What the data for this use case looks like
Aquaculture research reports used for industry analysis by financial institutions draw data from multiple sources: the National Fisheries Technology Extension Station, local aquaculture monitoring stations, on-site survey data from feed manufacturers, and monthly updates released by industry associations. Update schedules include fixed-cycle monthly monitoring reports, quarterly industry analyses, and immediate notifications for sudden disease outbreaks or price fluctuations. Document formats include structured aquaculture data tables, text analyses from on-site surveys, and some reports include water quality monitoring images and aquaculture site videos. Fields covered include breeding area, species, yield per unit, feed usage, and market transaction price. Supported units include mu, kg, yuan/kg, ton, and others.
Constraints on Model Access and Configuration Imposed by These Data Characteristics
Aquaculture research report retrieval in financial scenarios requires meeting both accuracy and compliance requirements. Multiple data sources require configuring multi-source data fusion recall rules during model access. This ensures uniform processing of data from different channels and avoids data bias. The combination of fixed and real-time update cycles requires configuring dynamic recall trigger conditions. This adapts to rapid retrieval needs for sudden notifications and meets the timeliness requirements of financial analysis. The mixed structure of structured tables and unstructured analyses requires the model to support both structured data extraction and natural language interpretation. This covers all content of research reports. The diversity of field units requires configuring parameters to perform unit normalization preprocessing. This avoids unit confusion during model recognition that affects the accuracy of analysis results.
How to Configure Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recallTopK | Top 6-8 entries | Individual aquaculture research reports contain substantial content. Recalling too many entries will exceed the model's context window, while recalling too few will fail to cover accurate data for targeted aquaculture scenarios. |
similarityThreshold | 0.72-0.78 | Aquaculture research reports are dense with specialized terminology. A threshold that is too low will introduce irrelevant general agricultural content, while a threshold that is too high will make it difficult to recall accurate research reports for specific categories. |
maxContext | 8000-12000 characters | Individual research reports often contain multiple pages of structured tables and analytical paragraphs. Sufficient context must be reserved for the model to integrate and interpret specialized content. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing image and video files attached to large monitoring reports takes a long time. This avoids file parsing failures due to timeout. |
responseFormat | json | When integrating into business systems, structured output of retrieval results is required to facilitate subsequent data processing and display. |
model | qwen-max or qwen-vl-max | This supports both structured data extraction and interpretation of multimodal research report content such as monitoring videos and images. |
The parameter values provided on this page are conventional recommendations used as a starting point 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 the configuration.
Three Common Configuration Mistakes
- Symptom: The problem classification workflow released via the API returns chaotic results, with retrieved research reports unrelated to the query. Cause: The task type bound to the
modelparameter was not specified, and the research report question-answering model was used in the problem classification link, leading to misplaced task logic. - Symptom: When using the
qwen-vlmodel in FastGPT v4.9.6, uploading an aquaculture monitoring MP4 file results in a recognition failure returned by the model. Cause: TheUPLOAD_FILE_MAX_SIZEand file parsing timeout parameters were not adjusted, and the generated long URL exceeded the character length limit supported by the model. - Symptom: After configuring the
responseFormatparameter, the interface still returns plain text. Cause: The forced response format validation function for the corresponding version was not enabled, or the parameter configuration did not cover the format requirements for all returned fields.
How to Verify Successful Configuration
- Call the test API, pass a standardized aquaculture professional question, and verify whether the returned result fields match the structured data in the research report.
- Upload a single aquaculture monitoring MP4 file, check whether the analysis results returned by the model include key monitoring indicators from the video, and confirm there are no errors related to URL length.
- View the number of retrieved results, confirm that it matches the value set for
recallTopK, and adjust the similarity threshold to optimize result relevance. - Verify the interface return format of the release channel, confirm that it matches the configured
responseFormat, and ensure normal integration with existing business systems.
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.