What Data for This Category Looks Like
Aquaculture investment research data primarily originates from on-site aquaculture monitoring equipment, feeding management logs, seed breeding records, disease prevention archives, industry market reports, and national standard documents. Monitoring equipment data comprises minute-level updated structured time-series records, including metrics such as dissolved oxygen, water temperature, and pH value, with standardized units attached. Feeding and disease records are daily updated unstructured text or spreadsheet documents. Industry reports and standard documents are long-form materials updated quarterly or annually. The data includes both structured fields and unstructured descriptions, and field units vary based on aquaculture species and breeding mode.
What Constraints These Characteristics Impose on Context and Token Management
The multi-type and high-update-frequency characteristics of aquaculture data create multiple constraints for context and token management. Structured time-series data has short individual entry lengths but a large total volume. Without effective filtering, recalled irrelevant historical data will quickly consume token quotas. Long-form industry reports that are not properly segmented will exceed the model’s context window limit. Differences in field units across different aquaculture scenarios require recalled context to be standardized; otherwise, invalid token consumption for model understanding will increase. If no incremental update mechanism is configured for high-frequency real-time data, stale data will continue to occupy context resources, further increasing token costs.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed characteristics of aquaculture data including short time-series records and long industry reports, balancing single-chunk information integrity and token usage |
similarityTopK | Top 3–5 entries | Aquaculture structured data has strong correlation; excessive recall will lead to token redundancy. This range retains core monitoring and analysis data |
rerankTopN | Top 2–3 entries | Focuses on core aquaculture metrics and market data, avoiding non-critical documents from occupying excessive context tokens |
maxContextToken | 8000–12000 tokens | Adapts to the context window range of mainstream large language models, preventing output truncation caused by exceeding model token limits |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to scenarios where users batch upload industry reports and datasets for aquaculture, balancing upload efficiency and content completeness |
tokenLimitPerResponse | 2000–3000 tokens | Ensures investment research Q&A can output complete analysis conclusions, avoiding incomplete results due to token limits |
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 individual analysis, and it is recommended to test on samples specific to the deployment before finalizing settings.
Three Common Configuration Errors
- Q&A return results are truncated with
[hide X char]displayed. This occurs whenmaxContextTokenortokenLimitPerResponseis not configured correctly, causing context or output tokens to exceed model limits. - Q&A response takes too long and has excessive token consumption. This occurs when the number of recalled entries is set too high, introducing a large volume of non-core historical aquaculture data and increasing context token usage.
- An exception
failed to get gpt-3.5-turbo token encoderis displayed on startup. This occurs when the local model’s token encoder configuration is missing, or it does not match FastGPT’s token statistics rules.
How to Confirm a Configuration is Properly Set Up
- Upload a single aquaculture industry research report, and check if the character count of each segmented chunk falls within the
chunkSizeconfiguration range. - Initiate a Q&A that includes multiple sets of aquaculture monitoring data, and verify that the number of recalled documents matches the
similarityTopKsetting. - Test a Q&A output that includes Markdown tables, and confirm there are no truncation prompts and tables are fully displayed.
- Upload a batch of structured aquaculture datasets, and check that the upload process does not trigger restrictions related to
UPLOAD_FILE_MAX_SIZE.
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.