What the Data for This Category Looks Like
Data sources for general comprehensive intelligent due diligence reports include public industrial and commercial disclosure information, industry compliance disclosure documents, cross-departmental business ledgers, and project-related documents. Update rhythm is adjusted according to the corresponding project cycle, with no fixed frequency. Document structures contain both structured fields (such as report number, subject unified social credit code, risk level) and unstructured paragraphs (such as risk investigation details, business transaction descriptions). Fields include character-based identifiers and numeric scores, and the length of individual documents varies widely.
What Constraints These Characteristics Impose on Vector Models and Indexing
The mixed structured and unstructured document structure requires the vector encoding process to adapt to splitting logic, avoiding excessive splitting of structured fields or loss of associated semantics. No fixed update frequency and wide variation in individual document lengths require indexes to support dynamic incremental updates and flexible shard threshold configuration, preventing processing timeouts for large documents or index redundancy from small documents. Differences in data formats across sources require preset unified field mapping rules to avoid field semantic drift during vector encoding. Subject identification fields involved need separate entity vector anchoring to improve retrieval accuracy.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_MAX_SIZE | 800–1200 characters | Adapts to the splitting needs of mixed-structure documents, balances semantic integrity and the number of shards, and avoids encoding deviations caused by overly long single shards or index redundancy caused by overly short shards |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the maximum length of most individual general comprehensive intelligent due diligence reports, preventing upload interruptions caused by oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing and vectorization time required for large due diligence reports, avoiding timeout interruptions mid-process |
INDEX_INCREMENTAL_UPDATE | Enabled | Adapts to the document characteristics of no fixed update frequency, only re-encodes modified shards, reducing resource consumption for index reconstruction |
VECTOR_SIMILARITY_THRESHOLD | 0.72–0.80 | Filters low-relevance search results, adapting to the semantic matching accuracy requirements for risk association in due diligence reports |
RECALL_TOP_K | Top 8–12 results | Covers the search requirements for multi-dimensional risk association in due diligence reports, avoiding missing key information due to insufficient recall |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and testing on the reader's own samples is recommended before finalizing settings.
Three Common Mistakes
- Symptom: When uploading due diligence report files larger than 10 MB, partial chunk vectorization fails after the number of shards exceeds 1000, with some recoveries or persistent abnormalities after retries. Cause: The matching configuration between
CHUNK_MAX_SIZEand shard thresholds was not adjusted, leading to fragmented semantics of single shards or a number of shards exceeding the concurrency limit of the vector encoding queue. - Symptom: After the server restarts due to overload from multiple file uploads, the existing knowledge base is in a not-ready state, cannot automatically trigger index reconstruction, and the interface shows stagnation without progress. Cause: The automatic recovery configuration for
INDEX_INCREMENTAL_UPDATEwas not enabled, and the system did not detect index status abnormalities or trigger a rerun logic. - Symptom: After upgrading to version 4.9–4.10, the existing due diligence report knowledge base cannot return vector search results. Cause: Field mapping rules for the vector model were changed during version iteration, and the field vector encoding of the existing index is incompatible with the current version, and a full reindex was not performed.
How to Confirm Configuration Is Correct
- Upload a general comprehensive intelligent due diligence report of typical length, verify that the number of shards matches the calculated result of
CHUNK_MAX_SIZE, and confirm no excessive splitting or missing structured fields. - Trigger a large file upload, check for timeout errors related to
PARSE_FILE_TIMEOUT_SECONDSin system logs, and confirm that the timeout configuration adapts to document processing time. - Perform an incremental update operation, verify that only modified shards are re-encoded and the full index is not triggered repeatedly, and confirm that the incremental update configuration is effective.
- Enter search terms related to risk descriptions in the report, review the similarity matching logic of returned results, and adjust the threshold to a reasonable range.
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.