What this type of data looks like
Data sources for power grid equipment investment research include industry whitepapers published by the China Electricity Council, public product manuals from power grid equipment manufacturers, operation and maintenance logs from provincial grid companies, and real-time quotation data from electricity spot trading platforms.
Update cycles fall into three categories: product manuals are updated quarterly, operation and maintenance logs are synchronized in real time, and industry whitepapers are updated 2 to 3 times per year.
Document structures are divided into three types: structured parameter tables, unstructured operation logs, and bidding documents. Structured fields include equipment model, rated capacity, insulation rating, and service life, with units such as kVA, %, and years.
Constraints on model access and configuration
Structured parameter tables have numerous fields with strict unit requirements. Configure unified field verification and unit conversion rules during model access to avoid parameter matching errors.
Real-time operation data updates require context retrieval to support real-time data sources. Adjust retrieval rules to adapt to frequently updated content.
Single bidding documents and bulk equipment manuals have long total lengths. Adjust document segmentation and context window parameters to avoid parsing timeouts or truncated key content.
Data formats vary widely across different sources. Configure unified field mapping rules to ensure the model can accurately distinguish device parameters from different data sources.
Queries in investment research scenarios often involve multi-dimensional parameter combinations. This places higher requirements on the model's context understanding and concurrent processing capabilities. Adjust model deployment and retrieval parameters accordingly.
How to configure
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Total length of a single power grid equipment product manual and operation log typically falls within this range, preventing context overflow and loss of key information |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Adapts to upload requirements for single large bidding documents or bulk equipment manuals, avoiding blockage due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Long document parsing requires extended processing time, preventing parsing interruptions due to timeout |
RECALL_TOP_N | Top 8–12 results | Balances retrieval needs for multi-dimensional device parameters and latest operation data, while moderating model load and ensuring information completeness |
SIMILARITY_THRESHOLD | 0.75–0.85 | Structured parameter matching requires high similarity, avoiding incorrect retrieval of data from non-target devices |
vllm.max_batching_tokens | 4096–8192 | Addresses concurrent operation data query requests, balancing GPU memory usage and processing speed |
MODEL_PROVIDER | Only access domestic models | Adapts to domestic data source requirements for power grid investment research, eliminating need to call overseas model APIs |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Request queuing timeout occurs and response speed fails to meet expectations when processing bulk device parameter queries after vLLM deployment. Cause: The
vllm.max_batching_tokensparameter was not adjusted. The per-batch token count is too low, failing to fully utilize GPU memory for concurrent request processing. - Phenomenon: An investment research robot built via a third-party platform cannot respond to power grid equipment-related queries, and the interface displays no matching results. Cause: Correct retrieval scope was not configured. Power grid equipment-specific data sources were not included in the retrieval pool, and only generic knowledge base content was called.
- Phenomenon: When extracting SQL from power grid equipment structured data, the generated statement only removes \n characters without adding supplementary spaces, causing keywords and parameters to merge and fail to parse, returning a syntax error prompt. Cause: Prompt engineering for SQL generation targeted at structured data was not configured. The model was not instructed to add necessary spaces when replacing newline characters.
How to confirm successful configuration
- Upload a single typical power grid equipment document, verify the completeness and unit accuracy of parsed fields, and adjust relevant parsing parameters until expectations are met.
- Initiate multiple concurrent queries, observe response latency and queue status, and adjust concurrency-related configurations to meet business requirements.
- Submit a structured data query containing newline characters, verify that the generated SQL statement format is compliant, and confirm that the newline character processing rule is active.
- Switch to a domestic model service provider, verify successful interface calls, and confirm that overseas model access parameters are not used.
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.