What data for this category looks like
Data sources include brand marketing scripts issued uniformly by headquarters, in-store activity copy edited independently by individual stores, and standardized reply templates for member outreach. Headquarters content updates every 1-2 months in line with marketing milestones. Store-customized content updates when a single event launches. Most documents are semi-structured text with scene tags and applicable store scope metadata. Fields include event theme, compliance reminders, target customer groups, and execution details. No unified mandatory format exists, but most include store adaptation metadata.
Constraints on retrieval and recall
The layered content structure of headquarters and individual stores requires retrieval to associate store permission metadata to filter recall results. This prevents cross-store delivery of non-compliant content. Semi-structured scene tags and compliance fields require recall to return associated metadata synchronously. This ensures content can be implemented directly. Frequently updated marketing content requires an incremental sync mechanism. This reduces delays from full index updates. Markdown-formatted activity documents that lose title hierarchy cannot match the applicable store scope. This increases retrieval matching errors.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | Top 8-12 results | Professional chain marketing content is mostly short text with segmented scenarios. Too many recall results create redundant context. Too few lead to insufficient coverage. |
parse_chunk_size | 800-1200 characters | Most marketing content includes activity details and compliance reminders. Too long chunks lose scene associations. Too short chunks break script integrity. |
metadata_include_fields | ["store ID", "activity validity period", "compliance reminders"] | Professional chain scenarios require association with store permission and compliance information to ensure recalled content can be implemented directly. |
index_update_strategy | Incremental update | Marketing content has a high update frequency. Full index updates cause long synchronization delays. Incremental update ensures content timeliness. |
similarity_threshold | 0.75-0.85 | Marketing scripts use standardized phrasing. A threshold that is too low recalls irrelevant activity content. A threshold that is too high fails to match phrasing for similar scenarios. |
rerank_top_k | Top 3-5 results | Content must be filtered from recall results to match the current store scenario. This reduces manual screening costs. |
The parameter values listed on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: Retrieval results only return text fragments, and do not carry metadata such as store ID and activity validity period. Cause: The
metadata_include_fieldsparameter is not configured, and associated fields to return synchronously are not specified. - Phenomenon: After uploading QA pair-formatted knowledge base content, the large language model only returns standard answers, and does not associate the corresponding activity scene. Cause: The hierarchical association between QA pairs and scene tags is not retained. Metadata binding relationships are lost during segmentation.
- Phenomenon: After importing markdown-formatted activity documents, the subordinate relationship between titles and their associated stores is lost. This causes recalled content to fail to match the permissions of the corresponding store. Cause: Direct segmentation is selected during import, and the markdown title hierarchy is not retained. This destroys the logical association of the document.
How to confirm configurations are correct
- Upload a marketing document that includes store ID and activity validity period. Run a retrieval test. Check if returned results include both text fragments and associated metadata.
- Simulate a retrieval request for a specified store. Verify returned results only include marketing content within the permissions of that store. No cross-store non-compliant content appears.
- Import a markdown-formatted activity document. Check if parsed segments retain the subordinate relationship between titles and their content. No logical breaks exist.
- Initiate a retrieval request for frequently updated marketing content. Verify returned results include the latest updated activity scripts. No old content remains.
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.