What the Data for This Category Looks Like
The data source for collateral material risk control is user-submitted paper scans or electronic documents. These include enterprise/personal guarantee letters, mortgage property ownership certificates, third-party guarantee agreements, and similar materials. Update frequency is once per submission, with updates only when guarantee relationships change. There are no periodic bulk updates. Most documents are multi-page PDFs or scanned files. Their structure includes core fields such as guarantee subject qualifications, guarantee amount, guarantee period, and signature pages. Fields include guarantee amounts with currency units, certificate numbers, mortgage property addresses, and similar items. Units include ten thousand yuan, years, square meters, and others.
Constraints on Deployment and Upgrade Posed by These Characteristics
Single submissions and low update frequency: The file parsing module during deployment must adapt to messy layout issues in multi-page scanned documents. No frequent adjustments to incremental synchronization logic are needed during upgrades.
Multiple fields include units: The parsing module must extract the binding relationship between fields and units. Extracting only numerical values will lead to errors in risk control logic.
Complex multi-page document structure: Configure segment parsing thresholds during deployment to adapt to long documents. Multi-page OCR alignment logic must be updated synchronously during upgrades.
Local area network deployment scenarios: Collateral materials are sensitive internal documents. Configure local vector databases and data encryption during deployment. Retain data isolation rules during upgrades.
Configuration Settings
| Configuration Item | Recommended Value | Basis for Selection |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Collateral materials are mostly multi-page long documents, which take longer to parse. 600 seconds covers the parsing process for most complex scanned files |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Collateral materials may include high-resolution multi-page scanned documents. 1000 MB can accommodate a full set of collateral materials submitted in a single batch |
| Segment Length | 800–1200 characters | Core fields and text logic in collateral materials are mostly coherent long sentences. A segment length of 800–1200 characters preserves context integrity and avoids semantic breaks after splitting |
| Similarity Threshold | 0.75–0.85 | Core verification fields for collateral materials require high matching accuracy. A threshold that is too low will introduce false positives, while a threshold that is too high will miss compliance risks |
| Number of Recalled Entries | Top 8 entries | Associated verification of collateral materials needs to cover multiple relevant documents. 8 recalled results balance recall completeness and retrieval efficiency |
VECTOR_STORE_TYPE | milvus | Data volume for collateral materials in production environments grows quickly with business. Milvus' distributed expansion capability supports efficient retrieval of subsequent incremental data |
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. Each case requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: A
401 Unauthorizederror is returned when calling the model. Cause: TheCHAT_API_KEYis entered incorrectly, and does not match the key for the locally deployed glm or qwen model. - Issue: A MongoDB connection timeout error appears after deployment starts. Cause: Correct MongoDB intranet address and port are not configured, or the MongoDB service is not started during local deployment.
- Issue: The number of vector retrieval results does not match the configured number of recalled entries. Cause: The pg version vector database configuration was directly migrated to the milvus version, and corresponding sharding and retrieval logic were not adjusted.
How to Verify Proper Configuration
- Upload a single standard collateral material document, view the parsed text content, and confirm that core fields have been correctly extracted. Adjust the values of relevant configuration items based on extraction results.
- Initiate a vector retrieval request, check the number of returned associated documents, and adjust the configuration for Number of Recalled Entries and Similarity Threshold based on business needs.
- Start the deployed service, check the connection status of MongoDB and the vector database, and confirm there are no timeout or authentication errors.
- Test the model calling process for local or local area network deployment, and verify that the
CHAT_API_KEYconfiguration can normally trigger model responses.
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.