What the data for this category looks like
The data for dairy industry financing daily reports comes primarily from publicly monitored data released by the national dairy industry association, financing announcements voluntarily disclosed by domestic dairy enterprises, third-party supply chain financial data platforms, and public information posted by local agricultural and rural affairs departments. Data is updated daily. Each individual document includes seven core fields: financing entity name, financing amount, financing round, investor list, financing completion time, involved dairy product category, and landing location. Amount units are uniformly ten thousand yuan or hundred million yuan. The round field uses standard financing stages such as Angel, Pre-A, and Series A. Some documents include a brief description of financing use.
What constraints these characteristics impose on knowledge base retrieval and recall
The daily updated data source requires the knowledge base to support incremental synchronization tasks, to avoid overusing cluster resources from full-scale pulls. Multi-dimensional structured fields (financing round, dairy product category, landing location) require structured filtering rules during retrieval, to prevent insufficient precision from unstructured retrieval. Numeric fields for financing amounts need configured numeric range retrieval adaptation logic, to meet retrieval needs for different amount ranges. Precise matching of dairy product subcategories requires entity recognition models adapted to dairy product types such as liquid milk, cheese, and infant formula milk powder, to avoid generalized matching. Differences between abbreviated and full names of financing entities require the knowledge base to enable entity normalization processing, to improve recall accuracy.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Dairy industry financing daily reports mostly use structured tables as individual documents. Each single document is under 100 KB. This setting reserves sufficient space for batch uploads |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Structured document parsing does not require complex processing. Set the timeout threshold to twice the normal parsing duration |
maxContext | 800–1200 characters | Core information of financing daily reports is concentrated in the title and first section. Excessively long context will interfere with retrieval precision |
Recall count | Top 6 entries | The number of daily financing events is limited. Excessive recall will increase the workload of subsequent result processing |
Similarity threshold | 0.75–0.85 | Names of dairy product categories and financing entities have high similarity. A threshold that is too low will introduce irrelevant results, while a threshold that is too high will miss matching items |
Incremental synchronization interval | Every 24 hours | The data source updates daily. Synchronization frequency matches the update rhythm |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: After deploying using the default docker compose setup, accessing the knowledge base page returns a
502 Bad Gatewayerror. Cause: The default configuration does not enable port mapping for the knowledge base service, or the dependent vector database container fails to start normally. - Issue: Knowledge base retrieval response time exceeds 30 seconds, and no improvement is seen even when deploying the vector database with GPU. Cause: GPU-accelerated indexing for the vector database is not configured, and the CPU indexing mode is still used, or the recall count setting exceeds the hardware's carrying capacity.
- Issue: Retrieval results include financing events unrelated to the dairy industry. Cause: Entity normalization configuration for dairy product categories is not enabled, and no category filtering conditions are added to retrieval requests, leading to generalized recall of irrelevant content.
How to confirm configurations are correct
- Run an incremental synchronization task. Check if the synchronization log displays
Incremental synchronization completedwith no error entries, to confirm the synchronization frequency matches the data source update rhythm. - Submit a retrieval request that includes dairy product categories and financing amount ranges. Verify that the returned results have complete fields, to confirm that structured filtering rules are active.
- View the vector database's monitoring dashboard. Confirm that GPU resource usage is within a reasonable range, and that the index configuration has been switched to GPU-accelerated mode.
- Test retrieval for financing entities with different abbreviations, such as "Mengniu" and "Inner Mongolia Mengniu Dairy". Confirm that the entity normalization configuration returns matching results.
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.