What this category of data looks like
Source data for power industry investment research covers multiple categories: real-time operation logs from grid dispatching systems, daily/hourly quotation and transaction data from power trading centers, operation and maintenance records of generating units, industry policy documents and professional research reports.
Update frequencies vary significantly. Real-time operation data updates every second or minute. Transaction data updates per trading session. Policies and research reports update irregularly.
Document structures include structured Excel/CSV tables (such as unit parameter tables, transaction daily reports), semi-structured log files, and long-form industry analysis reports. Professional fields include active power (unit MW), reactive power (unit Mvar), electricity price (unit yuan/megawatt-hour), equipment number, operation duration, and other specialized attributes.
What constraints do these characteristics impose on the context and token stage
Real-time and high-frequency updated data leads to frequent changes in source data versions. This requires frequent synchronization and updates to the knowledge base, increasing token consumption during context splicing.
Structured tables contain multiple field attributes, with high information density per single record. If not segmented properly, single segments will exceed token limits. Long-form research reports and multi-dimensional data splicing significantly increases overall context length, which may exceed the token upper limit of large models.
Additionally, power data has many specialized fields. If matching is not precise during recall, irrelevant data will be included, further wasting token resources.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Power data has many fields per structured record, and long-form research reports account for a high proportion. This range adapts to the context length after multi-field splicing |
chunkSize | 800–1200 characters | Single records of power operation logs and transaction data have dense fields. Too short a segment will split parameter associations, while too long a segment will exceed token limits |
recallTopK | Top 3–5 entries | Power investment research requires associating multi-dimensional data (such as unit parameters + transaction quotations). Too few entries fail to cover relevant dimensions, while too many consume excessive tokens |
AIPROXY_API_ENDPOINT | Fill in a dedicated node address based on the deployment environment | Dedicated proxy nodes adapted to data compliance requirements of the power industry, ensuring secure data transmission |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Power industry Excel source data often contains tens of thousands of rows, with long parsing times. This avoids timeout interruptions |
similarityThreshold | 0.75–0.85 | Power data fields have high professionality. Precise matching is required to avoid recalling irrelevant equipment operation and maintenance logs or policy documents |
The parameter values provided on this page are common recommended 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 conduct testing on local test samples before finalizing settings.
Three common mistakes
- Uploading a 10,000-row power transaction Excel file triggers the
E11000 duplicate key error collectionerror. The cause is failure to adjustchunkSize, resulting in duplicate primary keys in generated document segments after segmentation, or failure to properly configure data deduplication rules. - Context truncation occurs when launching a power investment research association query, with key power parameters missing from the response. The cause is that
maxContextis set too small to accommodate the spliced multi-field power data context. - Incorrect proxy address or secret key is filled when configuring
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. The cause is failure to verify the dedicated node information in the deployment documentation, resulting in failure to call the large model interface normally.
How to confirm correct configuration
- Upload a single fragment of power operation log Excel file, check the number of parsed segments to match the
chunkSizevalue, and confirm that segment lengths meet expectations. - Launch a test query that associates multi-dimensional power data, check the completeness of spliced fields in the returned context, and confirm that
maxContextadapts to the data volume. - Verify that the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches the deployment documentation, and launch a test call to confirm interface connectivity. - Import more than 10,000 rows of power transaction data, check the parsing progress and timeout prompts, and confirm that
PARSE_FILE_TIMEOUT_SECONDSis set reasonably.
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.