Multi-Turn Conversations and Prompts for Cold Chain

Data Characteristics for This Category

Cold chain logistics registration documents involve multiple data sources and complex document structures. Data primarily comes from temperature control equipment supplier product specifications, calibration reports, validation plans, in-transit temperature monitoring records, risk assessment reports, and operating procedures. This data updates relatively infrequently, typically with equipment model changes, regulatory updates, or annual validations. Document types include PDF technical manuals, Word SOPs (Standard Operating Procedures), and Excel temperature data logs and validation reports. Fields and units are highly specialized, such as Temperature Range (°C), Humidity Range (%), Temperature Control Accuracy (±°C), Calibration Cycle (Months), and GSP/GMP Compliance. Data forms often contain key identifiers like equipment serial numbers, batch numbers, and expiry dates.

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The data characteristics of cold chain logistics documents place specific requirements on multi-turn conversation and prompt configurations. First, diverse document formats and dense technical jargon require the model to have strong text understanding capabilities, accurately extracting key information from unstructured text. Second, low data update frequency means knowledge base construction and maintenance can use a periodic update strategy, avoiding frequent full rebuilds. For tabular data like temperature logs, the precision of fields and units is critical, requiring prompt design to effectively guide the model in identifying and processing numerical data, performing unit conversions, or range judgments. Additionally, regulatory requirements like GSP/GMP compliance necessitate incorporating specific legal and regulatory knowledge into conversations, ensuring the model generates submission content that meets regulatory standards. Contextual references in multi-turn conversations must be precise, down to specific document paragraphs or table rows, to support engineers in tracing and confirming submission details.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`8`	Ensures the model can review enough historical conversation turns for complex questions, understanding context and providing accurate cold chain-specific answers.
`Chunk size` (Segment Length)	`500–700 characters`	Cold chain documents contain lengthy descriptive paragraphs; this range helps retain semantic integrity and reduces context loss due to segmentation.
`Recall count` (Retrieval Count)	`Top 5`	Given the specialized and interconnected nature of cold chain submission documents, retrieving more relevant document snippets increases information coverage.
`Similarity threshold` (Similarity Threshold)	`0.75`	Increases the threshold to ensure retrieved knowledge snippets are highly relevant to the user query, filtering out low-quality or generalized information.
`Rerank result count` (Reranked Return Count)	`3`	Reranks retrieved document snippets, prioritizing the display of the most critical compliance or technical parameter information.
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Accommodates PDF documents in cold chain logistics that may contain numerous charts and high-resolution images, ensuring successful file uploads.

Three Common Mistakes

Temperature unit confusion or numerical errors appear in conversation responses. This occurs because prompts do not explicitly require the model to focus on units for numerical fields, or related data in the knowledge base is not standardized.
The model cannot reference specific table data within a document. This occurs because file preprocessing fails to effectively identify and parse table structures, leading to incorrect indexing of table content.
The system cannot conduct multi-turn follow-up questions on a newly uploaded calibration report. This occurs because the stream parameter is not set to true during API calls, or conversationId is not correctly passed, preventing the system from maintaining the session state.

How to Confirm Correct Configuration

Upload a cold chain validation report PDF containing a temperature curve graph and detailed parameters. Use the conversation interface to ask for key temperature point values and corresponding equipment serial numbers, checking if the model can extract them accurately.
Simulate a multi-turn conversation about GSP compliance for cold chain equipment. During the conversation, repeatedly shift the focus of the questions, observing if the model maintains contextual relevance and ultimately provides answers that meet regulatory requirements.
Use a newly uploaded Excel format temperature log file. Ask the model about the temperature fluctuation range for a specific batch of goods and request it to identify records exceeding a preset threshold, verifying the accuracy of data extraction and judgment.

The values provided are common starting points and should be measured against the reader's 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.