What this category of data looks like
Financial sector software development intelligent due diligence report data is primarily sourced from code repository commit records, requirement specification documents, test defect reports, code review records, and project delivery archive files for corresponding projects. Data update rhythm follows project iterations. Bulk updates occur after single project delivery or quarterly compliance checks, while incremental updates are triggered by daily code merges. A single report document structure includes fields such as basic project information, code module list, third-party dependency list, defect statistics, compliance check items, and historical change logs. Specific fields include commit hash values, lines of code, dependency package version numbers, defect level codes, and more. Units are mostly lines, count, and version number identifiers.
Constraints imposed by these characteristics on vector models and indexing
Software development intelligent due diligence report data contains mixed formats including code snippets, structured dependency lists, and unstructured review text. This requires vector models to adapt to mixed input of code and natural text, to avoid vector representation bias. Two data update scenarios exist: bulk and incremental. Indexes must support incremental writes based on change time or commit hash, to reduce resource consumption from full index rebuilding. A single report contains multiple independent module contents. When splitting into chunks, module boundaries must be preserved to avoid vector confusion across modules. Metadata includes structured identifiers such as commit ID and defect level, which must be embedded into index metadata fields simultaneously to facilitate subsequent traceability and filtering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed length of code snippets and document text in a single due diligence report, avoids cross-module chunking |
embedding_model | Code-specific embedding model + general embedding model | Covers different representation needs for code snippets and natural text in reports |
embedding_batch_size | 32–64 items per batch | Adapts to the long length of code text, balances vector generation speed and memory usage |
recall_top_k | Top 8–12 results | Matches the recall needs of multi-module due diligence reports, avoids missing key code or compliance items |
similarity_threshold | 0.72–0.80 | Filters low-correlation code snippets and document content, improves recall accuracy |
index_incremental_mode | Triggered by commit_timestamp | Adapts to incremental update scenarios, only synchronizes updated report content |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. Testing on available samples is recommended before finalizing configuration settings.
Three Common Misconfigurations
- Phenomenon: After uploading a due diligence report using the chunk-mode pushdata API, the interface remains in the "Indexing" state for a long time with no progress updates. Cause:
index_incremental_modeis not configured as incremental mode, or thecommit_timestampfield in metadata is not correctly specified, triggering full index rebuilding which causes a blockage. - Phenomenon: All search results have very low correlation and cannot match the queried code module or compliance item. Cause: A general embedding model is used to process code snippets, and a code-specific model is not used to generate vectors, resulting in representation bias.
- Phenomenon: The last chunk of a single due diligence report fails to complete indexing, and the log returns a
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not adjusted to adapt to the upload size of long document chunks, and the chunk data exceeds the interface limit.
How to Confirm Configurations Are Set Correctly
- Check the vector model configuration items, confirm that the embedding model associated with code snippets and the general text model have been specified separately.
- Check the index mode configuration, confirm that the incremental index trigger field matches the
commit_timestampor change time field in the report. - Upload a single test due diligence report, check the chunking log, confirm that chunk boundaries do not cross code modules or document chapters.
- Initiate a test query for a code module or compliance item, verify that the metadata of the recall results includes the corresponding fields in the report.
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.