What this category's data looks like
Enterprise sales product training involves three types of materials: product manuals, sales sales language, and channel training schedules. Product manuals carry parameters and compliance wording, sales sales language carries scenario responses, and channel training schedules carry scheduling and assessment milestones. The material sources are the three lines of product, sales, and channel, and the update cadence changes with version iterations and channel rounds. Field characteristics include name, quantity, date, signature, and receipt. When the materials enter the vector model and index feature, product manuals and sales sales language serve as text index sources, and channel training schedules serve as structured record sources, jointly supporting the correspondence between index fields and the vector store.
Configuration and implementation checks
This step is executed by the index service and the vector store, outputting vectors and index status (whether recall is possible, whether data in the same group is merged). The version ledger and change records are then completed by subsequent AI nodes, code nodes, or manual work (retrieval and reference trimming only take supporting materials). The index model is consistent with the vector model actually used by the knowledge base, and the index field of a data row records multiple vector identifiers.
| Object | Check | Verification action |
|---|---|---|
| Product manual | Whether the name, quantity, date, and signature receipt are consistent between the manual and the sales language | Open a data row to check the index field and the defaultToken budget, and confirm the status is indexed |
| Sales sales language | Whether the version number and references are synchronized, and whether the old version is traceable | Retrieve data in the same group to see whether it is merged, and check that maxToken and the number of references are recorded separately |
| Channel training schedule | Whether the scheduling dates and assessment signatures match the manual and sales language | View the data range covered by the rebuild task records, and confirm that after changing the model, it is triggered only for data that has already been indexed |
| Version ledger and change records | The current version is unique, the old version is traceable, and references are synchronized | After changing one piece of content, check whether the version number and references are updated synchronously, and record the number of array entries and reference tokens |
Three common errors
- Symptom: The name, quantity, and date of the same record do not match between the manual and the sales language, and data rows that have not completed indexing do not participate in recall, so indexing must be completed before rebuilding.
- Symptom: Duplicate entries appear in retrieval results. Inspect the following: Data in the same group is not merged by the highest score, and the index field records multiple vector identifiers that are not consolidated.
- Symptom: After changing the model, recall quality drops, and data rows have content but cannot be retrieved. Inspect the following: The rebuild task records do not cover all data that has already been indexed.
How to verify the setup
Feature acceptance: sample a data row to check the index field, confirm that multiple vector identifiers are all present and the status is indexed, then retrieve once to see whether the same group is merged and the highest score is taken.
Business test: change one piece of content, see whether the version number and references are updated synchronously, record the number of array entries separately, and check the reference text tokens separately.
- In the knowledge base settings, check that the index model is consistent with the vector model actually used, and view the coverage scope of the rebuild task records.
- Open a data row to check the index field, and confirm that under the
limitbudgets, the number of references and tokens are recorded separately.