What the data for this category looks like
Logistics revenue yield-related data mainly comes from internal enterprise transport management systems, warehouse management systems, and third-party road freight rate index platforms. Different business types have different update frequencies: real-time trunk transport rates update daily, warehouse monthly rental rates update weekly, and monthly settlement surcharge data updates monthly. Single data documents are mostly Excel or CSV files, with fields including origin-destination route, vehicle type, billable weight, unit price, and settlement method. Common pricing units include yuan/ton, yuan/cubic meter, and yuan/ton·kilometer. Field naming varies across different systems.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-source and scattered nature of logistics revenue yield data requires multi-turn dialogue to guide users to supplement necessary parameters step by step according to business links, preventing missing key information in single requests. Diverse pricing units require prompts to explicitly specify that users confirm the pricing dimension, preventing calculation deviations caused by unit confusion. Differences in update rhythms across different business data require adding a data timeliness confirmation link in the dialogue flow, such as asking for the statistical period of required data. Non-standard field naming rules require presetting common field mapping logic in prompts to assist users in supplementing missing information items.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000–6000 characters | Logistics revenue yield data has many fields. Multi-turn dialogue needs to retain sufficient context to track user-supplied parameters such as routes and cycles |
UPLOAD_FILE_MAX_SIZE | 50 MB | Logistics freight rate data is mostly Excel tables. Single batch data files usually do not exceed 50 MB. Exceeding this size will cause upload failure |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing batch freight rate tables requires traversing multiple sets of route data. 120 seconds covers parsing time for most medium-sized files |
Recall count | Top 8 entries | Logistics route data has high similarity. Too many recalled entries will increase context burden. Top 8 entries covers common route comparison needs |
Similarity threshold | 0.75–0.85 | Logistics route names have abbreviations and full name differences. Too low a threshold will recall irrelevant routes. Too high a threshold will miss matching entries |
Chunk size | 1000–1200 characters | Single-parsed freight rate data needs to retain complete route and billing information. This length avoids losing key context after splitting |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three common mistakes
- Phenomenon: After deploying via image packaging, uploaded attachments cannot be recognized, with no error prompt. Reason: The file storage mount path was not configured correctly during image deployment, causing the system to fail to read uploaded attachment files.
- Phenomenon: When calling file link variables in dialogue, the returned content does not match the freight rate data in the attachment. Reason: The reading range of the variable was not clearly specified in the prompt, causing the system to only grab some irrelevant content from the file.
- Phenomenon: In the 4.6.9 version advanced orchestration, AI dialogue after the pre-judgment cannot obtain the initial user question. Reason: The switch to pass through the original user input was not enabled in the judgment's output node, causing the initial request to be lost in the context link.
How to confirm the configuration is complete
- Upload a standard logistics freight rate Excel table, check if the fields parsed by the system meet business needs, and adjust relevant configuration values based on the parsing results.
- Initiate a multi-turn dialogue, supplement parameters such as routes, vehicle types, and cycles in sequence, check if the context retains all input information during the dialogue, and adjust the context length configuration based on dialogue coherence.
- Test the attachment upload and parsing process in both local development environment and image deployment environment, and adjust relevant configuration for file upload and parsing based on test results.
- Run the advanced orchestration process, verify whether the AI dialogue after the pre-judgment can obtain the initial user question, and adjust the pass-through parameter configuration based on link integrity.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.