What the data for this category looks like
Data sources for coal chemical industry intelligent due diligence reports include industry regulatory authority public disclosure documents, enterprise production and operation ledgers, third-party test reports, and upstream and downstream trade contracts. Update cadence falls into three categories: production data updated monthly, compliance data updated quarterly, and annual comprehensive reports updated annually.
Document structure includes project filing information, capacity accounting sheets, raw material and energy consumption details, pollutant emission monitoring data, upstream and downstream cooperation ledgers, and compliance rectification records.
Fields include production scale, energy consumption per ton of product, wastewater discharge volume, sulfur dioxide emission concentration, and others. Some fields have clear physical units, and there are enumerated compliance status fields.
Constraints on Vector Models and Indexing
Mixed structured and unstructured data structures mean vector models must support vectorization of multi-dimensional fields, and indexes must support structured metadata filtering.
Differences in update cadence across data types call for configuring incremental index trigger rules to avoid wasted computing resources from full index recomputation.
Fields with clear physical units and enumerated values require retaining unit and enumeration semantics during vector retrieval. This prevents matching deviations caused by semantic confusion in the vector space.
The length of individual reports varies widely. When splitting text, retain upstream and downstream process-related context to avoid semantic fragmentation that affects retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Coal chemical due diligence reports contain process-related text. This length preserves process logic integrity within a single segment and avoids semantic fragmentation |
Vector Model Normalization Switch | Enabled | This configuration is available in version 4.8.23 and above. It adapts to the output format of unnormalized embedding models and improves matching accuracy for professional terms |
Recall count | Top 10–15 entries | Coal chemical reports have many associated fields. A sufficient number of recalled related segments is needed to cover multi-dimensional information including process, compliance, and energy consumption |
Similarity threshold | 0.72–0.80 | Coal chemical reports contain many professional terms. A threshold that is too low introduces irrelevant matches, while a threshold that is too high may miss relevant compliance or energy consumption data |
Incremental Index Update Frequency | Every 6 hours | Balances index update overhead and timeliness requirements for production and compliance data |
maxContext | 4000–6000 characters | The total length of core associated segments of a single coal chemical due diligence report is large. This ensures recalled content can cover a complete process logic chain |
The parameter values given on this page are common starting points 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
- Phenomenon: After retrieving matching segments, the large language model reports no relevant content found. Cause: The
maxContextparameter is not configured to a sufficient length, and the retrieved segments are not fully passed into the large language model context, causing the model to fail to read the matching content. - Phenomenon: Knowledge base query response delay is high, and logs show that context token usage exceeds expected levels. Cause: The total token count of retrieved segments is not limited, or the
Recall countvalue is too high, leading to excessive redundant content being passed in each retrieval. - Phenomenon: Vector retrieval matching results have significant semantic deviations from professional terms. Cause: The
Vector Model Normalization Switchis not enabled, and an unnormalized embedding model is used, resulting in deviations in vector space distance calculation.
How to Confirm Proper Configuration
- Upload a single coal chemical industry intelligent due diligence report, access the segment preview interface, and confirm that segment results match the preset
Chunk sizevalue, with each segment retaining complete process or compliance logic. - Initiate a professional term or field retrieval, review the number of recalled entries and similarity matching results of the retrieval results, and confirm that recalled content covers target fields and units.
- Check index update logs to confirm that incremental indexes trigger at the preset frequency, and only update newly added or modified documents.
- Test the response content of the large language model, and confirm that returned results include professional data and compliance information from retrieved report segments, with no uncited external content.
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.