What the Data for This Category Looks Like
Data sources include public industry monitoring databases, brand marketing white papers, media placement review reports, and public materials from industry associations. Update cycles follow regular monthly bulk updates, paired with temporary supplementary updates following unexpected marketing events. Document structures include placement channel details, audience group tags, cost-effectiveness metrics, trend forecasting sections, and some documents contain tabular placement data. Fields include placement budget (unit: ten thousand yuan), impressions (unit: person-times), cost per thousand impressions (unit: yuan), conversion volume, and customer unit price (unit: yuan). Document length varies widely, from hundreds of-word industry news briefs to tens of thousands of-word in-depth analysis reports.
Constraints on Vector Models and Indexing
Dispersed data sources and inconsistent formats require vector models to support unified vectorization adaptation for multi-source heterogeneous documents, to avoid semantic deviation caused by format differences. Mixed update cycles of regular bulk and temporary incremental updates require indexes to support incremental synchronization and breakpoint resumption, eliminating the need for full index reconstruction each time. Wide variation in document length requires chunking strategies to adapt to text units of different lengths, balancing semantic integrity for short texts and paragraph coherence for long texts. Multiple quantitative fields require vector models to support semantic weighted fusion of multiple fields, to improve retrieval accuracy in professional scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the text structure of advertising and marketing research reports, which contain both short sections (such as channel data) and long paragraphs (such as trend analysis), to avoid semantic fragmentation |
recall_top_k | Top 20 results | Covers multi-dimensional placement and audience-related information in research reports, to avoid missing key associated content in single recall results |
similarity_threshold | Calibrated via actual testing | Adapts to the semantic characteristics of research reports with dense professional terminology, to avoid false or missed recalls caused by generic thresholds |
vector_db_batch_size | 50–100 entries per batch | Balances index construction speed and server resource usage, adapting to the bulk update scenario for research reports |
index_refresh_interval | 15 minutes | Adapts to the mixed cycle of regular monthly updates and temporary incremental updates, balancing real-time performance and resource costs |
rerank_top_n | Top 8 results | Focuses on highly relevant research report content, aligning with user demand for precise information in advertising and marketing scenarios |
The parameter values provided on this page are common starting points for configuration setup. 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.
Three Common Misconfigurations
- Symptom: After custom document chunking, duplicate document chunks are automatically deleted in the knowledge base, causing the index order to differ from the custom chunking results. Cause: The knowledge base's document deduplication switch was not disabled. The default logic automatically filters duplicate chunks based on text hashing, which disrupts the custom chunking order.
- Symptom: After configuring a custom vector model, retrieval requests always call the large language model instead of the vector database. Cause: The vector model was not bound to the retrieval task's engine configuration, or the vector model's call endpoint was not correctly specified in the channel configuration.
- Symptom: The similarity values from vector retrieval results fall outside the normal range, and the filtering threshold cannot be adjusted via the interface settings. Cause: The similarity calculation output format of the current vector model was not adapted. Some models return cosine distance instead of normalized similarity, requiring threshold mapping to the corresponding range.
How to Verify Successful Configuration
- Upload a single typical advertising and marketing research report, check that the number of split text blocks matches the custom chunking rules, to confirm that the chunking configuration is active.
- Initiate a retrieval request, review the similarity value range of the returned results, to confirm that the threshold configuration adapts to the output format of the current vector model.
- Submit an incremental update task, check that the index only synchronizes newly added documents, to confirm that the index refresh configuration and incremental update logic work correctly.
- View the vector database import logs, confirm that the import batch size matches the configured requirement, with no abnormal timeouts or packet loss.
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.