What the data for this category looks like
Data sources cover e-commerce backend sales ledgers provided by brand parties, social media platform interaction data, public industry news, publicly available competitor marketing materials, and daily operation execution records. Update cadence uses multiple tiers:
- E-commerce sales data synchronizes daily
- Social media interaction data updates hourly
- Industry news and competitor materials synchronize on demand
- Operation records archive immediately upon execution completion
Document structure splits into three categories: structured reports, unstructured text, and transcribed multimedia materials. Structured reports include fields such as date, platform, transaction amount, and customer unit price. Transaction amount uses yuan as its unit, and customer unit price uses yuan as its unit. Unstructured text includes social media comments, marketing plan drafts, and user feedback records.
What constraints these characteristics impose on knowledge base retrieval and recall
Structured reports have clear fields. Retrieval must precisely match field dimensions to avoid invalid results from fuzzy recall. Data sources use multiple update frequencies. Support incremental indexing tasks to avoid excessive computing resource usage from full reindexing. Mixed-format unstructured text requires unified cleaning first. Remove platform-specific watermarks and redundant formatting before generating vectors. Dispersed multi-source data needs cross-source association indexing rules. Configure these rules to ensure sales data and marketing plans for the same brand can be recalled simultaneously. Immediately updated operation records need low-latency recall triggering logic. Use this logic to ensure the latest operation information can be retrieved quickly.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding model | text-embedding-3-small | Adapts to vector generation needs for multi-source text in brand agency operations, balances precision and computing costs |
chunk size | 800–1200 characters | Adapts to text lengths of marketing plans and operation records, avoids semantic fragmentation or vague vectors |
recall TopK | top 10–15 results | Covers recall needs for multi-dimensional investment research data in brand agency operations, provides sufficient candidates for subsequent reranking |
similarity threshold | 0.75–0.85 | Filters low-relevance recall results, avoids invalid information interfering with investment research judgments |
incremental update toggle | enabled | Adapts to data sources with multiple update frequencies, reduces resource usage from full indexing |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing duration needs for long documents such as quarterly marketing plans |
Three Common Misconfigurations
- After calling the knowledge base vector reindexing interface, existing recall results do not change. Cause: The reindex option for the associated knowledge base is not checked, only the index for a single file is reindexed.
- The accuracy of recall results for the same question decreases after multiple rounds of dialogue. Cause: No session context truncation rule is configured. Overly long historical conversations introduce irrelevant context during retrieval, interfering with vector matching.
- A large number of irrelevant social media comment entries appear in retrieval results. Cause: Structured data field matching configuration is not enabled. Only full-text retrieval is used, leading to insufficient precision in field dimensions.
How to Verify Proper Configuration
- Upload a brand agency operation e-commerce ledger document. Check if parsed segmented results match the configured segment length range. Adjust parameters to match the document structure.
- Initiate a retrieval request containing field keywords. Verify that recall results prioritize matching structured report field dimensions. Cases where only full-text keywords are matched are not prioritized.
- Upload an updated operation record. Check if the knowledge base automatically triggers incremental indexing. Latest content can be retrieved without full reindexing.
- Replace the
embedding model. Run the vector reindexing task. Verify that recall result relevance for the same query aligns with expected adjustment directions.
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
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.