What the data for this category looks like
Brand agency operation research data mainly comes from brand e-commerce backends, social media advertising backends, public competitor operation materials, industry compliance filing documents, and user review content. Data update frequencies range from real-time to monthly: e-commerce sales and social media exposure data are updated hourly, public competitor materials are updated weekly, and compliance filing documents are updated quarterly. Document structures include structured tables (such as SKU lists, advertising ROI reports), long texts (such as brand promotional copy, user long reviews), and semi-structured content (such as social media image and text tags, interaction data). Fields include SKU code, advertising channel, exposure volume, conversion rate, compliance filing number, and more. Units include times, yuan, percentage, and more.
Constraints imposed on model access and configuration
The multi-speed update cycle of research data requires configuring trigger rules for scheduled incremental synchronization and full synchronization, to avoid frequent synchronization occupying resources or missing real-time data. The high proportion of structured tables and semi-structured content requires configuring field mapping rules for entity extraction during model access, to accurately identify specific business fields such as SKU codes and compliance filing numbers. The large share of long-text user reviews and promotional copy requires that the maximum input length supported by the embedding model matches the average character count of single texts, to avoid truncating key information. Differences in format across data sources require configuring parsing templates for multiple document types, to adapt to content with varying structures such as e-commerce reports and social media copy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Adapts to the average length of long-text user reviews and promotional copy in the brand agency operation scenario, avoiding truncation of key information |
embeddingModel | text-embedding-3-small or locally deployed m3e-base | Supports vector extraction of multi-format texts, adapting to processing needs for structured reports, semi-structured social media data, and long-text user reviews |
UPLOAD_FILE_MAX_SIZE | 200 MB | Matches the conventional maximum file size limit for single e-commerce reports and compliance documents in the brand agency operation scenario |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Reserves sufficient time for parsing large compliance documents or batch advertising reports, avoiding mid-process interruptions |
recallTopK | Top 8-12 entries | Balances coverage of operation data across multiple competitors and brands, avoiding excessive redundant information that affects research efficiency |
similarityThreshold | 0.75-0.85 | Differentiates valid competitor operation data from irrelevant content, reducing the probability of incorrectly retrieving non-target documents |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, 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 configuration mistakes
- After deploying the
m3evector model locally, the knowledge base always displays an "indexing" status with no progress updates. The cause is that the API access port and authentication information for the local model are not configured, or the connection parameters for the vector database are incorrect, preventing the indexing task from proceeding normally. - After accessing the DeepSeek model, extraction results for ID cards or compliance filing numbers from documents are empty. The cause is that specific business field mapping rules for entity extraction are not configured, the model is not guided to recognize exclusive fields in the agency operation scenario, or key information is lost due to context truncation.
- When using the
text-embedding-2vector model, continuous call errors are returned. The cause is that the model was not correctly added to the API channel, or the access key and region parameters for the model were not configured, preventing API call requests from being processed normally.
How to confirm configuration is complete
- Upload a single brand e-commerce report document, check if parsed fields correctly identify business fields such as SKU codes and prices, to confirm that parsing rule configuration is effective.
- Initiate a vector recall test, pass preset research keywords, check if the number and similarity of retrieved documents match preset configuration rules, to confirm that recall parameter configuration is correct.
- Call the model interface, pass test text containing user reviews and compliance copy, check if the model output includes expected entity extraction results, to confirm that model access configuration is correct.
- View the knowledge base's indexing progress logs, confirm that the locally deployed vector model task completes normally with no error messages, to confirm that deployment and connection configuration is correct.
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.