What the data for this category looks like
Coal chemical industry-related data mainly comes from enterprise manufacturing execution systems, enterprise resource planning systems, and third-party public industry databases. There are three update frequencies: real-time production data updates every hour, sales ledger data updates daily, and public industry data updates every quarter. Documents are mostly structured tables or CSV files. Some on-site data exists as PDFs converted from paper scans. A single data record contains 15 to 20 fields including origin, purchase batch, process parameters, product output, and others. Each field is bound to a clear physical unit, such as degrees Celsius for reaction temperature, megapascals for reaction pressure, and tons for product output.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The update frequency of real-time data requires multi-turn dialogue to limit the valid time range of context. Expired production parameters will reduce the accuracy of marketing content, so this restriction is necessary. Many fields are bound to clear physical units. Prompts must explicitly require the model to identify and verify unit consistency. This prevents parameter matching errors. Data sources are scattered. Multi-turn dialogue must first guide users to clarify the data source type for the current conversation. This avoids confusion between production data and sales data. Marketing content needs to combine specific product parameters. Multi-turn dialogue must gradually refine user needs. Start with broad product categories, then move to specific batch parameters. This ensures generated marketing content accurately matches user scenarios.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Coal chemical data has many fields. Multi-turn dialogue must retain multi-round parameter requirements and historical data to avoid information loss from context truncation |
UPLOAD_FILE_MAX_SIZE | 500 MB | Files such as coal chemical production ledgers and sales reports usually contain multiple batches of historical data and are large in size. This setting must support large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large structured files take longer to parse multiple fields of data. This prevents parsing failures due to timeout |
Number of Recalled Entries | Top 8–10 entries | Coal chemical marketing content must combine specific product parameters. Too many recalled entries will cause context redundancy, while too few will fail to cover user needs |
Similarity Threshold | 0.75–0.85 | Precise matching of user product parameter requirements is needed. This avoids irrelevant historical data interfering with generated results |
maxToken | 2000–3000 characters | Marketing content must include detailed parameter descriptions and solutions. Overly long generated content will cause information redundancy |
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. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Issue: In a containerized deployment environment, uploading coal chemical production ledgers or sales reports via the conversation window produces no recognition results and no error logs. Cause: The temporary directory required for file parsing was not mounted during containerized deployment. Parsed file data cannot be read.
- Issue: In the v4.6.9 advanced orchestration flow, after configuring a judgment node, subsequent AI dialogue nodes cannot obtain the initial user question. Cause: The output variable of the judgment node was not bound to the context input parameter of the AI dialogue node.
- Issue: After configuring a file link variable in the setup, associated marketing material documents cannot be correctly called during dialogue. Cause: The call format for the file link variable was not explicitly specified in the prompt. The model cannot recognize the variable placeholder.
How to confirm configurations are set correctly
- Upload a coal chemical production ledger CSV file. Check if the parsed fields include preset parameter types and units. Verify field matching accuracy.
- Initiate multi-turn dialogue. Successively put forward product parameter requirements for different batches. Check if the context retains the previous round's parameter requirements.
- Adjust the number of recalled entries and similarity threshold parameters. Initiate a test dialogue. Check if the quantity and relevance of returned marketing materials meet expectations.
- Configure an advanced orchestration flow. Trigger a judgment node branch. Check if subsequent AI dialogue nodes can correctly obtain the initial user question.
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.