What the data for this category looks like
The data for professional services intelligent due diligence reports comes primarily from publicly disclosed corporate documents, industrial and commercial registration archives, regulatory penalty announcements, and transcribed due diligence interview recordings. Update frequency follows client-commissioned project cycles or changes in public information. Standard commissioned reports are updated according to project progress, while public information data is synchronized in real time with regulatory announcements. Document structures include structured headers (client, due diligence target, report date), multi-chapter compliance analysis, and risk rating modules. Fields include unified social credit codes, revenue amounts (unit: ten thousand yuan), penalty counts, compliance scores, and some long-form risk description text.
Constraints on Vector Models and Indexing
There are many structured fields with clear units, so structured and unstructured chunking logic must be differentiated to avoid incorrect unit associations. Long-form risk descriptions account for a large share of content, so long-text chunking strategies must be adapted to prevent truncation of core risk information. Data update frequency varies with project progress and public information, so incremental indexing mode must be supported to reduce resource consumption from full indexing. The chapter structure of different due diligence reports varies significantly, so custom chunking rules must be configured to match chapter boundaries. Enumerated fields (such as penalty types, compliance levels) must be associated with metadata indexes to improve retrieval precision.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length of long-form risk descriptions in due diligence reports, retaining key information with contextual relevance |
similarity_threshold | 0.72–0.85 | Filters low-correlation due diligence fragments, avoiding retrieval of compliance information for non-target enterprises |
top_k | Top 8–12 entries | Covers the number of core risk modules in a single due diligence report, balancing retrieval completeness and efficiency |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing time for multi-chapter due diligence reports, preventing interruptions during long document parsing |
enable_incremental_index | Enabled | Adapts to the update rhythm of due diligence reports based on project progress or public information, reducing resource usage from full indexing |
rerank_top_k | Top 3–5 entries | Focuses on the most relevant due diligence risk fragments, improving the precision of final outputs |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Individual scenarios require tailored analysis, and testing on in-house samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Scenario: After upgrading the platform version, previously parsable due diligence report CSV files trigger parsing errors, with the
PARSE_FAILEDstatus code displayed in the interface. Cause: The new version optimizes metadata verification rules for chunking logic, and the legacychunk_sizeconfiguration does not adapt to the new field length limits. - Scenario: The indexing task for the due diligence report knowledge base gets stuck in the "processing" state with no progress updates, and backend logs return the
TASK_STUCKstatus code. Cause: No reasonable value is configured forPARSE_FILE_TIMEOUT_SECONDS, and parsing timeouts for long-chapter due diligence reports do not trigger automatic retries. - Scenario: After switching the vector model, the retrieval results of the due diligence report knowledge base do not change, and the new model's encoding logic cannot be matched. Cause: Existing due diligence report data is not re-indexed, and legacy vector encodings remain stored in the index library.
How to Verify Proper Configuration
- Upload a single typical due diligence report file, check the chunk length distribution in the chunk preview interface, and confirm that chunk lengths match the preset rules.
- Initiate an incremental indexing task, check the update status in the indexing progress panel, and confirm that only newly added or modified report data is processed.
- Enter a query term related to the core content of the due diligence report, check the source and metadata tags of the retrieval results, and confirm that the association logic complies with configuration requirements.
- After switching the vector model, re-index all historical reports, and verify that the new model's encoding logic takes effect.
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.