Data Characteristics
Data sources for comprehensive service intelligent due diligence reports cover multiple channels including industrial and commercial public disclosure systems, public financial reports, industry regulatory announcements, and due diligence workpapers submitted by partners. Supported file types include common formats such as PDF, Word documents, Excel spreadsheets, CSV reports, and HTML announcements. The update rhythm centers on quarterly financial report updates, supplemented by real-time synchronization of regulatory policy changes. Document structure includes structured metadata fields such as the main body’s unified social credit code, establishment date, and registered capital, as well as unstructured body text including due diligence process descriptions, risk inspection items, and compliance conclusions. Amount fields uniformly use ten thousand yuan as the unit, and date fields follow the ISO 8601 format specification.
Constraints for Vector Models and Indexing
Multi-source heterogeneous data formats require the indexing system to support hybrid indexing of structured metadata and unstructured body text, to avoid semantic confusion between fields. Documents with mixed structures need to adapt to chunking strategies, ensuring that metadata information of structured fields is not split and lost, while preserving the contextual coherence of unstructured body text. Real-time synchronization update demands require the index to support incremental update mechanisms, avoiding performance losses caused by full reconstruction. Long body text content needs to match long-text capable vector models, preventing semantic truncation of key risk inspection items.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Adapts to the file size often seen in comprehensive service due diligence reports, which frequently include multiple pages of workpapers and attachments |
PARSE_CHUNK_SIZE | 1000–1200 characters | Balances contextual coherence of unstructured body text and the integrity of structured metadata without unintended splitting |
RECALL_TOP_K | Top 8 entries | Covers multi-dimensional risk inspection items required for due diligence, ensuring sufficient coverage of recall results |
EMBEDDING_MODEL | text-embedding-3-large | Supports long-text vector generation, preventing semantic truncation of core content in due diligence reports |
INDEX_INCREMENTAL_SYNC | Enabled | Meets real-time synchronization requirements for regulatory policy changes, reducing performance losses from full index reconstruction |
SIMILARITY_THRESHOLD | 0.75 | Filters low-correlation historical due diligence data, improving accurate matching efficiency |
The parameter values provided on this page are common recommended starting points for configuration setup. 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 settings.
Three Common Configuration Errors
- Phenomenon: The knowledge base creation process gets stuck at the build index step, with no error logs or only a timeout status code returned. Cause: The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted, and large due diligence report attachments exceed the system's default upload limit, causing index construction to interrupt. - Phenomenon: After configuring a new embedding model, the index can be generated normally, but search tests return a
500 Internal Server Error. Cause: The vector database dimension configuration was not updated synchronously, and the vector dimension of the new model does not match the preset dimension of the database. - Phenomenon: Only vector fields are visible when querying the vector database, and original document content cannot be retrieved. Cause: The
SAVE_ORIGINAL_DOCUMENTconfiguration item was not enabled, and the original due diligence report text was not persistently stored, only the vector index was retained.
Verify Correct Configuration
- A single large test due diligence report is uploaded, and the upload and index construction processes are confirmed to run without stalling.
- After configuring a new embedding model, test vectors are generated and compared with the vector database's dimension configuration to ensure the two match.
- A search test is run to verify that the number of recall results matches the value set in the preset configuration.
- Storage records in the vector database are viewed, confirming that both the original document text and vector index fields exist.
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.