What This Type of Data Looks Like
Coal chemical industry financing daily report data is sourced from public corporate financing announcements, local financial supervision disclosure platforms, and daily summaries from industry information terminals. It is updated daily to cover financing projects in the coal chemical industry disclosed on the previous working day, including equity financing, debt financing, supply chain finance and other types. Documents use a structured format with fields including enterprise name, unified social credit code, financing type, financing amount (unit: ten thousand RMB), financing party’s affiliated coal chemical sub-sector, disclosure date, fund provider name, and financing term. The core information length of individual documents varies widely. Statistics or testing using local samples is recommended before finalizing settings, and there is no ultra-long nested content.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Daily incremental updates require the index to support incremental writing and scheduled synchronization, to avoid resource consumption from full index rebuilding. Multiple structured fields require the vector model to support joint encoding of multiple fields, or require field-level index optimization to improve retrieval accuracy. The short length of individual documents requires vector encoding to adapt to short text scenarios, while controlling chunk size to avoid redundant index entries. Financial data compliance requirements require retaining data traceability fields, to ensure retrieval results can be traced back to original disclosure information. In addition, demand for matching financing projects by the same track requires the vector retrieval similarity threshold to adapt to semantic matching accuracy in the financial field, avoiding mixing of irrelevant results.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
EMBEDDING_MODEL | text-embedding-3-large | Supports joint encoding of multiple fields, adapts to the structured fields of coal chemical industry financing daily reports, and the vector dimension can cover the semantic matching needs of financial terms and enterprise names |
INDEX_CHUNK_SIZE | 300–500 characters | Most individual financing daily report documents are within 200 characters in length. Too small a chunk will increase the number of index entries, while too large a chunk will lose context association |
INDEX_INCREMENTAL_SYNC | Enabled, synchronize incremental data from the previous day at 00:00 daily | Financing daily reports are incremental data updated daily. Full index rebuilding will occupy too many computing and storage resources |
RECALL_TOP_K | Top 10 results | The demand for matching coal chemical financing projects by the same track. Too many recalls will increase subsequent processing time, while too few will fail to cover valid associated results |
SIMILARITY_THRESHOLD | 0.75–0.85 | Need to filter irrelevant financing projects with low similarity, while retaining financing association results of enterprises in the same track |
DATA_DEDUPLICATION | Deduplicate by disclosure date + enterprise name + financing amount | The same financing project may be disclosed multiple times, avoiding redundant index entries caused by duplicate indexing |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and testing on applicable samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The knowledge base retrieval response time exceeds expectations, and retrieval speed is faster under the same configuration in other scenarios. Cause:
INDEX_CHUNK_SIZEis not optimized for short text documents, resulting in too many index entries and excessive vector similarity calculation volume. - Phenomenon: The dataset status remains "Indexing" for a long time and cannot switch to "Ready". Cause: The automatic retry mechanism for incremental synchronization tasks is not configured. After the incremental synchronization is interrupted due to network fluctuations, it does not automatically recover, causing the indexing process to block.
- Phenomenon: After configuring
text-embedding-3-large, the indexing task gets stuck with no response, and restarting the service after commenting out the model does not resolve the issue. Cause: The request timeout parameter for the vector model is not set. After the model call times out, no retry mechanism is triggered, causing the process to remain blocked.
How to Verify Correct Configuration
- Check the running logs of the indexing task to confirm that the incremental synchronization task is triggered automatically daily, with no consecutive call error records.
- Perform a single retrieval test, verify that the returned result fields include the preset structured fields, and the similarity results fall within the preset range.
- Check the number of index entries in the dataset to confirm there is no abnormal growth, and duplicate financing project data has been filtered.
- Check the monitoring indicators of vector model calls to confirm that the request success rate meets the preset qualified standards.
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.