What the data for this category looks like
Data sources for dairy industry investment research include internal production and quality inspection logs of dairy enterprises, public sampling reports from third-party food testing institutions, supply chain dynamics released by industry associations, and terminal retail sales statistics. Update frequency varies by data type: production and quality inspection logs are updated in real time with production batches, sampling reports are released with each sampling batch, industry dynamic data is updated weekly, and terminal sales data is synchronized daily. Document structures include structured quality inspection indicator tables, unstructured production process description documents, and semi-structured supply chain circulation records. For fields and units: quality inspection data includes milk protein content, milk fat content, total bacterial count, and other metrics. Milk protein content is measured in grams per 100 grams, and total bacterial count is measured in colony-forming units per milliliter.
What constraints these characteristics impose on model access and configuration
Structured quality inspection data has minor differences in fields and units. This requires configuration of field mapping rules during model access to unify format standards across different data sources. Real-time updated production batch data requires the model API's response latency to match production rhythms. Timeout parameters must be adjusted to avoid data expiration. Unstructured process documents and long-text sales records require configuration of appropriate segment lengths and context windows to prevent key information from being truncated. Multi-source data association needs require configuration of field matching weights during retrieval. This ensures cross-source data required for investment research can be accurately retrieved. Compliance requirements for dairy product sampling data require the model to prioritize identifying compliance-related fields. Similarity thresholds must be adjusted to filter low-relevance content.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Dairy-related documents include long-text production process descriptions and sales logs. This range balances information integrity and context window usage |
similarityThreshold | 0.72–0.85 | Structured quality inspection data has high requirements for field matching accuracy. This threshold filters low-relevance unstructured documents while retaining valid quality inspection report associations |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing bulk quality inspection reports and long documents for dairy products takes significant time. This duration prevents parsing from being interrupted mid-process |
embeddingModel | Determined based on actual testing | Dairy product data includes structured numerical values and unstructured text. General-purpose embedding models can cover semantic and numerical associations across both data types |
rerankTopN | Top 8–12 entries | Investment research requires association of multi-dimensional supply chain and quality inspection data. This quantity covers sufficient association dimensions while retaining relevance |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single files for dairy product bulk production logs and historical quality inspection reports are large. This upper limit supports bulk import requirements |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to conduct testing on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Connection timeout or connection refused errors occur during model access when deployed in an internal network environment. Logs show database connection exceptions. Cause: No internal network model access whitelist was configured, and no adjustment was made to the database connection's internal IP binding parameters, resulting in the model service being unable to access dependent components normally.
- Phenomenon: The vector results returned by the embedding model have low matching accuracy with dairy product quality inspection fields, and a large number of irrelevant documents appear in retrieval results. Cause: No adjustment was made to the embedding model configuration for structured numerical fields. General-purpose embedding models cannot accurately capture semantic associations of numerical fields.
- Phenomenon: After the knowledge base is created, the number of results returned by the reranking model does not match the configured value, and some batch number fields are not correctly identified. Cause: Batch numbers were not configured as specified retrieval fields in the reranking model's trigger rules, resulting in the model being unable to prioritize association with dairy product batch data.
How to confirm the configuration is complete
- Upload a single dairy product quality inspection report, verify that the parsed field extraction results match the original document, and adjust the field mapping rules until the matching accuracy meets investment research requirements.
- Initiate a retrieval request for a specific batch number, verify that the retrieval results include relevant documents for that batch, and adjust the retrieval weight parameters until the results meet expectations.
- Upload a single large-volume production log document, verify that the parsing task completes within the preset timeout period, and adjust the timeout parameters until the task runs without abnormal interruptions.
- Call the embedding model interface, verify that the returned vector results can correctly distinguish quality inspection indicators of different dairy products, and adjust the embedding model configuration until semantic matching meets requirements.
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.