What the Data for This Category Looks Like
Ledger data for infrastructure construction comes primarily from business systems including construction logs, material on-site acceptance slips, weekly progress reports, and completion acceptance reports. Data update frequency follows construction milestones. Complete ledger entries are created after individual project acceptance. Progress and material consumption data is updated daily during active construction. Most documents are structured, with fixed fields such as project ID, construction section, material specification, on-site date, inspector, and qualification status. Common units of measurement include cubic meters, tons, days, square meters, and other standard engineering units.
Constraints on Knowledge Base Retrieval and Recall
Structured ledger data with multiple fields requires retrieval to precisely match specific field content, to avoid recalling irrelevant entries. Frequently updated construction data requires retrieval nodes to dynamically bind project scopes, to ensure returned ledger content is up to date. Many engineering-specific terms have Chinese-English translations. Retrieval must optimize query terms to match these professional expressions. Long documents such as acceptance reports and progress statements require reranking of recall results to prioritize the most relevant content fragments.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75-0.85 | Infrastructure ledgers contain many professional terms. A higher similarity threshold filters irrelevant recall results and ensures matching accuracy |
limit | Top 8 entries | Ledgers have a large number of fields. Too many recall results will exceed context length limits, reducing the large model’s ability to extract valid information |
searchMode | vector | Precise matching of structured ledger field content is required to ensure recall results are highly relevant to queries |
usingReRank | Enabled | Long documents such as acceptance reports and progress statements have large amounts of content. Reranking places the most relevant content fragments first, improving retrieval efficiency |
datasetSearchUsingExtensionQuery | Enabled | Ledgers contain professional terms with Chinese-English translations. Optimizing query terms improves matching success rates for professional expressions |
collectionIds | Fill in by project ID list | Retrieval scope must be limited to the ledger knowledge base of specific projects to avoid recalling irrelevant data from other projects |
Three Common Misconfigurations
- Phenomenon: Only the first query in a workflow triggers knowledge base recall. Subsequent queries do not reference knowledge base content. Cause: Global variable persistence configuration is not enabled, or the knowledge base retrieval node is not bound to the dynamic
collectionIdsparameter. This causes subsequent requests to switch dataset scopes. - Phenomenon: After importing ledger translation documents with Chinese-English translations, the large model does not call knowledge base content. Cause: The
datasetSearchUsingExtensionQueryparameter is not enabled, and retrieval terms are not optimized for professional terms. This leads to failed matching of professional expressions. - Phenomenon: The model prompts that no specific knowledge base content is provided. Cause: The
similaritythreshold is set too high, resulting in zero matching recall results, or thecollectionIdsfield is filled incorrectly, failing to link to the target ledger knowledge base.
How to Confirm Successful Configuration
- View the return logs of the knowledge base retrieval node in FastGPT v4.17.0, to confirm the number of recall results matches the range set by
limit. - Input professional terms from ledgers, to verify that retrieval results include content fragments from the corresponding fields.
- Switch the
collectionIdsparameter for different projects, to confirm that retrieval results only include ledger data for the target project. - Test multi-turn conversations, to confirm that every query triggers knowledge base recall and references relevant content.
The values provided 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.