What the data for this category looks like
The documents and materials for general contracting of building construction projects originate from the full project lifecycle: bidding, construction preparation, on-site execution, and completion acceptance. These include construction organization designs, change visa forms, project settlement statements, quality acceptance reports, and similar items. Data updates follow project milestones, with one to dozens of documents updated per batch. Individual documents contain standard fields: project number, project name, section information, signing date, core technical clauses, and cost details. Some documents include tables and drawing attachments, with field units such as square meters, ten thousand yuan, and calendar days.
What Constraints Do These Characteristics Impose on Vector Models and Indexes?
Documents for general contracting of building construction have three key traits: long paragraphs, dense professional terminology, and fields with fixed units. These traits create clear constraints for vector model and index configuration. Long documents require proper chunking rules to preserve contextual semantics. Professional terminology demands a vector model that supports engineering domain content. Fixed unit fields must avoid matching errors caused by unit confusion. Frequently updated change visa documents require indexes to support incremental updates, eliminating resource costs from full reindexing. Batch-imported bidding documents need systems that can handle batch chunking and embedding processing.
How to Set the Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Building construction documents often include long sections on construction techniques and cost details. This range balances semantic completeness and chunking efficiency |
| Overlap Length | 100–200 characters | Chunked long documents need to retain contextual connections to prevent semantic loss from broken segments |
| Similarity Threshold | 0.72–0.85 | Building construction professional terminology has high distinctiveness. This range filters low-relevance matching results and improves retrieval accuracy |
| Single Search Limit | Top 10 entries | Project-related documents are mostly concentrated in specific sections or change files. A small number of results meets typical retrieval needs |
maxToken | 4000 | Supports vector embedding for long texts in building construction documents, carrying sufficient professional terminology and contextual information |
defaultToken | 1000 | Matches the token limit converted from the character count of a single chunk, preventing truncation during embedding |
Three Common Configuration Mistakes
- Symptom: Index progress stalls after switching vector models, and the original model configuration cannot be restored. Cause: Only the model channel configuration was modified, and a forced index rebuild was not executed. The system does not trigger a re-embedding process for existing data.
- Symptom: Table and drawing text from source documents is not included in embedding results. Cause: The image OCR index toggle was not enabled, or the selected vector model does not support embedding non-text content.
- Symptom: A 404 error returns during testing after configuring a custom embedding model. Cause: The model's API access address and key were not filled in correctly, or the model channel did not complete permission setup in the system.
How to Confirm Correct Configuration
- Upload a single typical building construction project change visa document, and check if the chunk preview's paragraph division matches the configured chunk length and overlap length.
- Run a professional terminology query, and confirm that the matching logic of returned results aligns with the set similarity threshold rules.
- Perform a vector model switch operation on the index management page, confirm the progress bar loads normally, and no abnormal error prompts appear.
- Validate the access configuration for the custom embedding model, confirm test requests successfully return vector data, and no system prompt errors occur.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.