What the data for this category looks like
Building engineering research reports originate from several sources. These include specialized infrastructure industry reports published by financial institutions, public engineering quota documents released by housing and construction authorities, research reports issued by industry associations, and project detail documents from bidding announcements. Updates align with policy releases, quarterly industry reviews, and major project completion announcements. Most documents are in PDF format. They contain project overviews, cost analysis tables, construction process descriptions, policy interpretation sections, and numerous nested structured tables. Fields include project number, floor area, unit cost, construction duration, and more. Common units are square meters, yuan per square meter, calendar days, and other professional engineering units.
Constraints imposed by these characteristics on vector models and indexing
The dense structured tables and specialized terminology in building engineering research reports require vector models with strong long-text semantic alignment capabilities. This prevents loss of contextual associations for specialized terms after text splitting. The batch update cadence requires indexing systems that support incremental synchronization. This avoids resource waste from full index rebuilding. Multiple unit fields require indexing processes to retain associations between fields and units. This prevents confusion of parameter semantics during retrieval. Large format differences between research reports from different sources require preprocessing to standardize field formats. This avoids vector embedding deviations from inconsistent formats.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Building engineering research reports contain many nested tables and specialized technical paragraphs. Excessively long segments break contextual associations of data within tables. Excessively short segments split fixed specialized term combinations |
chunk_overlap | 150–200 characters | Building engineering specialized terms are mostly fixed collocations. Overlapping segments preserve semantic coherence for terms across segments, avoiding semantic breaks during retrieval |
embedding_model | text-embedding-3-large | Building engineering research reports have many engineering specialized terms and structured data. This model delivers more stable semantic alignment for professional text, reducing retrieval deviation |
index_type | HNSW | The volume of building engineering research report data grows gradually with project updates. HNSW indexes see slower declines in query efficiency as data volume increases, making them suitable for incremental update scenarios |
retrieval_top_k | Top 10–15 results | Specialized content in building engineering research reports has strong relevance. Excessive recall introduces irrelevant detailed project data, reducing retrieval result accuracy |
enable_incremental_index | Enabled | Building engineering research reports are updated in batches quarterly. Incremental indexing avoids the time overhead of full rebuilding, improving update efficiency |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Issue: Multiple duplicate index entries appear in the index list. A single research report in the dataset corresponds to multiple index fragments. Cause: The deduplication logic for
enable_incremental_indexis not enabled. Old incremental index fragments are not cleaned up during each synchronization task, causing index counts to accumulate automatically over time. - Issue: The vector index building task does not respond for an extended period, returning a
504 Gateway Timeouterror. The problem persists after commenting out theembedding_modelconfiguration item and restarting the service. Cause: Theindex_build_timeoutparameter is not set. The default timeout period is insufficient for processing long-text segmentation tasks of building engineering research reports. The cached embedding vector request queue is not cleared after commenting out the model, leading to persistent task blocking. - Issue: After adding a free indexing model, the knowledge base fails to generate valid recall results, returning a
403 Forbiddenerror. Cause: The call quota for the free embedding model is not confirmed. The number of segments submitted in a single batch exceeds the free interface's limits.
How to confirm correct configuration
- Upload a sample building engineering research report. Check the segmentation preview interface to confirm segment length matches the
chunk_sizeconfiguration value. - Trigger an incremental synchronization task. Check the index update log to confirm only newly added research report data is included in the index, with no duplicate index entries.
- Initiate a research report retrieval request. Confirm the number of recall results matches the
retrieval_top_kconfiguration value. - Check the model binding configuration. Confirm the indexing model and question answering language model are bound to different resource pools, with no resource conflicts.
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.