Knowledge Base Retrieval and Recall for Thermal Industry

Data Characteristics of This Category

Public research reports from securities firm public utility research teams and local thermal industry associations are the primary source of data for this category. Updates follow a quarterly routine schedule, with supplementary new reports released within 1 to 3 business days for sudden heating pipe network adjustments or supply-demand changes during heating seasons. Each individual research report includes the report title, issuing entity, release time, core thermal indicators, regional supply-demand analysis, and policy impact interpretation. Core fields include thermal supply load (unit: megawatts), number of households covered by heating supply (unit: ten thousand households), pipe network leakage rate (unit: cubic meters per hour). Some reports include detailed parameters for regional thermal projects.

Constraints Imposed on Knowledge Base Retrieval and Recall

The multi-field, unit-inclusive characteristics of thermal research reports require matching both field names and their corresponding units during retrieval, to avoid recalling irrelevant non-thermal category reports. The update rhythm, which prioritizes quarterly updates with supplementary emergency updates, requires the knowledge base to support incremental updates and scheduled synchronization mechanisms, to prevent old data from overwriting core indicators from new heating seasons. Each single report often contains long analysis content and scattered structured indicators, so chunking must distinguish between structured fields and unstructured analysis text, to avoid damaging the integrity of indicators during chunking. Additionally, thermal supply and demand data has strong timeliness, so the recall link must prioritize sorting content from reports released within the last 3 months.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunkSize`	`800–1200 characters`	Balances the integrity of structured indicators and semantic coherence of analysis text, avoiding semantic fragmentation from too small a size and reduced recall accuracy from too large a size
`similarityThreshold`	`0.75–0.85`	Thermal research reports contain many professional terms, so this range filters low-match irrelevant content while retaining sufficient relevant results
`recallTopK`	`Top 8 entries`	Single research report content is lengthy; too many recalled entries will exceed context window limits, while too few will fail to cover multi-regional thermal data
`PARSE_FILE_TIMEOUT_SECONDS`	`120 seconds`	Some reports contain multi-page tables and long text, so sufficient parsing time must be reserved to avoid document parsing failure due to timeout mid-process
`enableFieldMatch`	`Enabled`	Thermal research reports have clear professional fields; enabling this allows precise matching of retrieval requests for specified fields, improving recall accuracy
`incrementalSyncInterval`	`Every 24 hours`	Balances synchronization needs for routine quarterly updates and emergency temporary updates; daily synchronization ensures data timeliness

The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Symptom: The knowledge base retrieval returns a 504 Gateway Timeout error, or the interface displays "Search timed out". Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter was not adjusted, insufficient time was reserved for parsing long-text research reports, or the recallTopK value was set too high, leading to excessive time spent on recall and sorting.
Symptom: Chunked documents have structured indicators and analysis text split apart, or single chunk content exceeds expected length. Cause: The chunkSize was not adjusted for the long-text characteristics of thermal research reports; the default chunking logic in version 4.8.10 does not perform secondary truncation on segments exceeding the chunk length, leading to incomplete chunk semantics.
Symptom: After retrieving the knowledge base for question answering, the returned content is AI-generated text that does not correspond to the preset question-and-answer pair responses. Cause: The strictKnowledgeRetrieval parameter was not enabled, or the question-and-answer pair was not set as a dedicated recall source, leading the model to invoke general generation logic.

How to Verify Proper Configuration

Upload a test thermal research report, check the chunked content after document parsing, confirm that structured indicators and analysis text are not incorrectly split.
Initiate a retrieval request containing professional thermal terms, check the field matching degree and timeliness of the recall results, adjust the corresponding parameters until expectations are met.
Trigger an incremental synchronization task, check whether the update time of research reports in the knowledge base matches the release time of the source data, confirming that the synchronization logic is effective.
Import a preset question-and-answer pair dataset, initiate a corresponding retrieval request, confirm that the returned content is the original response of the question-and-answer pair, with no additional content generated.

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.