What the Data for This Category Looks Like
Business data for refined oil sales mainly comes from gas station POS transaction systems, inventory and supply chain management systems, oil quality inspection archives, and regional price public announcement platforms. Data update frequencies cover three categories: real-time (transaction records), hourly (stock levels), and daily (posted prices). Each individual business document includes fields such as transaction time, oil product grade, transaction unit price, transaction volume, payment method, and service outlet ID. Units include liters, yuan, yuan per liter, transactions, and more. Outlets in different regions will add local discount rule fields.
Constraints on Model Access and Configuration Posed by These Data Characteristics
Real-time updated inventory and price data require the model to pull the latest data in real time during calls. Dynamic context introduction must therefore be supported in configurations. Multi-field business data with clear units require the model to accurately match fields and units. This avoids confusion in grade labels and incorrect units in guidance content. Regionalized discount rules need to split knowledge base content by outlet dimension. This prevents cross-region rule confusion, so context recall must limit the dataset scope for the corresponding outlet. Business process guidance must strictly follow established rules. Random content generation should be avoided.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Refined oil business data includes 7-day transaction records, regional price rules, and inventory details. Sufficient context is required to support accurate guidance |
quoteMaxToken | 1500–2500 token | Business guidance needs to reference specific inventory and price data. Limiting quoted token counts avoids exceeding the model's context capacity limit |
temperature | 0.2–0.4 | Business handling must strictly match established rules. Guidance content deviating from actual business should be avoided |
toolChoice | auto | Automatically call tools based on query scenarios to obtain real-time inventory or price information for the current outlet |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address provided by the FastGPT platform | Large model calls adapted to refined oil business need to bind dedicated nodes to ensure stable data transmission |
AIPROXY_API_TOKEN | Dedicated key bound to the access node | Ensure permission security for model calls and avoid unauthorized access to business data |
Three Common Configuration Mistakes
- Symptom: The workflow runs and returns a "context limit exceeded" error, and the parameter settings on the model configuration page are correct. Cause: The values of
maxContextorquoteMaxTokenare not restricted, causing the business data tokens brought into a single call to exceed the model's supported limit. - Symptom: The model prompts "no relevant oil product information found" during business guidance, but the corresponding grade oil product data has been uploaded to the knowledge base. Cause: The value of
quoteMaxTokenis too low, resulting in insufficient recall of inventory and price details to support effective matching. - Symptom: Single business query takes too long, and a loading timeout prompt appears on the page. Cause: The generation limit of
maxTokensis not restricted, causing the model to generate overly long guidance text, or bringing in excessive historical transaction data during calls.
How to Verify Correct Configuration
- Upload one test document containing 7-day oil product transactions and regional price rules, trigger the business guidance process, and check whether the context token count brought into the model call logs in the FastGPT platform meets the configuration requirements.
- Simulate oil product query requests for different grades and different outlets, and check whether the guidance content returned by the model accurately matches the corresponding rules in the knowledge base.
- Adjust the value range of the
temperatureparameter, and verify that the generated guidance content maintains rule consistency without obvious expressions that deviate from actual business. - Check the binding relationship between
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT, and confirm that there are no permission error or connection failure log records.
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 2026-09-28.