What this type of data looks like
Investment platform research data comes from three primary sources: third-party industry data APIs, publicly disclosed documents of listed companies, and original research reports from professional institutions. Update frequency varies by content type:
- Institutional research reports update in real time on workdays
- Financial reports update in concentrated batches quarterly or annually
- Industry indicator data syncs daily
Individual document lengths vary significantly. Some are short industry commentaries with only a few hundred characters. Others are in-depth research reports spanning tens of thousands of characters. Standardized document fields include publishing institution, publish time, main body content, and core viewpoint tags. Field units follow uniform standards: character count for text length, standard date format for timestamps, and classification tags for categorization.
Constraints on vector models and indexing
The wide range of individual document lengths requires an adaptive segmentation strategy. This strategy avoids truncating long texts and losing core logic, and reduces unnecessary splits of short texts. Mixed updates across multiple content types require indexes to support incremental synchronization. This adapts to the different update rhythms of real-time research reports and batch financial reports, cutting resource use from full index rebuilds. High distinctiveness in standardized fields requires explicit specification of the target field for vector generation. This prevents embeddings from non-core fields such as publishing institution from interfering with recall accuracy. Dense professional terminology in the domain requires vector models optimized for financial investment research scenarios. This ensures accurate vector mapping of professional expressions.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed length of in-depth research reports and industry commentaries. Avoids single segments being too long for model context limits, while reducing redundant splits of short texts |
chunk_overlap | 50–100 characters | Retains contextual continuity between segments, preventing professional terms from being split across paragraphs and causing semantic breaks |
vector_model | Vector model optimized for the financial domain | Optimized for professional terminology in investment research scenarios, improving the accuracy of vector mapping for professional expressions |
retrieval_top_k | 10–15 results | Covers multi-source reference information required for investment decisions, avoiding missing key research reports or financial data due to too few recall results |
similarity_threshold | 0.75–0.85 | Filters low-relevance recall results, while retaining reference space for similar viewpoints across different institutional research reports |
index_refresh_interval | Tiered by content type: real-time data every 10 minutes, batch data every 1 hour | Adapts to different update rhythms of real-time research reports and batch financial reports, balancing index performance and data freshness |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Index construction takes excessively long, and background logs return
ETIMEDOUTorindex_process_timeouterrors. Cause: Thechunk_sizeparameter was not adjusted based on document length. Long-text research reports consume excessive computing resources during segmentation processing, and incremental index mode was not enabled. - Phenomenon: The configured vector model does not appear in the text understanding model dropdown menu when creating a knowledge base. Cause: The deployment port of the vector model was not added to the service network whitelist, or the embedding task was not set to use this model in the knowledge base configuration.
- Phenomenon: Recall results from the vectorized dataset have significant semantic deviation from the original investment research documents. Cause: Vector embeddings were not generated exclusively for the main body content field, causing embeddings from non-core fields such as publishing institution to interfere with recall accuracy.
How to Verify Proper Configuration
- Upload a test in-depth research report and a test industry commentary. Check that the length of segmented text blocks falls within the set
chunk_sizerange, with no obvious truncation or excessive splitting. - Run a manual index refresh task. Check the backlog and processing time of background task queues, confirming there are no continuously timed-out index processes.
- Enter investment research-related keywords in the knowledge base test query bar. Verify that the number of recall results matches the
retrieval_top_ksetting, and that result relevance aligns with thesimilarity_thresholdrule. - Check the vector model status in the model channel. Confirm the model is linked to the current knowledge base's embedding configuration, with no inactive or unlicensed model entries.
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.