What This Category of Data Looks Like
Data sources for aquaculture financial reports include publicly disclosed annual reports, quarterly aquaculture monitoring reports, and regional aquaculture data documents released by industry associations.
Updates follow three tiers: annual, quarterly, and monthly. Annual reports update once per year. Quarterly reports cover full-process data for the current quarter’s aquaculture operations. Monthly data includes real-time records such as feeding and disease prevention.
Document structures include fields like aquaculture scale, feed and epidemic prevention costs, revenue breakdowns, government subsidy details, and more. Some documents include visual content such as pond distribution maps and production trend charts.
Fields and units follow industry standards: stock volume commonly uses "tail" and "kilogram", area uses "mu" and "square meter", and costs use "yuan/kilogram" and "ten thousand yuan".
Constraints on Vector Models and Indexing
Documents with different update frequencies require matching index refresh strategies. Monthly data needs high-frequency synchronization. Annual reports require full index reconstruction to avoid data lag.
Documents contain many aquaculture-specific terms and specialized units. Vector models must recognize industry terms to avoid semantic confusion that causes incorrect recall results.
Some documents include visual charts. Embedding models must support multimodal content parsing. Without this support, key data such as production volume and pond area from charts cannot be fully extracted.
Content linked across multiple fields — such as "XX mu pond with XX kilogram stock volume" — must retain contextual associations. Chunking must not break the logical binding between fields.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aquaculture financial reports include multiple sections of professional content. This range avoids splitting that disrupts the logical association of fields such as aquaculture scale and costs |
chunk_overlap | 100–150 characters | Retains contextual continuity between chunks. Ensures semantic coherence between monthly feeding data and corresponding cost records |
embedding_model | Multimodal embedding model | Adapts to non-text content such as pond distribution maps and production trend charts in financial reports. Enables full extraction of visual data |
index_refresh_interval | Daily | Matches the update frequency of monthly aquaculture data. Synchronizes latest feeding and disease prevention records in a timely manner |
recall_top_k | Top 8–12 entries | Aquaculture financial reports have strong associations between professional fields. This recall volume covers complete business scenario fragments |
similarity_threshold | 0.75–0.85 | Filters generic industry content. Accurately recalls professional segments related to aquaculture species and costs |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- The interface displays "No available index model detected". This occurs when the embedding model is not bound to the index storage instance, or the index storage permission configuration is not enabled for the current application.
- Long sections of aquaculture cost details are truncated in chunking results. This happens when the maximum paragraph depth is set to 3, which cannot cover the multi-level professional content nested in financial reports.
- An error starting with
Invalidis returned when calling the multimodal embedding model. This occurs when the API access permission for the multimodal model is not configured, or the uploaded chart content is not converted to the base64 format supported by the model.
How to Confirm Proper Configuration
- Upload a quarterly financial report from an aquaculture enterprise, view the parsed chunk list, and confirm that fields such as stock volume and unit are not split across different chunks.
- Call the embedding model API, pass in the base64 converted content of a chart screenshot from the financial report, and verify that the returned embedding vector format meets requirements with no error returned.
- Check the index storage backend logs to confirm that the daily refresh task is executing normally with no timeout or failure records.
- Create a test conversation, input "The quarter's aquaculture stock volume and unit", and verify that the recalled snippet includes complete fields and corresponding values.
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.