Vector Models and Indexing for Securities Intelligent Due

Data Characteristics of This Category

Securities intelligent due diligence report data is mainly sourced from periodic reports of listed companies, temporary regulatory disclosure documents, special brokerage research reports, and due diligence working papers provided by enterprises. Update cadence falls into two categories: periodic and real-time. Periodic reports are released quarterly and annually. Temporary announcements and regulatory inquiry responses are updated as events occur. Document structures include both structured fields and unstructured long text. Standardized fields such as "due diligence entity name", "disclosure date", and "core risk points" are included, alongside long text content such as business descriptions and financial detail notes. Some data is submitted in Excel format, containing multi-row and multi-column financial indicators and corresponding unit information.

Constraints Imposed on Vector Models and Indexing

The mixed structure and professional attributes of securities due diligence data create multiple constraints for the vector models and indexing workflow. First, structured financial data tied to units. If fields and units are split during chunking, vector matching accuracy will be distorted. Second, long-text business descriptions and notes must retain complete semantics. Otherwise, risk point information required for due diligence cannot be covered. Third, batch imports of real-time updated temporary announcements and periodic reports require indexes to support incremental updates and batch processing. Fourth, text dense with professional terminology requires an embedding model adapted to the financial vertical domain. Otherwise, core due diligence content cannot be accurately matched.

Configuration Guidelines

Configuration Item	Recommended Setting	Rationale
`embedding_model`	Qwen/Qwen3-Embedding-8B or same-scale financial vertical embedding models	Adapts to professional terminology and financial expressions in securities due diligence, improving semantic matching accuracy
`chunk_size`	800–1200 characters	Covers continuous business descriptions and financial notes in securities due diligence texts, retaining complete semantics of single logical units
`chunk_overlap`	100–150 characters	Connects context between adjacent chunks, avoiding loss of cross-block professional expression associations after long text chunking
`recall_top_k`	Top 10–15 results	Balances recall coverage and computational cost, covering multi-dimensional risk point information required for due diligence
`similarity_threshold`	0.72–0.78	Distinguishes semantic similarity of professional terminology, filtering non-core due diligence-related text
`rerank_model`	Same-series vertical reranking models	Performs secondary ranking on recall results, improving matching priority of core due diligence content

The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

Phenomenon: When configuring embedding_model, repeatedly adding the same model overwrites the existing configuration, leading to calls to the incorrect embedding model after deployment. Cause: No independent model instance is bound for the due diligence scenario on the platform configuration page, only the globally unique model configuration item is used.
Phenomenon: After importing Excel-format due diligence detail data, the knowledge base chunks are too large. A single chunk's text exceeds the token limit of the embedding model, and an error token limit exceeded is thrown during vector generation. Cause: The chunk_size parameter is not adjusted based on the row data granularity of securities due diligence Excel files, and the default chunk length is used directly.
Phenomenon: After public network deployment, markdown first- and second-level headings in recall results display abnormally, with disrupted formatting. Cause: Title hierarchy context of due diligence reports is not retained during chunking, leading to failure to restore the original document's structural information after vector matching.

How to Verify Successful Configuration

Upload a single typical securities due diligence report document, check the chunk preview interface, and confirm that chunk length falls within the preset chunk_size range, and that core financial fields and units are not split.
Call the embedding model test interface, input a single piece of due diligence professional text, and check that the returned vector dimension matches the output dimension marked by the model.
Simulate batch import of Excel due diligence data, check the index construction log, and confirm that no token limit exceeded errors are present.
Manually input a target professional term, and check that the similarity score of recall results falls within the preset similarity_threshold 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.