What data for this category consists of
This category’s data typically originates from ERP systems, supplier shipping documents, logistics records, and equipment manufacturer backends. Updates are generated per order or weekly. Daily processed documents include purchase orders, delivery acceptance receipts, serial number lists, and invoice materials. Common data fields include purchase identifiers, equipment models, serial numbers, delivery times, acceptance statuses, and billing information. Some documents include binary scanned files or electronic credentials.
Constraints imposed on model access and configuration
Multi-source data access requires adaptation to different interface protocols and format parsing rules. Model invocation data source configurations must cover multiple data source types. Per-order batch updates require setting a reasonable context window. This prevents single requests from exceeding model limits. Mixed input of structured fields and unstructured credentials requires configured content splitting and extraction rules. Security and compliance reviews require sensitive information desensitization. Corresponding processing logic must be enabled in model configurations to ensure compliance with data usage standards.
How to set configurations
This configuration guide applies to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total text length of a single batch of purchase orders and delivery acceptance receipts, prevents context overflow |
maxTokens | 2000–3000 token | Limits the output length of a single round of review results, complies with document output standards for compliance materials |
quoteMaxToken | 500–800 token | Limits the length of referenced original document fragments, ensures compliance of review content |
toolChoice | auto | Automatically triggers tool calls to extract structured fields such as serial numbers and models, improves extraction accuracy |
vision | Enabled | Supports parsing scanned copies of delivery acceptance receipts and electronic credential images of invoices, covers unstructured input scenarios |
temperature | 0.2–0.4 | Reduces output randomness, ensures consistency of compliance review results |
AIPROXY_API_ENDPOINT | Fill in the AI Proxy service root address according to the deployment environment, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. This is not a single-model address for VLLM/Ollama/Xinference | Required environment variable for the App/Admin backend, ensures requests are properly routed to the AI Proxy service |
AIPROXY_API_TOKEN | Fill in the non-empty administrator token consistent with the AI Proxy backend configuration. This is not an application API Key or model vendor key | Required environment variable for the App/Admin backend, completes AI Proxy service identity verification, ensures request legitimacy |
Three common configuration mistakes
- Phenomenon: Intermediate steps of tool calls are displayed on the public interface of compliance reviews. Cause: Tool output session display is not disabled in model configurations, leading to leakage of intermediate processing processes for compliance materials.
- Phenomenon: Image-format shipping documents and invoices cannot be parsed correctly. Cause: The
visionconfiguration item is not enabled, preventing the model from recognizing unstructured image documents. - Phenomenon: Extracted equipment models do not match actual delivered equipment. Cause: The
temperatureconfiguration value exceeds the reasonable range of 0.2–0.4, leading to excessive model output randomness and field extraction errors.
How to confirm configurations are properly set
- Submit a combined dataset of a single purchase order and its corresponding delivery acceptance receipt. Check if the model correctly extracts core fields such as equipment models and serial numbers.
- Upload a scanned image related to equipment. Verify that the model can recognize text information in the image and complete extraction.
- Initiate a tool call request. Confirm that structured data returned by tools is correctly integrated into review results, and intermediate steps are not publicly displayed.
- Adjust configuration item values. Compare field extraction consistency across different settings to determine value ranges that meet business requirements.
The values provided are common starting points and should be evaluated 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 2026-10-19.