What the Data for This Category Looks Like
Collateral materials originate from paper or electronic guarantee documents attached to individual credit grant applications. These include guarantee contracts, collateral ownership certificates, guarantee qualification certificates, and similar items. Submissions occur once when an individual credit grant application is initiated. Existing collateral materials are only updated when the main creditor’s rights change. Most documents are multi-page structured text, with some unstructured content such as handwritten signatures or scanned attachments. Fields include guarantee amount, guarantee period, collateral valuation, guarantor entity information, and more. Units include ten thousand yuan, square meters, years, and others.
Constraints These Characteristics Impose on Vector Models and Indexing
Collateral materials contain both structured metadata and unstructured scanned content. This requires vector models to support both text semantic embedding and semantic association capture for structured fields, which increases the complexity of index storage and calculation. The volume of collateral materials submitted per individual business is concentrated, and update frequency is low. There is concentrated demand for batch indexing, so the system must support high-concurrency batch processing. Long text splitting must retain complete semantic units, to avoid semantic breaks across sentences or fields that would reduce subsequent retrieval accuracy. Some materials require OCR conversion first, which increases preprocessing time. The indexing workflow must support asynchronous OCR and vector generation in series to avoid blocking the main process.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Collateral materials are mostly long texts. This range retains complete semantic units and avoids cross-sentence semantic loss after splitting |
similarity_threshold | 0.72–0.85 | Core fields of collateral materials (such as guarantee amount, period) have high semantic similarity requirements. This range filters low-match irrelevant materials |
recall_top_k | Top 10 results | The number of collateral materials for a single credit grant is limited. Excessive recall increases subsequent reranking overhead |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Collateral materials may contain multi-page scanned documents. OCR and parsing take a long time. This duration covers the processing cycle of conventional materials |
vector_db_replica_count | 2–4 replicas | Batch indexing scenarios require sufficient replicas to support concurrency and avoid index queue backlog |
enable_metadata_filter | Enabled | Collateral materials need to be quickly filtered by metadata such as guarantor, guarantee amount, to improve retrieval accuracy |
The parameter values provided on this page are common recommendations for establishing a starting point 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 Misconfigurations
- After importing a collateral material dataset, the interface displays an "Indexing" status that persists beyond the preset timeout duration. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. OCR processing for scanned collateral materials takes longer than the default threshold, causing the indexing workflow to interrupt. - The similarity values returned by vector retrieval fall outside the 0-1 range, with values as high as 10000+. The cause is incorrect configuration of the vector model’s normalization parameters. L2 normalization of embedding vectors is not performed, so similarity calculation does not follow standard cosine similarity rules.
- When calling the vector retrieval API, requests always trigger a large language model instead of vector retrieval. The cause is failure to specify a vector model as the retrieval backend in the retrieval configuration. The default large language model remains used as the retrieval entry point.
How to Confirm Proper Configuration
- Upload a single typical collateral material, review the parsed text splitting results, and confirm that the
chunk_sizeparameter’s splitting logic meets expectations. - Trigger a batch indexing task, monitor the processing speed of the index queue, and confirm that the
vector_db_replica_countconfiguration supports concurrency requirements. - Execute a retrieval request with metadata, confirm that the
enable_metadata_filterconfiguration correctly filters non-target collateral materials. - Review the return results of the vector retrieval API, confirm that the similarity values fall within the 0-1 range and match the configured
similarity_thresholdrange.
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.