What the Data for This Category Looks Like
Feed marketing content data is sourced primarily from public feed ingredient databases, feeding feedback logs from large-scale livestock farms, industry standard recipe documents, enterprise in-house product parameter libraries, and marketing and customer acquisition requirement documents for feed enterprises in the financial sector. Update frequency varies by data source type:
- Ingredient market data updates daily.
- Recipe parameters adjust every 1-3 months based on raw material price fluctuations.
- Standardized feeding plan documents are released on a fixed schedule.
- Financial service requirement documents update irregularly alongside enterprise business adjustments.
Each individual document includes fields such as ingredient composition, applicable livestock and poultry categories, feeding dosage, and other details. All fields have clear associated units. Each document also includes supporting promotional copy and product model identifiers.
What Constraints Do These Characteristics Impose on Deployment and Upgrade
The data characteristics of feed marketing content create clear constraints for the deployment and upgrade process for financial sector use cases. Multi-source data has differentiated update cadences. This requires configuring toggle logic between incremental synchronization and full refresh during deployment, to avoid overloading server computing power with full synchronization operations. Field units vary widely. This requires pre-configuring unit conversion rules during the parsing stage, to prevent parameter parsing errors that could lead to incorrect feeding recommendations. Product models and applicable livestock and poultry tags in marketing content require corresponding weight adjustments in vector recall configurations, to improve the accuracy of matching results. The upgrade phase must support format conversion for legacy recipe data. This prevents historical existing materials from failing to load properly in the new system, and must comply with financial sector compliance requirements.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the long-text marketing document parsing requirements of financial sector feed enterprises, reserves sufficient parsing time |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for feed marketing materials including high-definition ingredient images and long documents, satisfies large-file storage requirements for financial scenarios |
recall_count | Top 8-12 results | Balances accurate matching of feed marketing content and coverage of promotional needs for different livestock and poultry categories, aligns with the precise targeting goals of financial customer acquisition |
similarity_threshold | 0.72-0.85 | Strictly filters low-relevance results, prevents incorrect delivery of feed product content that does not match livestock farming scenarios, ensures the professionalism of financial marketing |
VECTOR_MODEL_PATH | ollama://bge-large-zh-v1.5 | Adapts to the deployment path of mainstream open-source vector models, supports local or remote calls, meets the private deployment requirements of financial scenarios |
maxContext | 8000 characters | Adapts to the long-text structure of feed documents, retains sufficient context for generating accurate marketing content, complies with content specifications for financial marketing |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing settings.
Three Common Mistakes
- Symptom: A 404 error is returned when calling the vector model, and document vector generation cannot be completed. Cause:
VECTOR_MODEL_PATHis not correctly configured to point to the locally deployed ollama bge-large-zh-v1.5, or the oneapi interface address does not match the vector model port. This prevents normal recall of marketing materials in financial scenarios. - Symptom: Severe delays occur when generating marketing content, with single response times exceeding reasonable ranges. Cause: The
maxContextorrecall_countparameters are not adjusted, leading to excessive loading of long-text feed documents, which occupies too much inference resources and reduces response efficiency for financial customer acquisition. - Symptom: The m3e vector model cannot be added in a non-GPU environment, and the system prompts insufficient resources. Cause: CPU mode for vector model operation is not enabled in Docker startup parameters, or corresponding parameters to enable CPU inference are not configured. This fails to meet the lightweight deployment requirements of financial scenarios.
How to Confirm Successful Configuration
- Upload a test document that binds feed recipes and financial services, check field recognition in the parsing results, confirm that unit conversion and tag extraction rules are working correctly.
- Submit a test request for financial marketing content based on livestock farming scenarios, observe response times, and adjust relevant parameters alongside current server computing power to ensure compliance with response requirements for financial scenarios.
- Test the recall effect of feed marketing materials across different categories, adjust the similarity threshold and recall count to match the precise targeting needs of financial customer acquisition.
- Verify the vector model call link, confirm no connection errors, ensure the model is properly connected to the system, and meet data security requirements for financial scenarios.
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.