What the data for this category looks like
Multi-financial intelligent due diligence report data sources include regulatory public disclosure documents, official financial reports of target entities, statistical reports from industry self-regulatory organizations, and subject profile data from third-party credit reporting agencies. Update rhythms vary across sources: regulatory disclosure documents are updated quarterly, target entity financial reports are updated semi-annually or annually, and third-party credit data is updated monthly. Documents mostly consist of structured tables paired with unstructured explanatory text, including fields such as unified social credit code, business qualification number, risk classification level, related party transaction amount, and regulatory penalty records. Field units include ten thousand yuan, person-times, level codes, and similar categories.
What constraints these characteristics impose on model access and configuration
Multi-source and heterogeneous data formats require integrated parsing modules to support mixed format parsing, preventing structured field loss from single-logic processing. Differences in update rhythms across data sources require vector index refresh strategies to match the update cycles of corresponding data sources, avoiding index data lag. Field types cover numerical, classification, text and other categories, requiring embedding models to support multi-type field vectorization, or configuration of field-level vectorization rules. The text length of a single due diligence report is typically long, requiring the model’s context window configuration to cover full input requirements.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | A single multi-financial due diligence report usually contains multiple pages of structured data and explanatory text, requiring coverage of full input needs |
EMBEDDING_BATCH_SIZE | 32–64 | Due diligence data has many fields, and single-batch processing volume adapts to general hardware resources to avoid memory overflow |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing needs of long text spliced from multiple sources, reserving sufficient processing time |
RECALL_TOP_N | Top 8–12 entries | Due diligence reports need to cover multi-dimensional information such as subject qualifications, related party transactions, and risk records, and appropriate recall ensures complete information |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-correlation unstructured explanatory text, retaining field data that strongly matches the due diligence theme |
FUNCTION_CALL_ENABLED | Enabled | Due diligence reports need to call tools such as structured field extraction and numerical verification to improve output accuracy |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration errors
- Phenomenon: A
Request Timeouterror occurs during conversation, with a status code of 504. Cause: The long text input of multi-financial due diligence reports is not adapted, and themaxContextconfiguration is too small, causing model processing timeout. - Phenomenon: Unable to call functions to complete structured field extraction, with the log showing
function call not supported. Cause: TheFUNCTION_CALL_ENABLEDconfiguration is not enabled, or the large model deployed via ollama does not properly adapt to the function call protocol, or the function call forwarding rule is not configured when accessing via oneapi. - Phenomenon: Recall results only contain a small number of unstructured text fragments, with no structured field data. Cause: Mixed parsing rules for multi-source data are not configured, only unstructured text is extracted, and key fields such as qualification numbers and transaction amounts are lost.
How to confirm successful configuration
- Upload a standard multi-financial due diligence report, check whether the parsed structured fields are fully extracted, and verify whether preset core fields are included.
- Initiate a query including field verification and numerical calculation, confirm that the model can trigger corresponding tool calls and return structured results.
- Adjust the similarity threshold parameter, observe the correlation changes of recall results, and confirm that the threshold configuration meets scene requirements.
- Simulate a multi-data source update scenario, check whether the refresh cycle of the vector index matches the update rhythm of the corresponding data source.
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.