What this category of data looks like
Gas industry investment research data comes from upstream gas supply quotation platforms, pipeline network operation and maintenance logs of urban gas enterprises, gas price adjustment policy documents from the National Development and Reform Commission and the Ministry of Housing and Urban-Rural Development, and supply and demand analysis reports from gas industry associations. Update rhythms vary significantly: gas source quotations are updated daily, pipeline inspection records are updated weekly or in real time, policy documents are released irregularly, and annual industry reports are updated quarterly or annually. Document structures fall into three categories: structured supply and demand reports (with dedicated fields such as gas supply pressure, calorific value, and pipeline segment corrosion rate), semi-structured policy notices, and unstructured operation and maintenance logs and analysis documents.
What constraints do these characteristics impose on model integration and configuration?
The characteristics of gas investment research data impose multiple constraints on model integration and configuration: Differentiated update rhythms of multi-source data require configuring differentiated incremental synchronization trigger mechanisms. This avoids resource waste from high-frequency synchronization or data obsolescence from low-frequency synchronization. Coexistence of structured reports and unstructured logs requires configuring adaptive parameters for both structured field extraction and non-semantic segment parsing. Dedicated terms such as "station pressure" and "gas calorific value" require the model to have domain semantic coding capabilities. This prevents recognition bias from general-purpose models. Large documents such as monthly operation and maintenance logs have significant length. This requires adjusting parsing timeout and segment length parameters to prevent parsing failures.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_batch_size | 16–32 items/batch | Gas data includes structured numerical values and long-text logs. Excessively large batches cause memory overflow, and excessively small batches reduce parsing efficiency |
rerank_top_k | Top 8–12 items | Gas investment research needs to balance multiple types of information including gas source quotations, pipeline network data, and policy documents. Excessive values increase inference delay, while insufficient values lose critical associated data |
similarity_threshold | 0.72–0.80 | Semantic similarity of gas-specific terms has high differentiation. A threshold that is too low introduces irrelevant data, while a threshold that is too high misses relevant interpretations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single large gas enterprise annual report or monthly operation and maintenance log can reach tens of thousands of characters. A timeout causes parsing failure |
maxContext | 8000–12000 characters | Gas investment research needs to integrate multi-source data context. Excessively long values exceed the model's context window limit |
UPLOAD_FILE_MAX_SIZE | 500 MB | Single gas pipeline network topology diagrams or batch inspection reports have large file sizes. An overly large limit may cause excessive service load |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration mistakes
- Phenomenon: System logs show that recall results contain parsed data from multiple models simultaneously, and field units are mixed. Cause: The responsibility boundaries of multiple models are not clearly defined, and the data source parsing logic is mixed between problem optimization models and knowledge base reference models.
- Phenomenon: A
413 Request Entity Too Largeerror is returned when uploading gas inspection logs. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the default value is insufficient to accommodate a single large inspection report. - Phenomenon: The Embedding model's returned results fail to recognize dedicated terms such as "gas calorific value" and "pipeline segment corrosion rate". Cause: An Embedding model that supports domain fine-tuning is not selected, and general-purpose models have insufficient semantic coding accuracy for industry-specific terms.
How to verify successful configuration
- Upload a gas document containing both structured reports and unstructured logs, and check whether the units of the parsed fields match the original document.
- Initiate an investment research query, and check whether the number of recalled documents in the system logs matches the configured value of
rerank_top_k. - Test recall results under different thresholds, compare the recognition accuracy of professional terms, and adjust
similarity_thresholdto a range that meets business requirements. - Test the incremental synchronization function, and verify whether newly added gas source quotation data can be recalled within the time matching the business update frequency.
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.