Data Characteristics for This Category
Core data sources for cold chain logistics registration and declaration documents include temperature and humidity monitoring reports, equipment calibration certificates, transport validation plans, risk assessment reports, SOP documents, and supplier qualification proofs. These documents typically come in PDF, Word, or Excel formats. Temperature and humidity data are continuous and update frequently, with new records potentially generated every few minutes. However, summary reports are usually generated per batch or periodically. Document structures for reports often include charts and tables. SOP documents primarily consist of structured text. Common fields include batch number, equipment serial number, calibration date, validity period, Temperature Range (temperature range, e.g., 2-8°C), humidity value, timestamp, and various standard codes and units, such as ℃, %RH, h.
Constraints Imposed by These Characteristics on Citation and Traceability
The continuous temperature and humidity data and periodic reports in cold chain logistics documents require the knowledge base to effectively handle time-series data and batch information during ingestion and indexing. This ensures citations can trace back to specific monitoring timestamps and batch records. The numerous tables and charts in documents demand high-quality structured information extraction from parsers. This ensures key data in charts are correctly identified and used for citation. The heterogeneous nature of different document types (reports, SOPs, certificates) necessitates distinguishing whether cited content comes from guiding principles in standard operating procedures or from actual measured data of a specific batch. Specific units and ranges in fields, such as 2-8°C, require precise matching or range-based retrieval during search. This avoids citation failures due to unit or format discrepancies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 300-500 characters (characters) | Temperature and humidity reports and SOPs often contain relatively independent paragraphs or operational steps. This length helps maintain semantic integrity and prevents key information from being split. |
Recall count (Recall Count) | 8-12 entries (items) | Considering the diversity of document types and potential data density, increasing the recall count improves the probability of retrieving relevant temperature and humidity data, equipment calibration records, or SOP steps. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Cold chain data demands high precision. A threshold that is too low may introduce irrelevant batches or reports. A threshold that is too high may miss relevant records with slightly different phrasing. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5 items) | After recall, reranking further filters the most relevant few items. This focuses on core temperature/humidity anomalies or critical operating procedures, reducing interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Cold chain logistics reports may contain many charts and tables, leading to longer parsing times. Increase the timeout to ensure large documents are fully parsed and avoid parsing failures due to timeouts. |
maxContext | 4000 characters (characters) | For potentially long descriptive paragraphs and data lists in temperature and humidity reports, a larger context window helps the model understand the overall situation and citation relationships of batch data. |
Three Common Pitfalls
- The model output cites a series of red numbers. A knowledge base query reveals the batch number or equipment serial number does not exist. This happens when the parser fails to accurately identify formatted numbers in the document, leading to data discrepancies during indexing.
- Generated declaration documents cite the wrong SOP version or batch. This occurs because the knowledge base, when handling multi-version documents, lacks effective version control or timestamp association mechanisms. This leads to confusion between records from different time points during retrieval.
- When retrieving temperature and humidity data, the number of results is significantly lower than expected. This happens when table parsing parameters are not configured correctly, leading to key data in charts not being extracted, or when the
Similarity threshold(similarity threshold) is set too high, failing to recall all relevant data points.
How to Confirm Correct Configuration
- For typical temperature and humidity monitoring reports, upload them and then check if the knowledge base can retrieve key temperature and humidity data points, batch numbers, and corresponding monitoring timestamps from the report. Confirm that the citation source accurately points to the original report page.
- Upload multiple versions of SOP files. Use questions to verify if the model can differentiate between different SOP versions and correctly cite the latest or specified version of the operating procedure.
- Simulate queries involving specific temperature ranges (e.g.,
2-8°C) or equipment models. Cross-reference whether the returned citation sources include relevant equipment calibration certificates and transport validation records, and check their accuracy.
The values provided are common starting points. Measure them against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.