What the Data for This Category Looks Like
Thermal coal investment research data originates from official releases by the National Energy Administration, spot trading data from Qinhuangdao Coal Network, production capacity and shipment announcements from coal producers, delivery data from the Dalian Commodity Exchange, and third-party industry research reports. Data update cadence follows multiple tiers:
- Spot quotes update daily
- Weekly supply and demand balance sheets and port inventory data update weekly
- Monthly industry production capacity and transportation statistics update monthly
- Quarterly policies and industrial plans update quarterly
Document spans vary widely. Some are single-line spot quotes with only origin, calorific value, and price. Others are dozens of pages of industrial supply and demand analysis reports. Core fields include calorific value (unit: kilocalories per kilogram), price (unit: yuan per ton), origin, port, inventory days, transportation distance, and more. Minor format differences exist for fields across different data sources.
Constraints on Context and Token Management
The multi-source, multi-format, and varying update frequencies of thermal coal data create specific constraints for context and token management. First, document lengths vary drastically across sources. Single-line quotes and long research reports exist side by side. Without properly configured chunking parameters, short text redundancy or long text truncation can occur. Second, frequently updated spot data requires regular knowledge base refreshes. If context caches are not updated promptly, recalled content will lag behind market changes. Additionally, investment research scenarios require recalling multi-dimensional data such as spot, futures, and policy data simultaneously. Total token consumption for recalled content is high. Without limiting the number of recalled entries, the model’s context window upper limit will easily be exceeded, leading to request failures.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the average content density of thermal coal spot quotes, supply and demand balance sheets, and research report snippets, balancing information retention and token consumption |
chunkOverlap | 100–200 characters | Preserves critical cross-segment linked information, such as the relationship between calorific value and price, or the correlation between policy release time and price fluctuations |
recallTopK | 8–12 entries | Covers the multi-dimensional data sources required for thermal coal investment research, preventing single recalled entries from being too long and causing token overflow |
rerankTopN | 4–6 entries | Filters the most relevant segmented data, such as core indicators including Qinhuangdao Port flat price and northern Shanxi pit price, reducing redundant token usage |
maxContextWindow | 70%–80% of the model's native context window | Reserves sufficient tokens to carry user questions and system prompts, preventing core business information from being truncated in the context |
AIPROXY_API_TOKEN | Key string bound to the deployment environment | Ensures identity legitimacy for proxy requests, preventing unauthorized access and token abuse |
AIPROXY_API_ENDPOINT | Proxy address corresponding to the deployment node | Points to the correct model service node, ensuring proper token request routing |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The model returns a context truncation prompt, or investment research results are missing port price data for thermal coal from the past 7 days. Cause:
maxContextWindowis not configured according to the model's native context window ratio, and insufficient reserved tokens cause core data to be truncated. - Symptom: Too much content is recalled from the knowledge base, causing the model to reject the request and return a
400 Bad Requesterror. Cause: The values ofrecallTopKandrerankTopNare not limited, causing total context tokens to exceed the model's input upper limit. - Symptom: Errors related to
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENoccur during local deployment, preventing normal model calls. Cause: The proxy configuration is not bound to the actual deployed model service node, leading to incorrect token request routing or identity verification failure.
How to Verify Proper Configuration
- Submit a test document containing thermal coal spot quotes and supply and demand balance sheets, check that all core fields and units are retained in the chunked content, with no obvious content truncation.
- Send a test request containing multi-dimensional thermal coal investment research questions, confirm that the number of recalled context entries matches the configured
recallTopKparameter. - Review the model call logs, confirm that the total input token count does not exceed the set proportion of the model's native context window length.
- Verify the validity of the proxy configuration, send a test request to confirm there are no log outputs related to identity verification or routing errors.
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.