What the data for this category looks like
Engineering consulting research report data mainly comes from publicly available quotas from housing and construction authorities, technical specifications released by industry associations, internal project survey and design documents of enterprises, and cost analysis reports from third-party consulting institutions. The update rhythm of the data varies greatly: general specification documents are revised every 1 to 2 years, while project documents are updated in real time as projects are delivered. The structure of a single research report document usually includes four core modules: project overview, cost details, technical parameters, and compliance clauses. Fields include project number, cost unit (yuan/square meter, cubic meter, etc.), construction period (days), compliance standard number, etc. Units are tightly bound to fields, with no generalized vague expressions.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The data of engineering consulting research reports is scattered across multiple sources. This requires the retrieval link to distinguish the weights of different data sources to avoid prioritizing non-official outdated documents for recall. Single documents are long in content and clearly divided into modules. This requires retaining sufficient context information during segmented retrieval to prevent losing module relevance after splitting. The feature that fields are tightly bound to units requires retrieval to support field-level matching, to avoid incorrect recall results with mismatched units. The difference in update rhythms across different data sources requires the knowledge base’s recall and update functions to support classified management by data source, to adapt to mixed data scenarios with both real-time and periodic updates.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 10–15 | A single engineering consulting research report covers multiple modules, so enough relevant fragments need to be recalled to support complete reasoning |
similarity threshold | 0.72–0.80 | Engineering consulting terminology is highly professional, so low-match irrelevant content needs to be filtered to avoid incorrect recall |
max segment length | 800–1200 characters | A single research report includes multiple modules such as cost, technology, and compliance, so segments need to retain sufficient context to ensure retrieval accuracy |
reranked count | Top 5–8 | High-match core fragments need to be retained to avoid redundant content interfering with large model reasoning |
parseFileTimeoutSeconds | 900 seconds | Large engineering research reports have many pages and take a long time to parse, so extending the timeout prevents parsing failures |
incremental update trigger condition | Triggered by data source tags | Distinguish between different data sources such as general specifications and project documents, and perform incremental updates as needed to adapt to differentiated update rhythms |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: When no matching content exists in the knowledge base, a rejection message is returned directly, and general reasoning by the large model cannot be triggered. Cause: The
similarity thresholdis set too high, or the switch for only recalling matching content is enabled, causing the process to terminate immediately when no matches are found. - Phenomenon: Full-text retrieval returns no results, or matches documents unrelated to engineering consulting. Cause: No custom dictionary for engineering consulting terminology is configured, leading to incorrect splitting of terms and reduced matching accuracy.
- Phenomenon: Parsing timeout errors occur when uploading large engineering research reports. Cause: The
parseFileTimeoutSecondsparameter is not adjusted, and the default short timeout is used, which cannot complete complete parsing of long documents.
How to confirm the configuration is correct
- Upload a test fragment of an engineering consulting research report, input targeted professional questions, and check whether the returned recall fragments come from the uploaded test document.
- Adjust the
similarity threshold, observe changes in the matching degree of recall results, and confirm that the threshold setting meets the accuracy requirements of the current business. - Upload a single engineering research report with a large number of pages, check whether the parsing status is normal, with no timeout errors or truncation prompts.
- Test the incremental update function for research reports with different data source tags, and confirm that documents of the corresponding data source can be updated as expected.
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.