This document adapts to the knowledge base retrieval and recall configuration logic of FastGPT v4.17.0.
What This Category of Data Looks Like
The policy and regulatory basis data for building construction general contracting comes from official management measures issued by the Ministry of Housing and Urban-Rural Development, local supporting rules issued by local housing and construction departments, and industry standard specifications. The update frequency follows the release frequency of national and local housing and construction field policies, with no fixed cycle. Documents are in official document format, containing fixed fields such as policy document number, issuing authority, effective date, and clause hierarchy (article/paragraph/subparagraph). The length of single documents varies widely, ranging from hundreds of words of special notices to tens of thousands of words of comprehensive management measures.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The characteristics of building construction general contracting policy data impose multiple constraints on retrieval and recall. Policy clauses are clearly divided into articles, paragraphs and subparagraphs. Accurate matching of clause numbers is required to meet business query needs. Generalized retrieval may easily return irrelevant clauses or incomplete policy fragments. The span of single document length is large. When splitting long documents, care must be taken to avoid breaking clause logic, otherwise the model will fail to associate complete policy requirements. Policies have clear effective dates. Outdated policies must be filtered, so support for limiting the recall scope by release time or effective date is required. Some local policies only apply to specific regions, so support for narrowing the recall scope by region is needed to avoid cross-regional policy interference with query results.
How to Configure the Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Building construction policy texts are rigorous and professional. This range can filter low-match irrelevant content, balance retrieval precision and coverage, and can be adapted to different query scenarios after calibration based on actual testing. |
limit | Top 10 entries | Single policy documents have a large number of clauses. Recalling too much content at one time will exceed the context window. 10 entries can cover the core policy basis for most business queries and avoid redundant information interference. |
searchMode | embedding | Building construction policies contain a large number of professional terms and fixed formats. Vector-based matching can accurately identify clause semantics, which is better than the generalized results of keyword matching. |
usingReRank | Enabled | There are many cases where policy clauses have similar semantics. Reranking can secondarily sort recall results based on query intent, improving the display priority of core clauses. |
datasetSearchUsingExtensionQuery | Enabled | Business queries often contain engineering scenario descriptions. Extended queries can convert scenario descriptions into policy keywords, improving recall relevance. |
collectionIds | ID corresponding to the building construction general contracting policy dataset | The retrieval scope must be limited to only policies related to building construction general contracting, avoiding mixing policy documents of other construction categories, ensuring retrieval precision. |
Three Common Misconfigurations
- Phenomenon: When calling the FastGPT retrieval interface integrated with the business system, empty results or documents unrelated to building construction policies are returned, and knowledge base data synchronization has been completed through the interface. Cause: The retrieval scope is not limited via the
collectionIdsconfiguration, causing retrieval to cover non-target policy datasets, ordatasetSearchUsingExtensionQueryis not enabled to adapt to business scenario-based query text. - Phenomenon: When adding the building construction policy knowledge base in the FastGPT backend, a configuration failure prompt pops up, and dataset import cannot be completed. Cause: The uploaded policy documents do not follow official document formats, or the mapping rules between metadata fields and policy document numbers, effective dates are not correctly configured, causing the system to fail to parse document content.
- Phenomenon: When entering a long text query exceeding 2000 characters, the number of retrieval results is insufficient or the relevance is reduced, and some queries time out. Cause: Reasonable
similarityparameter andlimitnumber are not set, causing too much redundant content to be recalled and exceeding the system context window, orusingReRankis not enabled to optimize the sorting logic.
How to Verify Successful Configuration
- Upload one typical building construction general contracting policy document, and confirm whether the knowledge base parsing result correctly identifies the document number, effective date and clause hierarchy.
- Enter a query containing a specific clause number, and verify whether the retrieval result accurately returns the corresponding clause content, with no irrelevant policy documents included.
- Enter a business scenario query, such as "Safety management requirements for general contracting projects", and confirm whether the retrieval result covers relevant policy clauses and the sorting meets business needs.
- View the update log of the FastGPT backend dataset, and confirm that the policy data synchronized through the interface has been successfully imported and marked as valid.
Note: The configuration values listed above are common starting points. They should be adjusted based on specific samples and testing.
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.