What the data for this use case looks like
Data for thermal energy intelligent due diligence reports primarily comes from monthly operational ledgers of thermal energy operating enterprises, real-time pipe network monitoring data, records from heating billing systems, filing documents from government housing and urban-rural development departments, and annual audit reports. Update frequency falls into three categories: Pipe network operating parameters are synced hourly, monthly operational data is updated by the 5th of each month, and annual compliance and audit reports are updated in the first quarter of each year.
Document structure includes basic enterprise qualification fields (such as heating area, total pipe network length), operating parameter fields (heat supply quantity, heat exchanger station pressure, units are gigajoules and megapascals respectively), billing user data, operation and maintenance complaint records, and compliance filing numbers. The text length of a single complete report varies widely, ranging from several thousand words to over one hundred thousand words.
Constraints on vector models and indexing
The multi-source heterogeneity, differing update frequencies, wide text length range, and unit-attached numerical field characteristics of thermal energy category data impose multiple constraints on the vector models and indexing link.
Multi-source data includes structured operating parameters, unstructured operation and maintenance records, and long-document audit reports. Different vector encoding logic must be adapted to avoid vector space misalignment between structured numerical values and natural text.
Coexisting high-frequency real-time pipe network data and low-frequency annual reports requires the index to support flexible switching between incremental and full updates, reducing repeated calculation costs.
The wide text length range requires a segmentation strategy that can adjust dynamically based on content type, preventing key parameters in long documents from being truncated.
Unit-attached numerical fields must retain unit information during the preprocessing stage, preventing vector confusion of different magnitudes for similar parameters.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the text span of thermal energy reports, from short operation records to long audit reports, balancing segmentation integrity and vector recall accuracy |
embedding_mode | Hybrid encoding | Requires generating vectors for both unit-attached structured numerical fields and unstructured natural text. Hybrid mode can adapt to multi-type data characteristics |
index_update_strategy | Incremental real-time update + monthly full verification | Pipe network operating parameters require high-frequency synchronization. Annual compliance reports can be updated fully on a fixed cycle, reducing repeated calculation costs |
recall_top_k | Top 10–15 results | Thermal energy due diligence requires recalling multi-dimensional information such as heat supply quantity, pipe network pressure, and compliance records. This range covers core retrieval needs |
similarity_threshold | 0.75–0.85 | Filters irrelevant results from thermal energy parameter vector recall, matching the relevance judgment standard of the business scenario |
metadata_index_fields | Heating area, total pipe network length, filing number | These fields are core retrieval dimensions for thermal energy due diligence. Using them as metadata indexes enables precise positioning |
The parameter values provided on this page are conventional recommendations used to establish a starting point for configuration. 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 Configuration Mistakes
- Scenario: After indexing hundreds of thousands of thermal energy data entries in the knowledge base, search tests return insufficient or empty results. Cause:
metadata_index_fieldsis not configured, so vector recall is not associated with core retrieval dimensions of thermal energy reports, and cannot match user query keywords. - Scenario: Key parameters (such as total pipe network length) in long-text audit reports are truncated and lost. Cause:
chunk_sizeis set too small, failing to adapt to the text length of long documents, resulting in complete parameter fields being split during segmentation. - Scenario: Duplicate indexed pipe network operating data appears during incremental updates. Cause: No unique identifier field is configured as the verification basis for incremental updates, resulting in repeated synchronization of the same monitoring data.
How to Verify Correct Configuration
- Perform a full index test, check the segmentation log, and confirm that key parameter fields are not truncated during segmentation of long-text audit reports.
- Initiate a retrieval test for specific thermal energy parameters (such as heat supply quantity, filing number), and verify that recall results include target fields and meet the configured similarity judgment standard.
- Trigger an incremental update task, check the index monitoring panel, and confirm that only newly added pipe network operating data is synchronized, with no duplicate index entries.
- Export vector encoding logs, check that unit-attached numerical fields retain unit information, and no vector space misalignment occurs.
- Confirm that the currently deployed FastGPT version is V4.8.20-FIX2 or later, ensuring compatibility with indexing functions.
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.