What the Data for This Category Looks Like
Coke-related marketing data primarily comes from domestic port delivery ledgers, steel mill purchase order systems, bulk commodity spot trading platforms, futures exchange listing information, and financial institution position statistics. Data updates follow two cadences: spot transaction prices are updated daily, while port inventory, production capacity, and financial position data are updated weekly. Each data entry includes fields such as origin identifier, product specification, benchmark transaction price, total inventory, delivery cycle, and quality inspection parameters. Transaction price is measured in yuan per ton, total inventory in ten thousand tons, and delivery cycle in calendar days.
Constraints Imposed by These Characteristics on Model Access and Configuration
Daily-updated spot prices and financial position data require a real-time sync trigger mechanism when connecting the model, to avoid generating marketing content for financial clients using expired data. Weekly-updated inventory and production capacity data need to adapt to batch pull scheduling intervals, to avoid frequent calls that exceed interface quotas. Structured data with multiple fields requires clear field mapping rules during model access, to ensure key content such as quality inspection parameters and specification information is correctly identified for generating precise marketing materials. Fields with different units require unified unit conversion logic in the configuration, to prevent unit confusion in generated content. Additionally, exclusive data interfaces from futures exchanges and financial institutions have strict authentication rules, requiring separate configuration of dedicated tokens and channel permissions.
Recommended Configuration Values
| Configuration Item | Recommended Value | Rationale | ||
|---|---|---|---|---|
embeddingModel | bce-embedding-base_v1 | Adapts to semantic encoding of structured bulk commodity data, avoiding recognition bias of coke industry terminology by general-purpose models | ||
recallTopK | Top 8 entries | Coke marketing content needs to cover origin, price, and inventory dimensions. 8 recall results balance information richness and content conciseness | ||
rerankReturnCount | Top 3 entries | Marketing content for financial clients needs to focus on core decision-making information. 3 reranked results highlight the most reference-worthy spot and inventory data | ||
channelAuthTimeout | 15 seconds | Bulk commodity and financial institution interface response delays are generally high. A 15-second timeout avoids frequent authentication failures | ||
allowedModelList | gpt-4o, bce-embedding-base_v1 | Covers the entire workflow of conversation generation and semantic retrieval for financial clients | ||
PARSE_FIELD_MAPPING | Map as `origin→spec | transaction_price→price | inventory→stock` | Structured data field names have industry-specific differences. Clear mapping ensures correct data extraction |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Configuration Mistakes
- Symptom: An error is returned when calling the model:
This token does not have permission to use model: text-embedding-3-large. Cause: Dedicated token permissions for the target model were not configured in FastGPT's channel configuration, or the channel bound to the token does not cover the model type. - Symptom: The bce-embedding channel is configured and the service runs normally, but FastGPT prompts that no available channel exists. Cause: The bce-embedding channel was not added to the allowed model list of the current application, or the channel's authentication key was not correctly entered in the backend configuration.
- Symptom: Application logs show the error
No available channel for model gpt-4o under current group default. Cause: Channel permissions for gpt-4o were not added to the default group in the group configuration, or the channel's group binding configuration did not take effect.
How to Confirm the Configuration Is Complete
- Enter the FastGPT channel management page, check if the configured model channels include the currently used embedding and conversation models, and verify that the authentication parameters and interface address match the data source.
- Initiate a test retrieval, check if the number of recall results matches the preset recall configuration, and confirm that the content after field mapping matches the original data source fields.
- Generate a test marketing content piece, check if the content correctly uses the field units and parameters related to coke, and that no unit confusion or missing fields occur.
- View the application's log panel, confirm there are no error records such as
no available channelorauthentication failure, and that the interface call response time meets the configured timeout threshold.
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.