What the data for this category looks like
The data for this category comes from on-site supervision logs, parallel inspection reports, progress payment review forms, sub-project acceptance ledgers, and other materials generated during engineering consulting supervision work. Updates are executed per project progress milestones, with synchronization once weekly or monthly during the project cycle, and final archiving updates completed after project acceptance. Documents include structured metadata and free text descriptions. Metadata fields include project number, supervision unit, construction unit, inspection date, rectification items, acceptance conclusion, and more. Some documents have attachments such as on-site photos and PDF scans. Field units follow engineering industry standards: duration is measured in days, amount in yuan, and area in square meters.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Mixed structured metadata and free text document structure requires retrieval to cover both keyword matching and semantic intent recognition. The presence of attachments such as on-site photos and PDF scans requires the retrieval system to support multi-modal content recall and parsing. The update rhythm based on project milestones requires the knowledge base synchronization frequency to match project progress cycles, preventing outdated data from being recalled. Single documents contain multiple dimensions of business fields, requiring the number of recalled results to cover complete project information and avoid missing key fields.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.65–0.75 | Engineering consulting supervision ledgers have high requirements for field accuracy; this range filters low-relevance retrieval results |
limit | Top 8–12 results | A single ledger contains multiple dimensions of business fields; more recalled results can cover complete project information |
searchMode | Semantic + keyword hybrid retrieval | Ledgers contain both structured metadata and free text descriptions; hybrid mode matches both keywords and semantic intent |
usingReRank | Enabled | Similar content exists among multiple recalled results; re-ranking improves the ranking priority of highly relevant results |
datasetSearchUsingExtensionQuery | Enabled | Ledgers contain a large number of industry-specific terms for supervision; extended queries cover synonyms and professional expressions |
collectionIds | Bind the knowledge base collection of the target project | Ensure retrieval only covers supervision ledger data for the current project, preventing irrelevant cross-project content from being included |
Three Common Mistakes
- Phenomenon: Semantic retrieval fails to locate ledger knowledge blocks containing specified supervision keywords. Cause:
datasetSearchUsingExtensionQueryis not enabled, so synonym matching for industry-specific supervision terms is not covered. - Phenomenon: When using a strict question-and-answer template, the system prompts that no answer is found after retrieving knowledge base entries containing image addresses. Cause: The strict question-and-answer template only processes plain text knowledge blocks, and multi-modal retrieval adaptation for attachments such as on-site photos is not configured.
- Phenomenon: When calling the API, passing a globally variable knowledge base ID results in a parameter error prompt from the system. Cause: The global variable is not converted to the valid
collectionIdsparameter format, or the knowledge base collection ID is not correctly bound in the request body.
How to Confirm the Configuration Is Correct
- Enter exclusive keywords for the supervision project, such as the description of rectification items for the corresponding project, and check whether the retrieval results include the structured fields and content of the corresponding ledger.
- Upload a photo of on-site supervision rectification as test knowledge, and verify whether the entry can be normally recalled under different retrieval modes.
- Call the API interface, pass the preset
collectionIdsparameter, and check whether the returned results include entries from the target knowledge base. - Adjust the
similarityparameter range, observe the change in the relevance of retrieval results, and confirm the threshold range that meets business requirements.
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.