What This Category of Data Looks Like
Aquaculture research report data originates from monthly monitoring reports published by industry associations, technical white papers from research institutions, production logs of aquaculture enterprises, and annual reports of listed companies. Update cycles follow three patterns: industry monitoring data is updated weekly, special research reports are released monthly or quarterly, and technical documents are updated irregularly aligned with aquaculture cycle milestones. Document structures include standard fields: aquaculture species, seedling survival rate, feed conversion ratio, disease incidence rate, and market transaction price. Price units are mostly yuan per kilogram. Aquaculture density units are fish per mu or per cubic meter. Some documents include monthly aquaculture cost calculation tables. Supported document formats are PDF, Excel, and Word.
Constraints Imposed on Vector Models and Indexing Workflows
Aquaculture research reports contain both structured monitoring tables and unstructured technical text. Vector models must support vectorization of mixed-modal data. Weekly updated monitoring data and monthly released special research reports have different update frequencies. A refresh strategy combining incremental indexing and full indexing must be configured. Fields include numerical data such as aquaculture density and price, as well as text data such as disease descriptions. Vector models must adapt to encoding logic for multiple feature types. Many industry-specific terms are used. Vector models must use pre-trained weights adapted to agriculture, forestry, animal husbandry, and fishery fields to avoid loss of professional semantics. Some documents include multiple columns of associated data. Row and column context associations must be retained when splitting indexes. This prevents loss of data logic after vectorization.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aquaculture research reports contain long technical descriptions and table-associated data. This range preserves the integrity of professional semantics within a single segment |
chunk_overlap | 100–150 characters | Prevents professional terms across segments from being truncated, ensuring contextual association |
index_type | HNSW | Adapted to high-frequency recall scenarios. Weekly updated monitoring data has a large volume, and the HNSW index balances recall speed and accuracy |
recall_top_k | Top 10–15 results | Professional content in aquaculture research reports is highly concentrated. Excessive recall introduces irrelevant information, and this range covers core retrieval needs |
PARSE_TABLE_ENABLE | Enabled | Most aquaculture research reports include structured monitoring tables. Enabling table parsing preserves the vectorization logic of row and column associated data |
refresh_interval | 168 hours | Matches the weekly update cycle of industry monitoring data, ensuring the timeliness of index data |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After uploading data using "add to collection", the corresponding content cannot be retrieved during retrieval. Reason: The vectorization and index construction process was not triggered by creating a training order. Only uploading text did not complete the vector mapping step.
- Phenomenon: Vectorization process times out, and the task status shows failure. Reason: The
chunk_sizeparameter was not adjusted. Excessively long segments exceed the maximum input length limit of the vector model, resulting in parsing timeout. - Phenomenon: A large number of irrelevant aquaculture species content appears in retrieval results, and the number of recalled entries exceeds expectations. Reason: No reasonable similarity threshold was set, resulting in low-relevance content being recalled.
How to Confirm Proper Configuration
- Navigate to the vector index management page of the knowledge base, check whether the index status is "ready" to confirm that index construction is completed.
- Enter a query containing aquaculture professional terms, verify whether the fields of the recalled results match the uploaded research report content.
- Simulate uploading weekly updated monitoring data, check whether the vector database automatically triggers incremental index refresh without manual full index construction.
- Upload a document containing structured tables, check whether the row and column association information of the table is retained in the retrieval results.
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.