What Data for This Category Looks Like
Infrastructure engineering investment research data comes from project feasibility study reports, bidding documents, construction ledgers, industry technical standards, building material price index databases, and official cost announcements. Data update schedules follow project milestones. Single project documents are added in batches during project initiation, bidding, construction, and completion stages. Building material price data updates weekly or monthly. Document formats include long technical texts, structured bills of quantities, cost tables with professional units. Fields cover project numbers, construction scopes, material specifications, duration parameters, and more. Some drawing documents include unstructured annotation information.
Constraints for Vector Models and Indexing
Long technical texts for infrastructure engineering may exceed the context limits of basic vector models for a single document. Targeted adjustments to document splitting strategies are required. Structured bills of quantities contain many numerical fields with professional units. Separate extraction of structured features combined with non-text content is needed to avoid losing precise measurement information in plain text vectors. Batched project data updates require the indexing system to support incremental imports. This avoids resource consumption from full reindexing. Some drawing documents with annotations require text extraction before entering the vector generation process. This increases the complexity of preprocessing steps. Technical standard texts dense with professional terminology require vector models adapted to the engineering domain. Semantic recall deviations will occur otherwise.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Preserve complete semantic units when splitting long infrastructure engineering texts, avoid cross-segment semantic breaks |
chunk_overlap | 100–150 characters | Connect semantic associations between adjacent segments, prevent context gaps after long text splitting |
embedding_model | Specialized model adapted to the engineering domain | Infrastructure has dense professional terminology, general models have insufficient semantic recall accuracy |
index_batch_size | 20–30 entries per batch | Balance indexing efficiency and server memory usage, avoid memory overflow in Docker deployments |
recall_top_k | 10–15 entries | Cover multi-dimensional project parameters and technical details required for infrastructure investment research, avoid missing key information due to insufficient recall volume |
similarity_threshold | 0.72–0.85 | Filter low-relevance engineering documents while retaining associated content from different stages of the same project |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material formats, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Indexing tasks remain in a pending state or return the
ETIMEDOUTerror code in Docker deployment environments. Cause: Insufficient memory resources allocated to the vector indexing process, or no reasonable timeout and retry mechanism configured for embedding model calls. - Phenomenon: Vector recall results include a large number of irrelevant non-engineering text fragments, with low matching accuracy for core professional fields. Cause: No infrastructure-adapted embedding model selected, or no separate feature extraction configuration enabled for structured bills of quantities.
- Phenomenon: The specified large language model for chat can complete question answering normally, but the vector indexing task shows no progress updates. Cause: This model only supports text generation capabilities, and does not have embedding vector generation functions, so it cannot be used for the indexing link.
How to Verify Proper Configuration
- View vector generation logs to confirm that the number of chunks after single-document splitting matches the preset
chunk_sizeandchunk_overlapparameters. - Import a single typical infrastructure engineering document to verify that recall results include associated content for core fields such as project numbers and material specifications.
- Test incremental import of a single new document to confirm that the indexing task only processes newly added data, and does not perform full reindexing.
- Call the embedding model test interface to verify that there are no obvious semantic deviations in the vector generation results for professional engineering terms.
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.