Data Characteristics for This Category
Dairy industry investment research data sources include public periodic reports from dairy enterprises, raw milk procurement and supply and demand monitoring data released by industry associations, milk component test documents from third-party testing institutions, SKU sales ledgers from e-commerce platforms, and raw milk procurement records from pastures.
Update frequencies vary: Raw milk procurement prices are updated daily; dairy enterprise financial reports are released quarterly and semi-annually; industry monitoring reports are updated monthly; sales ledgers are synchronized weekly.
Documents include long research reports and structured test tables. Fields include milk fat content, total bacterial count, procurement unit price, and other metrics. Units include g/100g, CFU/mL, and yuan/kg.
Constraints Imposed on Deployment and Upgrade
Multiple data sources with varying update frequencies require differentiated incremental synchronization rules and scheduled task scheduling parameters.
Document structures that include both structured tables and long documents require preprocessing to adapt to text splitting logic for different formats.
Diverse fields and units require standardized mapping during knowledge base construction to avoid unit confusion during retrieval.
High-frequency updated raw milk data requires the vector database to support high-frequency incremental refreshes. Sufficient computing resources must be reserved during deployment to support synchronization tasks.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Dairy product test reports are mostly multi-page PDFs of 100-500 MB, and annual research report files are large. This setting covers the upload needs of most investment research documents. |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Long document parsing and multi-page table processing take a long time. This duration avoids parsing timeouts for large test ledgers. |
maxContext | 800–1200 characters | Balances short-text price data and long-text component analysis, and balances context recall accuracy and response speed. |
Retrieval Count | Top 8 entries | Dairy industry investment research needs to cover information across supply chain, component, and sales dimensions. 8 entries balances retrieval coverage and retrieval efficiency. |
Similarity Threshold | 0.72–0.85 | There are many professional terms in the dairy industry. This range filters irrelevant general industry reports while retaining relevant supply chain and component data. |
UPLOAD_INCREMENTAL_SYNC_CRON | 0 0 * * *, 0 0 1,15 * * | Synchronizes raw milk price data daily, synchronizes industry monitoring reports and financial reports on the 1st and 15th of each month, adapting to the update rhythms of multiple data sources. |
The parameter values given on this page are common starting points for configuration determination. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A
CUDA out of memoryerror is reported after the container starts, knowledge base parsing tasks fail, and logs show prompts that GPU memory usage exceeds the threshold. Cause: TheCUDA_MEMORY_FRACTIONparameter is not configured for high-resolution multi-page dairy product test reports, and the parsing shard size for single documents is not restricted. - Symptom: Redis cache cannot be connected after deployment, synchronization tasks time out, and logs show a
Connection refusederror code 111. Cause: The port mapping and network mode of the redis service are not correctly configured in docker-compose.yml, and the redis access password is not set. - Symptom: After selecting the
DeepSeek chatmodel, the corresponding version cannot be confirmed as DeepSeek V3, and the returned content does not match the dairy industry investment research scenario. Cause: The model version tag is not explicitly specified in the model configuration, andDeepSeek chatis not mapped todeepseek-chat:v3.
How to Verify Successful Configuration
- Upload a typical dairy product component test report PDF, and verify that the parsed text includes correct fields and standardized units.
- Execute an incremental synchronization task, and verify that no connection errors, timeout errors, or other abnormal prompts appear in the synchronization logs.
- View the GPU status via the container monitoring panel, verify that no GPU memory overflow related errors are present, and that GPU resources are being called normally.
- Access the access address reverse-proxied by Nginx, verify that the FastGPT interface loads normally, and that no abnormal status codes such as 403 or 502 are returned.
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.