Deployment and Upgrade for Cold Chain Logistics Quality

Data Characteristics for This Category

Cold chain logistics quality document data primarily originates from real-time sensor data across the supply chain, operational records, compliance reports, and audit documents. Data update frequency is high; real-time temperature, humidity, and location data may update every few minutes, while operational records and anomaly reports are event-triggered. Document structures typically include standardized templates such as temperature control records, equipment calibration certificates, supplier qualification proofs, and Standard Operating Procedure (SOP) files. In addition to general text descriptions, fields often include timestamps, geographical coordinates, temperature (units: Celsius or Fahrenheit), humidity (units: %RH), equipment IDs, batch numbers, and drug/biological product names. This data often exists in a mixed format of structured (e.g., CSV, database exports) and unstructured (e.g., scanned PDFs, Word documents) types.

Constraints on Deployment and Upgrade Due to These Characteristics

The high real-time nature and mixed structure of cold chain logistics data impose specific requirements on FastGPT's deployment and upgrade processes. Frequently updated sensor data necessitates an efficient incremental indexing mechanism to avoid rebuilding the entire knowledge base each time, which impacts the maxContext configuration. A large volume of unstructured compliance documents, such as scanned PDFs, requires robust OCR capabilities and document parsing timeout mechanisms, directly related to PARSE_FILE_TIMEOUT_SECONDS. Diverse field types, especially numerical data with units, require normalization or preprocessing before vectorization to ensure accurate similarity calculations, affecting the complexity of data preprocessing scripts. Furthermore, the system's ability to ingest data concurrently from various sources, and the compatibility of existing data indexes during system upgrades, are crucial considerations in deployment planning, particularly when resource limits like UPLOAD_FILE_MAX_SIZE are involved.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accounts for individual compliance or audit documents potentially containing numerous images and attachments, ensuring large files can be uploaded in one go.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Scanned PDFs are common in cold chain documents, and OCR parsing can be time-consuming; increasing the timeout prevents parsing interruptions.
`Chunk size` (Segment Length)	`800–1200 characters`	Continuous data streams like temperature and humidity have strong contextual relevance; longer segments retain more contextual information.
`Recall count` (Recall Count)	`Top 8 entries`	Quality document queries often require cross-validation of multi-dimensional information; increasing the recall count improves information completeness.
`Similarity threshold` (Similarity Threshold)	`Calibrate by actual measurement` (Calibrate by actual measurement)	Cold chain-specific fields (e.g., batch numbers, equipment IDs) are highly sensitive; testing is needed to ensure precise matching.
`Rerank result count` (Rerank Return Count)	`Top 5 entries`	Ensures that among numerous recall results, the most relevant few key quality records are prioritized.

Three Common Mistakes

The plugin editing interface shows "Unsaved" even after clicking the save button. This is usually due to an unrefreshed frontend cache or browser incompatibility, requiring clearing browser cache or upgrading the browser.
An error occurs during image building, stating a specific module (e.g., rehype-raw) cannot be found. This indicates incomplete dependency installation or a build environment configuration issue, requiring checking the package.json file and npm install output logs.
After Docker deployment, containers continuously restart or are inaccessible. This is often caused by incorrect port mapping, improper environment variable configuration, or database connection failures, requiring checking Docker logs and the docker-compose.yml file.

How to Verify Correct Configuration

Upload a comprehensive document containing real-time temperature data, batch numbers, and a scanned PDF. Check if it parses and indexes correctly.
Perform searches using cold chain-specific query terms (e.g., "temperature anomaly records for batch ABC123", "XX equipment calibration certificate"). Verify the accuracy and completeness of the recall results.
Simulate high-concurrency data uploads via API interfaces. Observe system response time and resource utilization, comparing against baseline performance to ensure stability under heavy load.
Check system logs to ensure there are no widespread file parsing timeouts, indexing failures, or out-of-memory errors.

Note: The values provided are common starting points. It is important to measure and adjust them based on specific data samples and operational requirements.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.