What this type of data looks like
Data primarily comes from regular and thematic research reports produced by internal investment research teams, plus third-party industry research reports obtained through compliant channels. Update cadence includes monthly regular updates, quarterly deep research report updates, and ad-hoc thematic reports released on an as-needed basis. Document structure includes publication identifiers, industry classifications, core data tables, rating conclusions, and risk warning modules. Fields include report unique identifiers, publication dates, relevant track codes, target price (unit: RMB yuan), rating tags, and other related fields.
Constraints on model access and configuration from these characteristics
Research reports contain structured numerical fields with units. Parameters supporting unit recognition and structured extraction must be configured during model access. Documents from multiple sources with different update schedules require recall filtering rules based on publication date and source type. Single research reports have long length, so context window parameters must be adjusted to accommodate long text parsing. Internal and third-party reports in different formats require segmentation and parsing parameters that adapt to multiple document structures, to avoid missing parsed fields.
How to set configuration values
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single research reports have long length, need to cover complete core analysis content to avoid context truncation and loss of critical information |
recallTopK | Top 8–12 results | Joint-stock bank research reports cover multiple industry segments, so precise recall of content relevant to the query topic is required |
similarityThreshold | 0.75–0.85 | Balance recall precision and coverage, avoid irrelevant reports being included in retrieval results while covering core relevant documents |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Research reports contain multiple tables and long text paragraphs, parsing takes longer time, so sufficient parsing time must be reserved |
apiFormat | Follow OpenAI base model format | Most financial domain specialized models adapt to this format, reducing adaptation costs for model access |
enableStructuredExtract | Enabled | Research reports contain structured fields such as target price and rating, so extraction must be enabled to support precise Q&A |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Model test returns
404 status code (no body). Cause: API address or key permissions for model access are not configured correctly, causing requests to fail to reach the target model service. - Phenomenon: Model calls return format errors after setting
apiFormatto a non-base model format. Cause: The API format supported by the actual model is not matched, causing the request body to not meet the model's requirements. - Phenomenon: After deploying a model locally, it is impossible to generate answers based on parsed research reports, and the error
Message field is requiredis returned. Cause: Associated fields for context recall are not configured correctly, causing the model to not obtain parsed document content, or required context parameters are missing from the request.
How to confirm the configuration is complete
- Call the model test interface, check that the returned status code is
200to confirm that the API address and key configuration are correct. - Upload a single standard research report, check that the parsed document fields include preset report identifiers, target price, and other content to confirm that the parsing configuration adapts to the research report structure.
- Initiate a research report retrieval Q&A request, check that the recall results include reports matching the query topic to confirm that the recall rules and threshold configuration meet requirements.
- Verify the structured extraction function, check that the generated answer correctly extracts fields such as target price and rating to confirm that the extraction configuration has taken effect.
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.