What the Data for This Category Looks Like
Seasoning industry investment research data comes from four main sources: public research reports from securities firms, public reports from industry associations, periodic reports of listed companies, and retail terminal monitoring databases. Update frequency varies by source:
- Securities firm reports are released irregularly alongside industry developments
- Industry association data is updated monthly
- Listed company financial reports are disclosed quarterly
Document structure centers on three core focus areas: cross-category horizontal comparisons, longitudinal tracking of individual enterprises, and upstream-downstream supply chain association analysis. It includes fields such as product category, ex-factory unit price, monthly sales volume, raw material procurement costs, and channel type. Units include physical measurement units like yuan/ton, kilogram, milliliter, and monetary units. Single research report or dataset typically contains multiple pages of structured tables and textual analysis, with strong content relevance.
Constraints on Context and Token Workflows
The multi-category details and cross-dimensional association properties of seasoning investment research data require including more associated information during context stitching. This easily exceeds standard token limits. Structured tables and long-form analysis included in single research reports can break data relevance if segmented improperly, increasing invalid token consumption. Joint recall of high-frequency retail monitoring data and cross-quarter financial reports further increases pressure on the context window. This requires more precise pruning and recall strategies. Format differences across data sources also require preserving field relevance during context stitching. This avoids redundant information occupying token resources.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Seasoning investment research data includes multi-category details and supply chain association content. This range covers the stitching needs of conventional recall content and avoids context overflow |
chunkSize | 1000–1500 characters | Seasoning research reports often include structured tables and long-form analysis. This segment length preserves the integrity of single-category analysis and reduces context association breaks |
recallCount | Top 8–12 entries | Investment research scenarios require association of multi-dimensional data. This recall volume covers core associated information while avoiding excessive entries occupying tokens |
similarityScoreThreshold | 0.75–0.85 | Filters low-relevance redundant data, reduces invalid token consumption, while retaining precise matching results for segmented product categories |
rerankTopK | Top 4–6 entries | Retains core entries after secondary sorting of recall results, avoiding non-critical data occupying context tokens |
maxResponseToken | 2000–3000 token | Investment research responses require multi-dimensional analysis and data comparisons. This range supports complete conclusion output and avoids response truncation |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test against your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Model response content is truncated, with
...[hide 38432 chardisplayed at the end. Cause: ThemaxResponseTokenparameter was not adjusted to the range suitable for investment research analysis, resulting in insufficient generation token limit. - Symptom: The
failed to get gpt-3.5-turbo token encoderexception occurs when starting the associated service. Cause: Token encoding association parameters for the model were not configured correctly, or the token mapping file for the local model is missing. - Symptom: The application cannot access the preset context path. Cause: Reverse proxy path forwarding rules were not configured correctly during private deployment, causing the context path to not map to the FastGPT service port.
How to Verify Correct Configuration
- A typical seasoning research report document is uploaded. The number and length of system-segmented fragments are checked to confirm the
chunkSizesetting matches the document structure. - A multi-dimensional query related to investment research is initiated. The number of recalled context entries is checked to verify the
recallCountandrerankTopKsettings align with expected information dimensions. - A complete investment research analysis query is initiated. The completeness of the model response is checked to confirm no content truncation occurs, and the rationality of the
maxResponseTokenconfiguration is verified. - The configured context path is accessed. The service’s ability to respond to requests normally is confirmed to verify the correctness of the reverse proxy rules.
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.