Deployment and Upgrade for Cold Chain Logistics R&D Document

Data Characteristics

R&D document data in cold chain logistics primarily originates from internal experimental reports, equipment validation reports, SOPs (Standard Operating Procedures), regulatory compliance declarations, and supplier technical manuals. These documents typically exist as PDFs, Word files, or scanned images. Data updates frequently, especially during new product development, new equipment introduction, or regulatory changes. Document structures are complex, containing numerous technical terms, charts, tables, and formulas. Key fields include temperature ranges, humidity parameters, time windows, batch numbers, sensor models, calibration dates, deviation records, compliance standards (e.g., GMP, GDP), and specific units (e.g., ℃, RH%, hours, cubic meters).

Constraints Imposed by These Characteristics on Deployment and Upgrade

The complex structure and high update frequency of cold chain logistics R&D documents pose specific requirements for FastGPT's deployment and upgrade. The specialized terminology and extensive numerical data in documents demand strong semantic understanding and entity recognition capabilities from the model. During deployment, ensure the model accurately parses this specific information. High update frequency means the knowledge base must support efficient incremental update mechanisms, avoiding full rebuilds with each update, which directly impacts service availability during upgrades. Additionally, common scanned documents and unstructured charts in documents require advanced file parser capabilities. During deployment, configure appropriate OCR and layout analysis components. During upgrades, pay close attention to whether the model maintains consistent parsing of historical data when processing new versions, preventing old data queries from failing due to model iteration.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Cold chain logistics documents often include images and charts, leading to larger file sizes.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Document content density is high; longer chunks help maintain contextual completeness.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Parsing large files and OCR processes can be time-consuming; this avoids timeout interruptions.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Ensures coverage of multiple highly relevant information points in complex queries.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Requires fine-tuning according to the distribution of specific terminology and numerical values.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (top 5)	Improves the precision of final results while maintaining recall rate.

Three Common Mistakes

Knowledge base query results are generalized, and the large model fails to effectively summarize output: This is due to an unreasonable chunking strategy, leading to insufficient contextual information in retrieved fragments, or insufficient coverage of specialized domain terminology during model fine-tuning.
After upgrading FastGPT, some historical document query results are abnormal or empty: This may be due to incompatibility between the new version's file parser or embedding model and the old version, leading to knowledge base index invalidation or semantic understanding deviations.
After deploying a custom model, the performance in the chat interface and workspace model is inconsistent: This is because model configuration files or parameters loaded in different environments vary, and consistency in model loading was not ensured.

Verification Steps

Select a batch of typical cold chain logistics R&D documents containing key parameters and technical terms. Upload and parse them. Check if the parsed text segments are complete and if key information (e.g., temperature ranges, batch numbers) is accurately identified.
For core business scenarios, design complex query statements with multiple variables. Verify if the knowledge base accurately recalls relevant document fragments and observe if the large model's summarization and abstraction of these fragments meet expectations.
In different network environments, use the docker logs command to check the log output of OneAPI or FastGPT services. Confirm there are no connection refused or timeout error codes, ensuring normal communication between services.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.