Vector Models and Indexing for Aquaculture Intelligent Due

What the data for this category looks like

Aquaculture intelligent due diligence report data mainly comes from daily pond monitoring logs, real-time water quality sensor data, feed feeding records, seed batch archives, disease test reports, and similar sources. Update rhythms vary significantly: water quality sensor data updates hourly, daily breeding logs update daily, and seed and disease archives update per batch or event. Most documents are structured Excel tables containing fields such as pond ID, dissolved oxygen (mg/L), pH value, water temperature (℃), feeding amount (kg/mu), seed density (individuals/㎡), and similar fields. Some test reports are in PDF format, containing text descriptions and test values.

Constraints imposed on vector models and indexing

The multi-source, variable update rhythm, and structured characteristics of aquaculture data impose multiple constraints on the vector models and indexing process. First, structured data contains a large number of numeric fields and unit information. Chunking must retain the association between fields and units to avoid losing semantic integrity in vector representations. Second, update frequencies differ widely across data sources: real-time water quality data requires support for incremental indexing, while batch archives can use full indexing mode. Additionally, multi-row data in Excel format has fixed row-level associations. Chunking must avoid splitting records across ponds or time periods into the same vector chunk, while adapting to batch import requirements for 10,000+ rows, balancing indexing efficiency and memory usage.

Configuration settings

Configuration Item	Recommended Values	Rationale
`chunk_size`	800–1200 characters	Adapts to the number of fields and information density of single aquaculture monitoring records, avoiding splitting that disrupts associations between ponds, time, and parameters
`chunk_overlap`	100–150 characters	Retains temporal context of adjacent monitoring records, preventing loss of sequence association information during retrieval
`embedding_model`	Locally deployed open-source embedding model	Meets privacy requirements for sensitive aquaculture data. M3E or Qwen/Qwen3-Embedding-8B can be selected
`embedding_batch_size`	32–64	Adapts to batch import of 10,000+ rows of Excel data, balancing indexing speed and memory usage
`similarity_threshold`	0.72–0.80	Filters low-match irrelevant monitoring records, meeting accuracy requirements for parameter matching in due diligence reports
`top_k`	Top 10–15 entries	Covers monitoring data across multiple ponds and time periods, avoiding overly concentrated recall results

The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and testing on samples prior to finalization is recommended.

Three common configuration errors

Symptom: A locally Docker-deployed M3E model fails to generate vectors normally, with the interface returning a 500 error. Cause: FastGPT's local_embedding_url parameter is not correctly configured to point to the internal port of the Docker container, or the container's external access permissions are not enabled.
Symptom: Repeatedly adding Qwen/Qwen3-Embedding-8B model configurations results in existing configurations being overwritten. Cause: No unique identifier is set for model configurations of different deployment instances. FastGPT uses the model name as the default unique key.
Symptom: Knowledge base chunks generated from Excel source data are too large, leading to insufficient granularity of retrieval results. Cause: The chunk_size parameter is not adjusted, and default long-text chunking rules are used, which do not adapt to the information density of single-row Excel monitoring records.

How to verify correct configuration

Import a single aquaculture monitoring Excel record, review the parsed chunk content to confirm that the chunk preserves full field integrity of the single record.
Initiate a due diligence report retrieval request, review the number of returned recall results, and adjust the top_k parameter to a range that meets business requirements.
Test the local embedding model's calling interface to confirm that FastGPT can normally retrieve vector data, with no connection timeouts or format errors.
Repeatedly add the same embedding model, verify that all uniquely identified model instances are retained in the configuration list, and no overwriting occurs.

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.