What the data for this category looks like
Aquaculture investment research data comes primarily from pond sensor monitoring, daily operation records of breeding entities, sampling reports from fishery administration departments, public weekly reports from industry associations, and supply ledgers from feed suppliers. Data update cycles vary widely. Sensors upload parameters such as water temperature and dissolved oxygen in real time or hourly. Daily breeding records are aggregated each day. Industry policies and market updates are released irregularly. Most documents use structured formats, with fields including unique pond identifiers, breeding species, water quality indicator values and their corresponding units, feeding amounts, disease records, and listing cycles. The semantic unit of a single document centers on a single breeding cycle for one pond.
What constraints these characteristics impose on vector models and indexing
High-frequency time-series data requires incremental index updates. This prevents full index rebuilds that cause indexing delays and reduce investment research timeliness. Structured multi-field documents must be split and embedded according to business units. This ensures semantic integrity of daily records for a single pond, and avoids breaking logical relationships between fields. Numeric fields have clear units. This requires vector models to support semantic encoding of both values and units, to improve recall accuracy for professional parameters. Documents for multiple breeding species have significant differences. Indexes must support filtering by metadata such as breeding species and pond ID, to narrow retrieval scope and reduce interference from irrelevant data.
How to Configure
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Select Doubao-embedding-v3 or a locally deployed bge-large-zh-v1.5 | Aquaculture data contains a large number of technical terms and numeric parameters, requiring models that support professional semantic encoding |
chunk_max_length | 800–1200 characters | The text length of daily monitoring records for a single pond typically falls between 600 and 1000 characters. Splitting within this range preserves business semantic integrity |
retrieval_top_n | Top 8–12 results | Aquaculture investment research requires a balance between comprehensiveness and accuracy, avoiding recall of excessive irrelevant pond data |
similarity_threshold | 0.72–0.85 | Filters low-similarity unrelated breeding records to reduce ineffective recalls |
reindex_batch_size | 500–800 items per batch | Controls per-batch processing volume during batch re-embedding, to avoid excessive system resource usage |
enable_metadata_filter | Enabled | Supports filtering retrieval results by breeding species and pond ID, to narrow retrieval scope |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After switching the
embedding_model, the system shows no progress and cannot roll back to the original model. Cause: The forced reindexing switch is not enabled. The system locks the currently active vector model configuration by default. - Phenomenon: After configuring the Doubao embedding interface, the test returns "404 page not found". Cause: The API access path is not correctly configured, or the key has insufficient permissions, preventing calls to the target model interface.
- Phenomenon: After batch re-embedding the knowledge base, the recall rate for multilingual documents does not meet standards. Cause: No dedicated adapted embedding model is configured for multilingual breeding documents. Only a single-language model is used to encode multilingual text.
How to Confirm the Configuration is Complete
- Navigate to the knowledge base settings page, and check if the
embedding_modelconfiguration item matches the preset value. - Submit a single pond breeding monitoring record for testing, and confirm there are no error messages in the vector generation logs.
- Run a batch re-embedding task, and confirm the processing progress displayed in the system task queue meets expectations.
- Initiate a retrieval test, and verify that the system can filter retrieval results by metadata such as breeding species and pond ID.
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.