What the Data for This Category Looks Like
Energy metal investment research data primarily comes from industry association public reports, futures exchange spot price quotes, mining company financial reports, and third-party industry news platforms. Update frequencies cover real-time dynamics (such as same-day spot prices), daily closing data, quarterly financial reports, and annual industry plans. Document types include three categories: structured price sheets, special research reports, and industrial chain maps. Structured sheets contain fields such as product, specification, origin, and price, with units mostly yuan/ton, ten thousand tons, ten thousand tons/year. Research reports are mostly long texts combining text and images. Industrial chain maps contain upstream and downstream node association information.
Constraints Imposed on Context and Token Processing
The data characteristics of the energy metal category impose multiple constraints on context and token processing workflows: Multiple fields and units in structured price sheets increase token consumption, requiring standardized processing. Coexistence of long-text research reports and short-text spot data requires context windows to support information carrying for both long and short texts. Frequently updated real-time data demands strict recall timeliness thresholds to prevent expired information from occupying tokens. A large number of industrial chain association nodes means an overly small context window will lose critical cross-linkage information, leading to broken investment research logic.
How to Configure Parameters
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
RECALL_CHUNK_SIZE | 800–1200 characters | Matches the segmentation needs of energy metal spot short texts and research report long texts, avoids excessively long single segments exceeding token limits, and retains field association information |
maxContextToken | 8000–12000 tokens | Adapts to total token usage of a single research report plus multiple sets of spot data, prevents context overflow that causes interrupted responses |
RECALL_TOP_N | Top 6–8 entries | Balances token consumption and information coverage, covers multi-dimensional investment research data such as supply and demand, prices, and inventory |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance data from non-energy metal categories, reduces invalid token consumption |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing duration of large industrial chain map PDF files, prevents segmentation failures caused by timeouts |
UPLOAD_FILE_MAX_SIZE | 500 MB | Allows uploading large-capacity industry yearbooks and full industrial chain database files |
Three Common Misconfigurations
- Phenomenon: When calling a local energy metal investment research model, an error log of
failed to get token encoderis returned, and no response can be generated. Cause: The tokenizer file path of the local model is not configured correctly, or the parameter values do not adapt to the model input format in the energy metal scenario, resulting in token encoding failure. - Phenomenon: After private deployment, the application's context configuration interface cannot be accessed via the
/fastgptpath, and a 404 status code is returned. Cause: The reverse proxy configuration does not correctly map requests with the/fastgptprefix to the FastGPT service port, resulting in path matching failure. - Phenomenon: After uploading an energy metal industry research report, the parsed segmented content has truncated fields or missing units. Cause: The
RECALL_CHUNK_SIZEparameter is not set to a reasonable range, which destroys the integrity of key fields and units during long text segmentation, wasting tokens and reducing recall accuracy.
How to Verify Correct Configuration
- A typical energy metal spot price sheet is uploaded, and the parsed segmented content is reviewed to confirm fields and units are complete, and segment lengths align with configured requirements.
- A question and answer related to energy metal investment research is initiated, and the number of recalled context entries in returned results is checked to confirm alignment with the configured
RECALL_TOP_Nvalue. - FastGPT service logs are reviewed to confirm there are no errors related to
token encoder, and the local model's token processing workflow is verified as operational. - Access to the configuration interface via the
/fastgptpath is tested, reverse proxy forwarding is confirmed as functional, and no 404 path errors are present.
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. Testing on own samples is recommended before finalizing settings.
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.