Data Characteristics for This Category
Securities research knowledge base data primarily comes from public research reports, exchange disclosure announcements, listed company periodic financial reports, industry index data, and real-time market information. Research reports include professional valuation models, risk warnings, and industry analysis. Announcements have uniform document numbers and disclosure time fields. Financial reports contain structured financial statement data, with units including yuan, ten thousand yuan, and hundred million yuan.
Update rhythms vary significantly: real-time market data and temporary announcements are updated immediately, industry research reports are updated on workdays, and periodic financial reports are released on a fixed quarterly and annual basis. Document formats cover structured tables and unstructured long texts, with some research reports reaching tens of thousands of words per piece.
Constraints Imposed on Vector Models and Indexing
The mixed multi-source data and varying update rhythms require vector models to adapt to both structured financial fields and unstructured professional text. The high proportion of long documents requires chunking strategies that avoid splitting professional semantic units. The immediate update needs for real-time market and temporary announcements require support for incremental indexing. Full reindexing cannot guarantee data freshness.
Financial data from different sources has unit differences. The indexing link must complete unified processing to avoid dimensional bias in vector encoding. Additionally, the research scenario has high requirements for information relevance, so low-quality recall results must be strictly filtered.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Use a general vector model fine-tuned for the financial domain | Adapt to securities industry terminology and improve accuracy of long-text semantic encoding |
chunk_size | 800–1200 characters | Match the typical paragraph length of securities research reports and financial reports, avoid truncating core analysis content |
chunk_overlap | 100–150 characters | Retain contextual association between chunks, prevent key logic from breaking at chunk boundaries |
retrieve_top_k | Top 10–15 results | Balance retrieval efficiency and information comprehensiveness, meet the needs of multi-dimensional cross-verification for research work |
similarity_threshold | 0.75–0.85 | Filter low-correlation content, meet the professional rigor requirements of securities information |
index_refresh_strategy | Real-time incremental refresh (market/announcements) + daily full refresh (research reports/financial reports) | Adapt to the update rhythms of different data types, balance indexing performance and data freshness |
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 individual analysis. Testing on relevant local samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: A large number of irrelevant non-research content is returned during retrieval, with extremely low relevance scores. Cause: A financial-domain fine-tuned vector model was not used. A general model cannot accurately recognize securities-specific terminology, leading to semantic encoding deviation.
- Phenomenon: Indexing tasks remain stuck on the final batch with no progress updates. Cause: A reasonable
chunk_overlapparameter was not set, leading to repeated loops during long-document chunking, or the shard size was not adjusted for large-volume financial reports, resulting in indexing timeouts. - Phenomenon: Real-time updated exchange announcements cannot be queried in the index. Cause: Only a full index refresh strategy was configured, and the incremental indexing function was not enabled, so newly released temporary announcement data cannot be synchronized.
How to Verify Correct Configuration
- Upload a listed company research report with over 10,000 words per piece. Review the parsed chunk list to confirm each chunk length falls within the 800–1200 character range.
- Input a professional research question, such as "The third-quarter brokerage business revenue proportion of a listed securities firm". Review the number of recall results and similarity scores to confirm the result count matches the
retrieve_top_kconfiguration, and that scores exceed thesimilarity_thresholdsetting. - Publish a temporary exchange announcement. Wait for indexing to complete, then perform a retrieval. Confirm the announcement content can be recalled normally, and verify the incremental indexing function is active.
- Review vector model call logs to confirm the financial fine-tuned model is only used for securities-related documents, and general vector models are not mixed, to avoid semantic encoding deviation.
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.