Data Characteristics
Cold chain logistics registration documents involve diverse data types. These originate from internal quality management system files, third-party validation reports, and regulatory updates. Data sources include temperature and humidity monitoring logs, equipment calibration certificates, supplier qualification documents, transportation route planning records, and regulatory updates from various national drug regulatory agencies. Data updates occur frequently. For instance, temperature and humidity logs are continuously generated, while regulatory updates are typically released quarterly or annually. Document structures are complex, comprising numerous batch reports in PDF format, operational procedures in Word format, and summarized data in Excel format. Common fields include "Batch Number," "Product Name," "Start Temperature," "End Temperature," "Transit Time," and "Calibration Date." Units involve degrees Celsius, percentage humidity, hours, and days, and multilingual expressions are present.
Deployment and Upgrade Constraints
The characteristics of cold chain logistics registration documents impose specific requirements on FastGPT deployment and upgrades. The high volume of temperature and humidity logs and various reports results in a large number of documents. This necessitates efficient document ingestion and indexing capabilities and high storage capacity. Frequent regulatory updates require the knowledge base to quickly synchronize external information and perform incremental updates, ensuring query result timeliness. Diverse document formats and a mix of structured and unstructured data challenge the parser, requiring accurate identification and extraction of key fields. Multilingual text requires multilingual processing capabilities during deployment and compatibility assurance during upgrades. Furthermore, the presence of sensitive data (e.g., product batches, customer information) makes security isolation and permission control critical deployment considerations.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large batch reports or validation files, ensuring unhindered uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 | Provides sufficient parsing time for complex PDF reports and scanned OCR recognition. |
maxContext | 1200 | Ensures sufficient context to cover lengthy regulatory clauses or report summaries during questioning. |
Chunk size | 800 | Balances semantic integrity and retrieval efficiency, preventing context loss from excessive segmentation. |
Recall count | 5 | Increases recall quantity to enhance relevance coverage due to the high accuracy requirements of registration documents. |
Similarity threshold | 0.75 | Balances recall precision and recall rate, reducing irrelevant results and ensuring critical information retrieval. |
Common Pitfalls
- Query results remain outdated after a knowledge base update. This occurs when incremental indexing is not correctly triggered or when high index service load causes update delays.
- After uploading PDF files, some table data is not correctly recognized and extracted. This typically results from insufficient OCR capabilities of the PDF parser for complex tables or scanned documents.
- The system responds slowly or times out when handling a large number of concurrent queries. This indicates insufficient QPS (queries per second) capacity or improper database connection pool configuration.
Verification Steps
- Upload a cold chain logistics validation report PDF containing complex tables and multilingual content. Verify that key fields (e.g., "Validation Date," "Temperature Range") are correctly extracted and indexed.
- Simulate a regulatory update by uploading a new regulatory document. Immediately query relevant clauses to confirm the updated content is effective.
- Use a stress testing tool to simulate multiple concurrent users querying the system. Monitor system response times to ensure stable performance under expected concurrency.
- Check the log system for any error messages related to file parsing failures, database connection errors, or out-of-memory issues.
The values provided are common starting points. Measure against specific samples to determine optimal settings.
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.