What the data for this category looks like
This category covers liquid milk, yogurt, milk powder and dairy ingredient manufacturers. Daily data includes raw milk acceptance records, standardized ingredient lists, pasteurization/UHT heat treatment records, CIP cleaning records, filling ledgers, retained samples and microbial test reports. Data is sourced from milk source testing systems, MES systems, homogenization and heat treatment equipment, online temperature and flow collection modules, CIP systems and LIMS laboratory management systems. Update frequency occurs per milk tank, per batch or in real time. The data consists of continuous records across the entire process of liquid biological raw materials, without the batch-by-batch isolation conditions present in solid product categories.
What constraints these characteristics impose on the vector model and index workflow
The high-frequency, real-time update rhythm of this category requires vector indexes to support incremental updates, to avoid redundant overhead from full index reconstruction. Full-process continuous liquid raw material data requires that key associated identifiers such as milk tank numbers and production batches are retained during vector chunking and indexing, to ensure the complete production chain can be restored during retrieval. Multi-field, strongly correlated business documents require index structures to support filtered retrieval by batch and link type, to narrow the recall scope. The timeliness requirement for abnormal data requires controlling index retrieval response delay to match production tempo, to avoid impacting on-site decision-making.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Matches the average field length of single documents such as raw milk acceptance records and microbial test reports, avoids splitting key information across chunks |
| Overlap length | 100–150 characters | Retains associated identifiers such as batch and tank numbers across chunks, ensures semantic coherence |
defaultToken | Calibrated based on actual measurements | Adapts to the proportion of professional term tokens in LIMS microbial test reports, avoids truncating key test data |
maxToken | 4096 tokens | Matches the input limit of mainstream embedding models, covers the total token count of full-process records for a single batch |
| Single search limit | Top 10 results | Balances retrieval recall rate and response speed, adapts to real-time query requirements in production links |
| Similarity threshold | 0.75–0.85 | Distinguishes the semantic similarity boundary between normal production data and abnormal data, meets the strict requirements of food processing |
Configuration values are common starting points and should be measured against your own samples.
This document adapts to the configuration logic of FastGPT v4.17.0
Three common configuration errors
- Phenomenon: The dataset.csv exported from the knowledge base only contains the index field and no content field. Cause: The content extraction switch was not enabled during chunk configuration, only index metadata was synchronized without capturing the original document text.
- Phenomenon: The number of returned retrieval results does not match the single search limit, and a large number of irrelevant cross-batch data appears. Cause: Filter rules based on production batches and tank numbers were not configured, causing the recall scope to exceed the target production chain.
- Phenomenon: The embedding model call returns an error or the generated vector has no valid semantics. Cause: The token proportion of dairy professional terms was not adapted, the
maxTokensetting was too low causing document truncation, or thedefaultTokenconfiguration did not match the model input requirements.
How to verify the configuration is correct
- Upload a typical raw milk acceptance record document, check if the chunked content field fully retains key information such as raw milk batch and antibiotic test results.
- Initiate a retrieval for "raw milk antibiotic exceeding standard", verify that the number of returned results matches the single search limit configuration, and all results are associated with valid production batches.
- View the index update log, confirm that frequently updated production data can automatically trigger incremental indexing without manual full index reconstruction.
- Export the knowledge base dataset, confirm that the generated csv file contains both index and content fields, and the field content matches the original document.