What the data for this category looks like
Financial report data for professional services scenarios is primarily sourced from periodic reports, temporary announcements, and audit reports of listed companies publicly disclosed by domestic and overseas stock exchanges. Data updates mainly follow a quarterly periodic report schedule, supplemented by irregular updates triggered by temporary announcements. A single document typically includes structured financial tables, unstructured management analysis and notes, with fields covering attributable net profit, asset-liability ratio, earnings per share, and others. Units are mostly ten thousand yuan or hundred million yuan, and some segmented fields require explanations aligned with the reporting period specifications.
What constraints these characteristics impose on multi-turn dialogue and prompting
The mixed structured and unstructured nature of financial report data requires multi-turn dialogue to clearly distinguish between field query and text analysis request types. Prompts must restrict the model to only use content from uploaded financial report files. The update rhythm of multiple reporting periods requires the dialogue flow to actively confirm the reporting period specified in user queries, to avoid confusion from cross-period data. The characteristics of long documents and dense professional fields require limiting the effective length of multi-turn context. Prompts must clearly specify the units and specifications of returned fields, preventing the model from referencing external irrelevant data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | A single complete financial report file often contains thousands to tens of thousands of characters, requiring sufficient context to support multi-turn follow-up questions and context association |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Financial report files contain multi-page tables and long text notes, with a relatively long parsing process, requiring sufficient processing time reserved |
Recall Count | Top 3–5 entries | Financial report fields are dense and highly professional; excessive recall will lead to redundant context and affect the accuracy of model responses |
Similarity Threshold | 0.75–0.85 | Precise matching of professional field names and values in financial reports is required to avoid interference from irrelevant content with core queries |
UPLOAD_FILE_MAX_SIZE | 50 MB | Single PDF or Excel format financial report files usually do not exceed this size, adapting to most business scenarios |
Segment Length | 1000–1500 characters | Financial report paragraphs have clear structure; segmenting at this length facilitates the model to accurately locate target content and analysis modules |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test against your own samples before finalizing.
Three common errors
- Phenomenon: Prompts fail to correctly recognize field combination names separated by spaces in financial report data. Cause: The prompt does not explicitly require retaining the original text's space format, or does not limit the model to generate responses only based on uploaded financial report files.
- Phenomenon: Multi-turn dialogue context is not retained when accessing the application through the API release channel. Cause: The
historyparameter or unique session identifier is not correctly carried in the API request body, resulting in failure to persist session state. - Phenomenon: Conversation logs are lost and cannot be recovered through Docker configuration. Cause: The
CHAT_LOG_PERSISTparameter is not configured astrueindocker-compose.yml, or a persistent log volume is not mounted, resulting in logs being cleared after container restart.
How to confirm the configuration is properly set
- Upload a single quarterly financial report file, initiate a query containing professional field names separated by spaces, and verify whether the reply accurately extracts the corresponding data.
- Conduct an API call test, carry the
historyparameter to initiate multiple consecutive questions, and verify whether subsequent replies are associated with query content from historical sessions. - View the log directory mounted by the Docker container, confirm that conversation log files are generated at fixed intervals and are not automatically cleared.
- Adjust the similarity threshold to initiate a fuzzy query, and verify whether the matching accuracy of returned content meets the requirements of the business scenario.
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.