What the data for this category looks like
Infrastructure engineering research report data primarily comes from public reports issued by industry associations, securities firm research reports on the building decoration sector, and specialized analysis documents from engineering consulting institutions. Update frequency adjusts based on project milestones and industry trends. Public industry research reports are updated monthly. Project-specific documents are released alongside bidding, construction start, and completion milestones. Document structures include modules such as project overview, investment budget, technical parameters, progress milestones, and cost breakdown. Fields include standardized unit content like project ID, investment amount (ten thousand yuan), construction area (square meters), and construction period (days). Some documents include attachments such as engineering drawings and construction logs.
What constraints these characteristics impose on the vector models and indexing workflow
Infrastructure engineering research reports contain long-form technical analysis and structured engineering parameters. When chunking text, balance the integrity of technical terminology and contextual coherence. Avoid splitting core content such as pile foundation bearing capacity and cast-in-place construction processes. Non-fixed update cycles require indexes to support incremental updates. This adapts to specialized documents released alongside project milestones. Standardized units in structured fields must be mapped to vectors alongside text content. This ensures retrieval can associate parameter values with technical descriptions. Segmentation length for long documents must match model input limits. It must also retain cross-paragraph engineering logical connections.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Infrastructure engineering research reports often include continuous technical parameters and construction logic. Too-short segments split professional content. Too-long segments exceed the input window of general vector models |
embedding_model | bge-m3 | This model has strong semantic understanding of domain-specific technical terms. It can accurately map vector features of infrastructure-related text |
vector_search_top_k | Top 10–15 results | Relevant results for infrastructure engineering research reports must cover multiple documents from the same project and technical direction. Too few results miss critical parameters |
filter_enabled | Enabled | Infrastructure engineering research reports include structured fields such as project ID and investment amount. Enabling this allows precise narrowing of retrieval scope via parameters |
index_refresh_interval | 1 hour | Project-specific research reports update alongside bidding milestones. A 1-hour refresh interval balances real-time performance and index resource usage |
UPLOAD_FILE_MAX_SIZE | 200 MB | Single infrastructure engineering feasibility study reports may include large numbers of drawings and data attachments. Larger file uploads must be supported |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test against your own samples before finalizing settings.
Three common configuration mistakes
- Vector indexing tasks show a "pending" status for more than 30 minutes with no progress updates. The cause is failure to configure the
index_refresh_intervalparameter, or setting it to 0. This prevents incremental indexing from triggering automatically. - Retrieved content is completely unrelated to the query. Recall results deviate from the infrastructure engineering topic. The cause is failure to enable the
filter_enabledparameter. Structured field filtering is not used to exclude research reports from non-target infrastructure projects. - Uploading large research reports triggers a
413 Request Entity Too Largeerror. The cause is theUPLOAD_FILE_MAX_SIZEparameter being set smaller than the actual size of the uploaded file.
How to confirm configurations are correct
- View the vector model and indexing configuration page. Confirm all configuration item values match the values in the configuration table.
- Upload a test infrastructure engineering research report. Check the indexing task logs. Confirm there are no errors about segment truncation or failed vector generation.
- Submit a query that includes structured parameters, such as "What is the construction period of a certain project". Confirm the retrieval results include content with the corresponding field.
- Check the index refresh records. Confirm incremental update tasks trigger automatically at the set interval.
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.