What the Data for This Category Looks Like
Sources of energy metals due diligence data include public industry association reports, commodity exchange market data, production financial reports released regularly by mining enterprises, and third-party commodity information platforms. Update cycles are split into multiple tiers:
- Spot prices are updated daily
- Monthly supply and demand balance sheets are updated weekly
- Quarterly exploration and production capacity data is updated monthly
- Annual financial reports are updated quarterly
The page count of individual due diligence reports varies widely. Values should be confirmed based on in-house sample statistics or actual testing. Reports are divided into five modules: spot market trends, inventory data, industrial chain price transmission, enterprise production capacity data, and policy developments. Each module includes structured tables and unstructured analysis text.
Structured fields include grade, production capacity, inventory, quotation, and output. Some overseas data sources use imperial units. Adaptation and conversion of these units are required.
Constraints on Multi-turn Dialogue and Prompt Engineering
The multi-source update cycles and heterogeneous field characteristics of energy metals due diligence data create multiple constraints for multi-turn dialogue and prompt engineering. First, high-frequency data such as spot prices requires real-time data source calls. Static knowledge base storage alone is insufficient. Multi-turn dialogue must support triggering data refreshes at any time. Second, individual reports have a high page count and inconsistent field units. Prompt engineering must limit the context recall range to avoid long text exceeding token limits. It must also explicitly require conversion to units commonly used in domestic markets. Additionally, users often ask follow-up questions across modules. Multi-turn dialogue must retain module association identifiers for context. This avoids confusion between field meanings from different data sources.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Top 30 Context Entries | Matches the configured context upper limit, adapts to the context association needs of cross-module follow-up questions in energy metals due diligence, and avoids loss of associated information during cross-module follow-up questions due to insufficient context entries |
system_prompt | Explicitly specify unified unit conversion rules, limit the scope of recalled document modules, and prioritize calling real-time data sources | Adapts to the characteristics of heterogeneous fields and high-frequency updates of energy metals due diligence data, avoids unit confusion and outdated static data |
retrieval_top_k | 5-8 Entries | Balances recall accuracy and token usage. Structured tables and analysis text in energy metals due diligence reports require precise matching. Excessive recall will cause context overflow |
similarity_threshold | 0.75-0.85 | Filters low-correlation industry data, prevents energy metals data from non-target categories from being mixed into dialogue context |
api_request_timeout | 600 Seconds | Adapts to the interface response delay of third-party commodity data sources, avoids dialogue interruptions caused by timeouts |
file_parse_chunk_size | 800-1200 Characters | Adapts to the long-text structure of energy metals due diligence reports, avoids context association breaks caused by overly fine text splitting, or token overflow caused by overly coarse splitting |
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 case-by-case analysis. Values should be validated against in-house test samples before finalization.
Three Common Misconfigurations
- Symptom: In FastGPT 4.10.0, after configuring
maxContextto 30 entries, conversation details only display 2 context entries, and replies cannot associate historical input. Cause: The context passing node is not correctly bound to the AI node in the workflow, or redundant context filtering rules are enabled, which accidentally truncates historical context. - Symptom: The prompt cannot recognize spaces in input, leading to incorrect unit parsing for energy metals. Cause: The system prompt does not explicitly require retaining the space format of input text, or a preprocessing step configured in the workflow automatically removes spaces.
- Symptom: When calling the application via API, conversation logs are lost and cannot be recovered. Cause: No persistent volume is mounted in the Docker configuration, or the
LOG_PERSISTENCE_PATHparameter is not set. Log files are automatically cleared after the container restarts.
How to Verify Proper Configuration
- Initiate multi-turn follow-up questions, confirm whether AI replies associate the category and data module from historical input.
- Submit a query with mixed units, confirm that the AI reply completes unit conversion correctly.
- Call the API to submit a session request, confirm that returned results include complete
conversation_idand historical context fields. - Review Docker container logs, confirm that conversation logs are continuously written to the specified persistent path, and no log loss errors appear.
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.