What the data for this category looks like
Data sources for this category include raw milk testing, MES systems, homogenization and heat treatment equipment, online temperature and flow collection systems, CIP cleaning systems, and LIMS laboratory management systems. Daily documents include raw milk acceptance records, standardized batching sheets, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, retained samples and microbial test reports. Data updates occur per milk tank, per batch, or in real time. Fields include raw milk antibiotic and physicochemical indicators, equipment operating temperature, processing duration, batch numbers, retained sample numbers, and more. Units include mg/kg, ℃, seconds, batch numbers, and others.
What constraints these characteristics impose on vector models and indexes
Multi-source heterogeneous data sources and formats require vector models and indexes to support unified vectorization and indexing of structured numerical transcriptions, equipment log text, and transcribed content from scanned paper records. High-frequency real-time update rhythms require indexes to support incremental update logic, avoiding performance loss from full reconstruction. Batch-level update and isolation requirements mean indexes must bind batch dimension fields, ensuring retrieval results accurately match corresponding production batches. Indicator association characteristics across different links require vector models to capture semantic associations between time series and numerical values, improving retrieval matching accuracy. The batch characteristics of perishable raw materials also require index retrieval results to strictly distinguish batches, preventing cross-batch data from interfering with audit judgments.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to mixed documents of long-text heat treatment records and short-field raw milk indicators in dairy processing scenarios, balancing semantic completeness and retrieval accuracy |
| Overlap length | 100–150 characters | Retains cross-block data context associated with batches, avoiding semantic fragmentation that affects retrieval matching |
| Similarity threshold | Calibrated based on actual measurements | Adapts to judgment needs of business scenarios such as raw milk rejection and heat treatment compliance, accurately distinguishing qualified and unqualified batch data |
| Single search limit | Top 10 entries | Meets the need to quickly locate key records in audit scenarios, with a small number of results supporting decision-making |
maxToken | 4096 token | Adapts to the input upper limit of most mainstream vector models, supporting vectorization of long-text heat treatment and CIP records |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three common errors
- Phenomenon: The interface displays the error "503 Current group default has an issue with model text-embedding". Cause: The API key for the vector model is not configured, or network access permissions are restricted, preventing vectorization requests from routing normally.
- Phenomenon: The knowledge base deployed via Docker remains in the indexing state continuously. Cause: Incremental update logic is not configured. Full indexing of high-frequency updated dairy batch data leads to backlogged indexing tasks.
- Phenomenon: Retrieval results cannot accurately match the target raw milk batch. Cause: Batch dimension fields are not bound for indexing, leading to incorrect recall of semantically similar data across batches.
How to confirm the configuration is correct
Based on the configuration logic of FastGPT v4.17.0, the configuration effect can be verified through the following actions:
- View vector model call logs to confirm that the configured
defaultTokenandmaxTokendo not exceed the input limit of the selected model. - Submit a single raw milk acceptance record for indexing testing to confirm that the indexing task completes within a reasonable duration without continuous backlog.
- Retrieve heat treatment records for a specific batch to confirm that returned results only include documents associated with the target batch, with no cross-batch interference.
- Adjust the similarity threshold and verify that the matching accuracy of retrieval results meets the judgment needs of business audits.
The values provided are common starting points and should be measured against the reader's own samples.