What the Data for This Category Looks Like
Data sources for energy metals include public trading boards from global major commodity exchanges, domestic industry association monitoring data, public reports from supply chain enterprises, and corporate financial reports. Update frequencies vary by data type: spot quotes update daily after market close, industry inventory data updates weekly, and corporate production capacity and revenue data updates quarterly. Most documents are structured CSV or Excel files, with fields including product type, specification, pricing unit, latest quote, month-over-month change, inventory balance, etc. All fields have clear unit identifiers, with no vague general descriptions.
What Constraints These Characteristics Impose on Tool Calling and Plugins
The predominantly structured document format requires tool calling to support precise field-level parsing, to avoid generic text recall that causes category confusion. Data sources with different update frequencies require tools to adapt to scheduled synchronization mechanisms: daily updated spot data must be pulled in real time, while quarterly updated production capacity data can be synchronized in batches. Diverse pricing units require plugins to include built-in unit conversion logic to standardize output to the standard units required for marketing content. Additionally, marketing content must focus on specific energy metal categories, so tool calling must support filtering redundant data from non-target categories to prevent generated content from straying from the marketing theme.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Energy metals documents are mostly structured tables with multiple fields, and parsing requires additional time for field validation and format conversion |
Recall count | Top 8–12 entries | Energy metal categories have numerous sub-types, so sufficient entries must be recalled to cover product data of different specifications, avoiding missed precise matches |
Similarity threshold | 0.75–0.85 | A threshold is needed to distinguish highly matched specific energy metal categories from similar non-ferrous metal categories, preventing recall of non-target data |
Rerank result count | Top 3–5 entries | Marketing content must accurately focus on core categories, returning a small number of highly relevant results to adapt to content generation |
UPLOAD_FILE_MAX_SIZE | 500 MB | Historical data archive files for the energy metals industry are mostly batch-structured tables, so support for larger file uploads is required |
api_request_timeout | 60 seconds | When connecting to external exchange APIs, sufficient time must be reserved for data returns to prevent call failures due to slow interface responses |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: External data source API calls return
500 Internal Server Error. Browser access functions normally, but API calls fail. Cause: FastGPT API authentication parameters are not properly included, or the request header does not match the Content-Type format required by the interface. - Phenomenon: Semantic retrieval tools return
Connection error. Cause: The configured data source interface does not allow cross-domain requests, or network policies restrict outbound access from the FastGPT service. - Phenomenon: Marketing content generated by calling code modules in workflows cannot be streamed out. Cause: Streaming response headers and output formats are not configured in the code module, and the FastGPT streaming transmission protocol is not adapted.
How to Confirm Configuration is Complete
- Upload a single structured test document for energy metals, and verify that preset fields such as
product_nameandquote_unitare fully retained in the parsing results. - Call the configured external data source API, and verify that returned data units, category classifications, and business rules match, with no abnormal missing items.
- Trigger a marketing content generation request, and check whether results embed accurately matched energy metal data, with no redundant non-target category information.
- Simulate batch tool calls, confirm that interface responses do not exceed the configured timeout period, and there are no repeated error logs.
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.