Deployment and Upgrades for Cold Chain Logistics Regulations

Data Characteristics

Regulatory and SOP documents in cold chain logistics originate primarily from supervisory bodies, industry associations, and internal quality management systems. These documents are typically in PDF or Word format, with some regulations published as HTML pages on internal knowledge platforms. Data update frequency is relatively low, usually quarterly or annually, but temporary updates may occur for emergency plans or new regulations. Document structures are hierarchical, containing numerous regulatory clauses, operational steps, terminology definitions, and diagrams. Fields and units include temperature (Celsius ℃), humidity (percentage %), time (hours h, days d), batch numbers, and storage conditions, requiring high precision and consistency.

Constraints on Deployment and Upgrades from Data Characteristics

The diverse sources of cold chain logistics regulatory documents require deployment to support unified ingestion and format conversion from multiple sources. A lower update frequency means initial data synchronization can use bulk imports, reducing the complexity of subsequent incremental update mechanisms. However, effective management and conflict resolution for new and old document versions remain important. The presence of diagrams and specialized terminology in documents demands higher accuracy in text extraction and semantic understanding. During segmentation, it is crucial to avoid breaking the integrity of key information blocks. Precise identification of critical fields like temperature and humidity requires targeted optimization in model training or prompt engineering to ensure accuracy and compliance of Q&A results. Deployment environment choices, such as private deployment, must meet data security and compliance requirements.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Regulatory documents are often large; full document uploads must be supported.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures each segment contains sufficient context and prevents semantic dispersion from excessively long segments.
`Chunk Overlap Length` (Segment Overlap Length)	`100 characters`	Guarantees continuity between segments and improves recall accuracy.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDF documents can be time-consuming; sufficient time must be allocated.
`Similarity threshold` (Similarity Threshold)	`0.75`	Increases recall precision and filters out irrelevant regulatory clauses.
`Recall count` (Number of Recalled Items)	`Top 5 entries`	Provides enough relevant evidence for the model, considering the rigorous nature of regulatory documents.

Common Pitfalls

Model channel testing fails, but the server is deployed. This may be due to incorrect AI_PROXY_URL or OPENAI_API_KEY configurations, preventing FastGPT from connecting to the model service.
After calling the API, model responses and reference details are not returned simultaneously. This typically occurs when the API request parameters do not include the detail or show_quote fields, causing the system to not return citation information by default.
After local deployment, creating a new knowledge base Q&A results in a configuration error. This may be related to incorrectly set environment variables such as VECTOR_STORE_TYPE or LLM_MODEL_NAME, leading to knowledge base initialization or model call failures.

Verification Steps

Upload a typical cold chain logistics SOP document. Check if knowledge base segmentation is complete and if key fields (e.g., temperature ranges, operational steps) are not truncated.
Ask questions about specific clauses in the regulations. Verify if the model can accurately cite the original text and provide expected answers.
Simulate a cold chain anomaly handling scenario. Test if the system can provide correct guidance based on relevant emergency plans and verify the cited regulation versions.

The values provided are common starting points and should be measured against specific 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.