What the data for this category looks like
Aquaculture marketing content data mainly comes from internal enterprise aquaculture operation manuals, pond patrol records, feed and medication ledgers, popular science copy for farmers, and sales pitches for dealers. The data update rhythm falls into two categories: pond patrol and aquaculture parameter data are updated daily, while marketing promotion materials are adjusted on demand alongside activities. The document structure includes two core field types: aquaculture parameter fields such as pond ID, dissolved oxygen (mg/L), feeding amount (kg/亩), and marketing content fields such as scenario tag, copy body. Some documents contain both types of information.
What constraints do these characteristics impose on vector models and indexing
The mixed document structure of aquaculture parameters and marketing copy requires vector encoding to adapt to both numeric parameters and semantic connections of text-based sales pitches. Avoid splitting chunks that separate parameters from their corresponding explanations. Daily updated patrol data and on-demand updated marketing materials require indexes to support flexible switching between incremental refresh and full refresh. This balances real-time performance and computing resource usage. Fields with units must retain unit information during chunking. Otherwise, vector matching will lose the precise meaning of parameters, which affects the relevance of subsequent recall results. Long documents such as aquaculture manuals need proper splitting. Avoid overly long single chunks that cause semantic fragmentation.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Aquaculture marketing content often includes aquaculture parameter explanations. Segments that are too long will lose the connection between parameters and their corresponding copy. Segments that are too short will damage the semantic integrity of marketing copy |
RECALL_TOP_K | Top 6–10 results | Matching marketing content needs to cover needs of different aquaculture scenarios. Too many results will increase inference costs. Too few will miss precise matching items |
INDEX_REFRESH_INTERVAL | Every 12 hours | Pond patrol data is updated daily, marketing materials are updated on demand. A 12-hour refresh cycle balances real-time performance and resource consumption |
SIMILARITY_THRESHOLD | 0.75–0.85 | Precise matching of aquaculture parameters requires a high threshold to avoid recalling irrelevant content |
CUSTOM_CHUNK_RULE | Calibrate based on actual testing | For mixed documents that contain both aquaculture parameters and marketing copy, custom chunk boundaries are needed to retain field connections |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- The interface cannot select the latest vector models such as Zhipu Embedding-3, CharGLM-4. The cause is that the built-in model list of the platform has not been updated synchronously. Manual configuration of third-party model interfaces is required.
- After local deployment, a prompt indicates vector model connection failure, even if ping is successful and the token is configured correctly. The cause is that the docker container network has not correctly mapped ports, or the permissions carried by the token do not cover the vector model call scope.
- When importing a knowledge base, it is impossible to create an index according to custom content. The cause is that the
CUSTOM_CHUNK_RULEconfiguration is not enabled, or the chunking rule is not associated with the corresponding document type.
How to Verify Successful Configuration
- Upload a test document that includes aquaculture parameters and marketing copy. Check whether the chunk preview retains the field content with units.
- Initiate a matching test, enter a query term for a specific aquaculture scenario, and verify whether the number of recall results matches the configured
RECALL_TOP_Kvalue. - Wait for the index refresh cycle to end, then upload new marketing materials. Check whether the knowledge base automatically updates the corresponding vector index.
- View system logs to confirm that the return status code of vector model call requests is
200, with no connection timeout errors.
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.