What the Data for This Category Looks Like
Data sources for gas investment research include: pipeline network operation logs, monthly gas usage reports, upstream gas supply contracts, industry policy documents, and meteorological linkage data from gas enterprises. Update frequencies fall into three categories: real-time (pipeline pressure, real-time gas usage), daily (operating revenue), and irregular (policy updates). Document structures are grouped into three types:
- Structured tables, including fields such as pipeline network ID, pipe diameter [millimeters], pressure [megapascals], and service life
- Semi-structured contracts, including fields such as gas supplier, gas supply volume [cubic meters], and signing date
- Unstructured policy documents, including fields such as document number and issuing authority
Field units uniformly use cubic meters, millimeters, and megapascals as standard in domestic scenarios. Unit conversion is required for reference documents from overseas markets.
Constraints for Database and Operations
Multi-source, heterogeneous data types require the database to support mixed storage of structured tables, semi-structured documents, and unstructured text. The database must also adapt to data access in different formats. Different update frequencies require the operations link to configure both real-time incremental sync tasks and scheduled batch sync tasks. This setup covers both real-time data and periodically updated documents. Complex field structures require the database schema to support dynamic expansion. This avoids restricting data access via hard-coded fields. The professional nature of gas industry data requires adding term verification and format conversion steps in the operations link. This ensures data consistency. Security and compliance requirements mandate desensitization processing for pipeline network data containing user privacy. Unauthorized access to this data must also be restricted.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Gas investment research reports often include large pipeline network topology diagrams and quarterly operation summary reports, requiring support for large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing long-term operation logs and annual industry analysis documents requires longer processing times |
Recall count | Top 8-12 results | Gas investment research needs to balance both detailed pipeline network data and macro policy interpretation. Too many recall results will cause redundant context |
Similarity threshold | 0.72-0.78 | The terminology of the gas industry is highly professional. A threshold that is too low will introduce irrelevant general energy documents, while a threshold that is too high will lose valid matching results |
DB_INCREMENT_SYNC_INTERVAL | 300 seconds | Real-time gas usage data requires high-frequency synchronization. Non-real-time data such as policies and contracts can be synchronized daily. Taking the middle value covers most scenarios |
EMBEDDING_BATCH_SIZE | 16-32 | Gas industry data has high vector dimensions. An overly large batch size will cause memory overflow. This range is compatible with most mid-range hardware configurations |
The parameter values provided on this page are all conventional recommendations, used as starting points for configuration determination. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: A
database schema mismatcherror appears after starting the service, and gas investment research data cannot be loaded. Cause: Database migration scripts between versions were not executed during cross-version upgrades, and the image was replaced directly, resulting in incompatible new and old table structures. - Issue: Knowledge base search returns a delay exceeding 30 seconds, and the vector recall phase logs show
CUDA out of memory. Cause:EMBEDDING_BATCH_SIZEwas not adjusted. The RTX2070 has limited video memory, and an overly large batch size causes memory overflow. Local caching for vector recall was not enabled. - Issue: After triggering a tool call, knowledge base matching results are not returned first, and no preset fallback answer is returned when there are no matches. Cause: The
RAG_RESPONSE_POLICYparameter was not configured correctly, and the fallback answer was not bound to the scenario where no knowledge base matches are found.
How to Confirm Correct Configuration
- Execute the database incremental sync task. Verify that the synchronized fields match the preset gas industry data schema. Confirm that the configured sync interval aligns with the data update frequency.
- Upload a typical gas industry operation log document. After parsing completes, check the service logs. Confirm no timeout errors appear, and verify alignment with the
PARSE_FILE_TIMEOUT_SECONDSconfiguration. - Initiate a test query with no matching knowledge base content. Check that a preset fallback answer is returned. Confirm that the
RAG_RESPONSE_POLICYconfiguration is correct. - Initiate a test query containing gas industry professional terminology. Check that the number of recall results matches the configured
Recall count. Confirm that the similarity threshold setting meets business requirements.
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.