What the data for this category looks like
The data for infrastructure construction intelligent due diligence reports comes primarily from project approval documents, construction logs, supervision weekly reports, cost settlement statements, bidding documents, and completion acceptance materials. Data updates follow project phases, with separate staged documents generated during project initiation, construction, and completion stages. Individual documents are lengthy, and typically include structured content such as project overviews, bills of quantities, progress ledgers, compliance certificates, and cost breakdowns. Fields use engineering-specific units including cubic meters, square meters, ten thousand yuan, and construction days. Supported document formats include PDF, Excel, Word, and structured database export files.
What constraints do these characteristics impose on vector models and indexing
The long-text nature of infrastructure construction due diligence report data requires vector models and indexing to preserve cross-paragraph semantic connections, and avoid splitting critical engineering data. The staged update rhythm of data requires indexing to support incremental updates, reducing resource consumption from full index rebuilding. Mixed input of multiple formats and structured fields requires indexing to support vector generation and storage for both unstructured text and structured metadata. Additionally, the professional nature of engineering data requires vector models to adapt to domain terminology understanding, avoiding semantic misinterpretation of professional concepts such as engineering quantities and construction schedules by general-purpose models.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | qwen3-embedding-8b or the HTTP API address of a locally Docker-deployed M3E model | Adapts to semantic understanding requirements for infrastructure engineering domain terminology, supports both general and domain-fine-tuned versions |
chunk_size | 800–1200 characters | Matches the long paragraph structure of infrastructure engineering documents, preserves contextual connections for critical information such as engineering quantities and progress |
chunk_overlap | 100–150 characters | Connects adjacent chunks, avoids splitting cross-paragraph engineering data |
retrieve_top_k | Top 8–12 results | Covers multi-dimensional engineering data required for due diligence reports, avoids missing critical information due to insufficient recall results |
similarity_threshold | 0.75–0.85 | Filters low-relevance recall results, ensures semantic matching between returned content and due diligence objectives |
index_incremental_update | Enabled | Adapts to the staged update rhythm of infrastructure project data, reduces time spent on full index rebuilding |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific scenarios require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: When adding an embedding model with the same name in the FastGPT backend for versions v4.9.11 to v4.9.12, existing configurations are automatically overwritten, and multiple configurations of the same model type cannot be retained. Cause: In these versions, embedding model configurations use the model name as the unique primary key, and do not support aliases or multiple instance configurations.
- Symptom: After uploading infrastructure engineering documents, key information such as bills of quantities and construction schedule milestones appears split in chunking results. Cause: The
chunk_sizesetting is too small, and insufficient character length is reserved to preserve cross-paragraph semantic connections. - Symptom: Malformed Markdown headings appear in knowledge base recall results, and document structure cannot be matched correctly. Cause: The automatic format parsing switch for source documents is not disabled, causing the embedding model to receive mixed-format text and reducing semantic matching accuracy.
How to Verify Proper Configuration
- Navigate to the model configuration page in the FastGPT knowledge base, and check if the configured
qwen3-embedding-8bor local M3E API address appears in the embedding model dropdown list. - Upload an infrastructure engineering document containing a bill of quantities, run parsing, and view the chunk preview to confirm that chunk lengths fall within the preset
chunk_sizerange and no critical information splitting occurs. - Initiate a recall test, check if the number of returned recall results matches the
retrieve_top_ksetting, and verify that the similarity score of each result falls within the presetsimilarity_thresholdrange. - Upload a new staged engineering document, run incremental update, search for key terms in the document, and confirm that the new content can be correctly recalled.
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.