What this type of data looks like
Data for refining and chemical financing daily reports originates from public financing announcements of petrochemical industry enterprises, supply chain finance platform disclosures, bank credit disclosures, and exchange public disclosure documents. Updates occur daily, covering refining and chemical-related financing projects added on the previous trading day and the current day. Each individual document follows a fixed structure, with fields including financing entity name, financing amount, financing term, fund usage, credit granting institution, and disclosure date. The amount field uses ten thousand yuan or hundred million yuan as its standard unit. The term field uses natural months or working days as the marking unit.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The daily incremental update requirement means the index must support low-latency incremental writing and updates, to avoid resource consumption from full index rebuilding. The combination of fixed structured fields and mixed text content requires the vector model to align embeddings for both numeric fields such as financing amount and descriptive text such as fund usage, to avoid single semantic bias. Each daily report includes multiple independent financing projects. Chunking must use a single financing project as the minimum unit, to prevent semantic confusion across projects. It must also ensure the index recall granularity matches the business query granularity. Financial financing data has high requirements for recall accuracy. A reasonable similarity threshold must be configured during the indexing stage to filter low-correlation recall results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 300–500 characters | The text length of a single financing project falls within this range, preventing semantic fragmentation caused by cross-project chunking |
embedding_model | Doubao-embedding-v2 | This model supports mixed embeddings of structured numeric values and text, and aligns with the business semantics of refining and chemical financing |
index_refresh_interval | 1 hour | Matches the daily update rhythm of the daily reports, balancing index real-time performance and system resource usage |
recall_top_k | Top 8–12 results | The number of financing projects included in a single daily report is usually under 10, so this range covers all relevant projects |
similarity_threshold | 0.75–0.85 | Filters low-correlation non-refining and chemical financing projects, improving retrieval accuracy |
vector_index_type | HNSW | Balances high-dimensional vector retrieval speed and recall accuracy, adapting to the daily incremental update business scenario |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The index displays a not ready status after construction, and retrieval requests cannot be initiated. Cause: The
index_refresh_intervalparameter is not configured, or its value is set too long, causing index synchronization delay that fails to cover the day's updated financing data. - Symptom: Embedding results from the
Doubao-embeddingmodel fail to accurately match refining and chemical financing business queries. Cause: Embedding alignment rules are not configured for structured fields such as financing amount and term, leading to semantic embedding bias in mixed fields. - Symptom: Retrieval results return only fragment text, without corresponding original document traceability information. Cause: Original document metadata fields are not retained during chunking, so the vector index fails to associate document identifiers and chunk positions.
How to Verify Proper Configuration
- Upload a single refining and chemical financing daily report document, and check that chunking results use individual financing projects as independent units, with no cross-project chunking.
- Initiate a query that includes financing amount and fund usage, and verify that the similarity scores of recall results fall within the preset threshold range.
- Wait for the daily incremental update to complete, and check that the index status switches to ready, allowing normal retrieval requests.
- View the traceability information of retrieval results, and confirm that each recall result is associated with the specific document and position of the original financing project.
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.