What the data for this category looks like
Coking coal investment research data mainly comes from daily market data of domestic futures exchanges, weekly supply and demand monitoring reports from industry associations, monthly production, sales and inventory data from production sites and ports, and public production and sales announcements from listed coal enterprises. Data update frequencies cover daily, weekly and monthly cycles. Documents include structured indicator tables and semi-structured industry analysis texts. Core fields include ash content, sulfur content, volatile matter, caking index, delivery grade, and port inventory. Corresponding units are percentage, grams per mole, tons, and others.
What constraints these characteristics impose on model integration and configuration
Multiple update frequencies require layered synchronization strategies to avoid excessive server resource usage from full data pulls. Mixed-structured documents require categorized parsing rules: extract fields from structured tables, anchor keywords in semi-structured texts to ensure core indicators are not missed. Dense specialized terminology requires custom term library configuration to ensure the model accurately identifies coking coal-specific metrics. Fields with clear units require unit validation rules to prevent unit confusion or omission in model outputs.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
OLLAMA_API_BASE | http://{local deployment IP}:11434/v1 | Adapts to Ollama's default OpenAI-compatible API port and path |
maxContext | 8192–16384 tokens | Single coking coal research report text length ranges from 5000–12000 characters, matching context window token count |
PARSE_TABLE_STRUCTURE | Enabled | Coking coal investment research data mostly includes structured indicator tables; enabling this allows accurate field information extraction |
SYNC_INCREMENT_INTERVAL | 1–24 hours | Layered configuration based on data update rhythm: 1 hour for daily market data, 24 hours for monthly reports |
CUSTOM_TERM_ENABLE | Enabled | Coking coal has exclusive specialized terminology; enabling this adds terms to the custom dictionary to avoid model misidentification |
SIMILARITY_THRESHOLD | 0.75–0.85 | Coking coal specialized terms have high semantic similarity; raising the threshold filters irrelevant recall content |
The parameter values provided on this page are common recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test against your own samples before finalizing.
Three common configuration errors
- Symptom: Extra spaces appear in model outputs, or letters are converted to uppercase. Cause: No format constraints are configured for the prompt template, and the default output format of the DeepSeek model deployed via Ollama does not match preset requirements.
- Symptom: Tests return a 404 status code after adding a local Ollama model. Cause: Incorrect
OLLAMA_API_BASEconfiguration, failed to point to the correct local API path, or the port is not open to external access. - Symptom: Coking coal data fields retrieved by the knowledge base lack unit information. Cause: The
PARSE_TABLE_STRUCTUREconfiguration is not enabled, and unit extraction and binding were not performed for table fields.
How to confirm the configuration is complete
- Access the model testing interface, enter coking coal specialized terminology for queries, and verify that the model output retains the original format and units.
- View the knowledge base synchronization logs to confirm that incremental synchronization tasks run according to the
SYNC_INCREMENT_INTERVALcycle. - Check the custom dictionary configuration to confirm that coking coal-specific terms have been successfully imported and take effect.
- Call the local Ollama API interface, send a test request using the configured
OLLAMA_API_BASE, and confirm that the return status code is 200.
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.