What the Data for This Category Looks Like
Engineering consulting supervision data mainly originates from on-site supervision logs, witnessed inspection records, sub-project acceptance reports, progress control ledgers, contract change attachments, and similar materials. Data update frequency adjusts with project progress. New record documents are generated daily or weekly during routine inspection and acceptance phases. Archived data in the completion phase stops updating. Individual document lengths vary widely: some are correction notices with hundreds of characters, while others are full-cycle supervision summaries with tens of thousands of characters. Documents contain both structured fields and unstructured text. Structured fields include project number, supervision unit, acceptance time, correction item number, and units such as days, counts, square meters, and others.
Constraints Imposed on Vector Models and Indexing
Decentralized data sources with both structured and unstructured content require indexes to support unified vector mapping and field association for multi-source data. Wide variation in document lengths demands adaptive text chunking rules. This avoids truncating long documents or redundant chunking of short documents. Frequently updated routine documents require indexes to support incremental updates. This reduces resource usage from full index rebuilding. Units and numbers from structured fields must be included in index metadata. This aids precise matching in semantic retrieval and prevents confusion between similar correction items across different projects.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide range of lengths in supervision documents, balances chunk completeness and retrieval accuracy |
| Overlap Length | 100–200 characters | Prevents context breaks between adjacent chunks, maintains semantic coherence for continuous text such as correction items and acceptance workflows |
maxToken | 4096 token | Supports vector storage for long documents, prevents forced truncation of lengthy supervision summaries |
defaultToken | 2048 token | Matches the context window for routine retrieval, balances retrieval efficiency and semantic coverage |
| Similarity Threshold | 0.7–0.85 | Meets semantic matching accuracy requirements for supervision documents, filters low-relevance non-project documents |
| Maximum Search Results per Query | Top 10 entries | Controls the number of retrieval results, prevents redundant results from interfering with supervision staff's issue troubleshooting |
Three Common Configuration Mistakes
- A 60-second timeout error occurs when switching knowledge base indexes. A timeout prompt pops up in the interface. The cause is that incremental index updates are not configured. Full index rebuilding loads all historical documents from the project cycle, exceeding the system's default processing duration.
- The total number of documents in the knowledge base is less than 100, but the backend status continuously displays training or rebuilding for more than 24 hours. The cause is that the chunk length is set too small. This generates far more vector chunks than actually needed, leading to index task backlogs.
- The batch add index API request returns an invalid parameter error. The cause is that the association field between vector chunks and corresponding supervision documents is not correctly specified. Structured metadata parameters are not passed as required.
How to Verify Correct Configuration
- Upload a single typical supervision correction notice. Confirm that the number of generated vector chunks matches the configured chunking rules.
- Submit a retrieval request containing keywords for a specific correction item. Confirm that the similarity of returned results falls within the set threshold range.
- Check the index task logs in FastGPT v4.17.0. Confirm that the incremental update logic triggers normally, with no abnormal records of full index rebuilding.
- Submit a batch index request. Confirm that the interface returns a success status code matching the configured parameters.
The values provided are common starting points. They should be evaluated 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.