Data Characteristics for This Use Case
Data sources include industry analysis documents produced by internal research teams, periodic reports and temporary announcements publicly disclosed by listed entities, macroeconomic monitoring data, policy documents released by regulatory authorities, and externally published industry research materials. Internal documents are updated according to research project cycles. Public data is updated based on disclosure timelines (quarterly, monthly, real-time policy releases). Some high-frequency monitoring data is updated daily. Most documents are long-text analysis reports and structured financial and risk indicator tables. Fields include report title, issuing institution, release time, core conclusions, and data support items. Common units used in the financial industry include 100 million yuan, basis points, and percentage.
Constraints for Vector Models and Indexing
These characteristics create constraints for the vector model and indexing workflow:
- Long-text analysis reports account for a large share of total data. Use segmentation rules optimized for long texts to avoid semantic breaks that cause invalid vector associations.
- Structured financial and risk tables account for a notable share of total data. Support structured data vectorization to preserve field association information within tables, preventing loss from plain-text vectorization.
- Update cycles vary significantly across different data sources. Support flexible switching between incremental indexing and full indexing to adapt to different update periods for internal project documents and publicly disclosed data.
- Financial indicators are tightly bound to their units and associated fields. Preserve field association logic during vector recall to avoid mismatches of unrelated indicators.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Adapts to the semantic integrity of joint-stock bank research reports, avoids excessive vector computation redundancy caused by overly long segments |
segment_overlap_characters | 50–100 characters | Connects semantics of adjacent segments, prevents context breaks after long document segmentation |
recall_count | 10–15 entries | Covers multi-dimensional research data requirements, avoids insufficient information from too few results, and reduces noise from too many results |
similarity_threshold | 0.75–0.85 | Filters low-correlation search results, meets the strict data requirements of the financial industry |
incremental_indexing_toggle | Enabled | Adapts to update cycles of different data sources, reduces repeated computation costs of full indexing |
structured_data_vectorization_toggle | Enabled | Preserves field association information for financial tables and risk indicators in research documents, prevents semantic loss of structured content |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: The vector model dropdown list fails to display target models such as the latest ZhiPu Embedding-3 and CharGLM-4, and configuration cannot be completed. Cause: A compatible model proxy interface is not configured, or the interface call token is not bound to access permissions for the corresponding models.
- Phenomenon: In a local deployment environment, the vector model interface can be pinged successfully, and the token configuration is correct, but a connection failure error is returned during knowledge base import. Cause: The model port is not correctly mapped in the Docker container network, or token parameters are not correctly loaded in the container’s environment variables.
- Phenomenon: After importing research documents into the knowledge base, search results do not include core conclusions or data support fields specified in custom settings. Cause: The structured data vectorization toggle is not enabled, or the segmentation rules fail to preserve association information for target fields.
How to Verify Proper Configuration
- Upload a test research report, confirm that the vector generation progress bar completes normally without timeout errors.
- Initiate a retrieval test, verify that the number of recalled results matches the configured
recall_countparameter. - Check the similarity scores of search results, confirm that the scores fall within the preset threshold interval.
- Review search results for structured table documents, confirm that field association information within tables is included.
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.