What data for this category looks like
Thermal energy industry financial report data primarily comes from publicly disclosed periodic reports (quarterly, annual) of enterprises and internal operation ledgers. Data sources include stock exchange public disclosure platforms and official enterprise announcements. Update frequency aligns with report disclosure cycles, with concentrated updates on a quarterly and annual basis. Daily operational data serves only as supplementary content for financial reports. Each individual financial report document includes two parts: structured financial tables and written analysis. Core fields include current heat supply volume, unit heat production cost, revenue composition ratio, pipe network loss rate, and others. Fields are accompanied by clear measurement standards and units. Document structure follows public utility industry disclosure templates consistently.
What constraints do these characteristics impose on vector models and indexing?
The structured fields and fixed disclosure structure of thermal energy financial reports require vector models to adapt to technical terminology and semantic connections of structured numerical values, to avoid vector deviation caused by missing units. Fixed disclosure cycles mean indexes do not need real-time refreshing, and batch offline update indexing strategies can be adopted to reduce vector database load. The structured multi-field feature supports metadata filtering, which can narrow the retrieval scope through fields such as report period and enterprise type, reducing invalid vector queries. At the same time, clear chapter division exists in individual financial reports, so segmented indexing must match disclosure chapters to avoid semantic breaks affecting retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
segment length | 800–1200 characters | The content length of individual chapters in thermal energy financial reports falls within this range. Segmentation preserves complete business semantics and avoids confusion of information across chapters |
recall count | 10–15 entries | Thermal energy financial report analysis needs to cover multiple modules such as financial indicators, operational data, and cost analysis. Sufficient recall volume ensures that key information is not missed |
similarity threshold | 0.75–0.85 | There are a large number of technical terms in the thermal energy industry. A higher threshold filters irrelevant report fragments and improves retrieval accuracy |
metadata filter toggle | Enabled | Thermal energy financial report analysis often requires filtering data by report period and enterprise region. Enabling this reduces invalid queries to the vector database |
batch index size | 50–80 documents | Individual thermal energy financial report documents have moderate size. This batch size balances indexing efficiency and server memory usage |
The parameter values provided on this page are general recommendations for establishing configuration baselines. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Vector database query logs frequently show identical query statements, and interface response latency is high. Query caching mechanisms are not enabled, so identical financial report keyword queries regenerate vectors and perform retrieval each time, leading to resource waste and increased latency.
- After uploading batch thermal energy financial reports, training task status remains stuck on pending for a long time, and index construction cannot be completed. The trigger logic for single-data incremental import and batch financial report indexing is confused. Collection add data only applies to small-volume text. Batch financial report import must be triggered via training orders.
- After deployment, the knowledge base cannot load financial report data, and container logs return a
connection refusederror. mongodb, vector database, and sandbox images are not started simultaneously. The sandbox is responsible for document parsing and text extraction. Missing the sandbox prevents normal vectorization of data.
How to confirm configurations are properly set
- Upload a single quarterly financial report document of a thermal energy enterprise, check that the parsed segments are split according to disclosure chapters, and that segment length falls within the configured 800–1200 character range.
- Enter the same thermal energy financial report query keyword twice, compare the response times of the two queries, and confirm that the second response time is significantly shorter to verify that the cache configuration is active.
- Access the vector database management panel, confirm that the number of indexed documents matches the number of uploaded financial reports, and that metadata fields (report period, enterprise name) have been correctly extracted.
- Adjust the similarity threshold to 0.7, retrieve financial report content from non-thermal energy industries, confirm that results are filtered, and verify that the threshold configuration is active.
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.