and contain the course name, class hours and credits, corresponding jobs, author, and review signature; job competency analysis tables are prepared by job cluster, and contain the job name, competency items, corresponding courses, survey date, and expert signature. When these three types of materials enter the model access and configuration stage, the language model handles dialogue and basis explanation, the indexing model handles material chunk retrieval, and the reranking model optimizes the ordering of basis items; the materials themselves are provided as knowledge base files, and the models are only responsible for generation and ranking, without changing the material fields or versions.
Configuration and implementation checks
This step is executed by the server-side model channel configuration, and outputs the model binding relationships and channel responses. "Basis items that can be traced back to the original text" are subsequently completed by AI nodes, code nodes, or manual work. Models are divided into five categories: language, indexing, reranking, speech synthesis, and speech recognition. Different stages bind different model types, and the type labels need to be checked in the model configuration.
| Object | Check | Verification action |
|---|---|---|
| Talent training program | The name, number of courses, total class hours, formulation date, and signature fields need to match the course standards and job competency analysis table | Check the name, quantity, date, and signature field by field against the original text, and record the locations of discrepancies |
| Course standard | The course name, class hours and credits, corresponding jobs, author, and review signature need to be consistent with the talent training program and job competency analysis table | Check the course name and class hours and credits, check the corresponding jobs and signatures, and check the version date |
| Job competency analysis table | The job name, competency items, corresponding courses, survey date, and expert signature need to be consistent with the other two types of materials | Check the job name and competency items, check the corresponding courses, and check the survey date and signature |
| Model binding relationships | The language, indexing, and reranking model types correspond to the stages in which they are to be used, and the model identifiers are consistent with the names listed in the documentation | Check the type labels in the model configuration, compare them with the model names returned by the channel, and check whether the values of maxContext, maxTokens, and quoteMaxToken match the scale of the materials |
Three common errors
- Symptom: The name or class hours of the same course do not match between two materials. Cause: The materials were revised separately, versions were not synchronized, and there is a lack of unified verification and signed receipts.
- Symptom: The number of courses in the talent training program is inconsistent with the actual number of entries in the course standards. Cause: After adding or discontinuing a course, only one material was changed, and the other was not updated in sync.
- Symptom: Retrieval reports an indexing model-related error or authentication failure. Cause: The indexing stage is bound to a language model, or the channel key is inconsistent with the service being accessed.
How to verify the setup
This feature's acceptance check: run one dialogue and one retrieval, check the model identifiers in the responses, confirm that the language model and indexing model are bound to their corresponding stages respectively, and check whether the values of maxContext, maxTokens, and quoteMaxToken match the scale of the materials; the number of citations is the array length, and quoteMaxToken is the citation content budget, so the two are not compared for equality.
Business test: ask a basis question that spans materials, and see whether the answer provides traceable sources.
- In the model configuration, check the type labels and model identifiers of the language, indexing, and reranking models, and compare them with the model names returned by the channel.
- Against the original text, check the names, quantities, dates, and signatures of the talent training program, course standards, and job competency analysis table, and record differences in versions and receipts.