What this category of data looks like
Policy and regulatory basis data for the warehousing and logistics sector mainly comes from official policy documents, industry standard documents, and internal warehousing operation regulations publicly released by the National Development and Reform Commission, the Ministry of Transport, and local commercial authorities. Data updates trigger alongside official policy releases, with small amounts of local detailed rules added monthly, and revised versions of core policies released each quarter.
Single document structure includes policy document number, issuing authority, effective date, applicable warehousing scenarios, clause details, and attachment list. Fields cover policy number, applicable warehousing type, covered logistics nodes, penalty clauses, and more. Common measurement units are characters, square meters, and calendar days.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The data structure of this category is complex with clearly subdivided fields. Multi-turn dialogue must retain key context information such as policy document numbers and applicable warehousing types to avoid mixing clauses across policies.
Data update frequency is high. Prompts must explicitly require the model to prioritize the latest released policy versions to avoid referencing invalid clauses. The number of clauses in a single policy document is usually large. The context window for multi-turn dialogue must limit valid length to prevent redundant information from interfering with the model's extraction of core clauses.
Additionally, covered logistics nodes and warehousing types differ significantly. Prompts must specify the specific scenario the dialogue will bind to in advance to narrow the search scope.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind key-value pairs for warehousing type and policy scope based on dialogue scenario, for example {"warehouse_type": "cold chain warehousing", "policy_year": "2024"} | Warehousing and logistics policies have clear scenario subdivisions. Passing fixed parameters via variables narrows the model's search scope and avoids mixing clauses across scenarios |
messages | Retain the context of the first 10 dialogue turns, with each message limited to 800 characters or fewer | The number of clauses in a single warehousing policy document is usually large. Excessively long context prevents the model from focusing on core content. Limiting turn count and length improves response efficiency |
chatId | Assign a unique identifier to each independent consultation session | Using chatId isolates context across different sessions and avoids information cross-interference when multiple users consult simultaneously |
| Chunk length | 800–1200 characters | The length of individual clauses in warehousing policy documents mostly ranges from 500 to 1000 characters. Chunking within this range fully covers individual clause content and reduces information loss caused by chunk breaks |
| Question optimization | Enabled | Queries about warehousing policies often include vague expressions, such as "warehouse land use policies". Question optimization automatically supplements applicable scenarios and policy types to improve search accuracy |
| Maximum context | Top 5 context entries | Non-core policy information from early multi-turn dialogue occupies the model's window. Limiting the number of context entries retains key dialogue logic and avoids redundant interference |
Three Common Misconfigurations
- Phenomenon: After passing the
variablesparameter via API, the model's generated content does not link to the specified warehousing scenario or policy scope. Cause: The prompt does not explicitly state that parameters withinvariablesmust be used, or the parameter key name does not match the pending verification in the prompt. - Phenomenon: After text content extraction fails, the system sends a specified prompt but does not wait for user-supplied supplementary information. Cause: No user input waiting node is configured in the workflow, causing the dialogue process to end directly.
- Phenomenon: The model only outputs Chinese content and does not return results in the specified language as required by the prompt. Cause: The language requirement in the prompt is not bound to the dialogue context, or the model does not correctly recognize the English instructions in the prompt.
How to Verify Proper Configuration
- Pass the preset
variablesparameter, initiate a test dialogue, and verify that the model's output includes the specified warehousing type and policy scope. - Check the session log to confirm that the
messageslist retains the most recent 5 dialogue turns and contains no redundant non-core information. - Upload a warehousing policy document and test whether the chunked content fully covers individual clauses without breaks or truncation.
- Initiate a vague query, such as "warehouse land use policies", and verify that the model automatically supplements applicable scenarios and policy types.
The values provided here 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 2026-09-28.