What this category of data looks like
Data related to the policy and regulatory basis of government procurement projects comes from sources including the China Government Procurement Network, local government procurement websites, fiscal integration systems, procurer budget systems, and supplier credit records. Daily processed documents cover procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and responses to objections. Involved parties include procuring entities, fiscal department government procurement supervision divisions, procurement agencies, and evaluation experts. Data is updated daily, so full and incremental synchronization of all business documents must be supported.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Scattered data sources with daily updates require the retrieval system to support unified access to multi-source data and incremental synchronization, to avoid missing the latest winning bid announcements or objection responses. Diverse document types with precise wording require distinguishing weight and matching rules for different documents, to ensure retrieval accuracy of core documents such as procurement requirements and scoring methods. The consequences of errors are severe: deviations in retrieved policy clauses may lead to bid invalidation, valid complaints, and other issues, so retrieval accuracy and recall completeness requirements are high.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75-0.85 | Meets the precision requirements for matching government procurement policy clauses, filters irrelevant content with low matching scores |
limit | Top 5 results | Controls the number of recall results per round, adapts to the quick review scenarios of supervision staff and evaluation experts |
usingReRank | Enabled, re-rank top 10 results | Optimizes the ranking accuracy of long-text policy documents, improves the recall ranking of core clauses |
searchMode | Exact match first | Adapts to the fixed wording format of government procurement policies, reduces misjudgments caused by fuzzy matching |
datasetSearchUsingExtensionQuery | Disabled | Government procurement policies use precise wording, no need to expand query terms to avoid deviating from the core content of the original text |
collectionIds | Government procurement exclusive dataset | Isolates non-government procurement data, ensures the retrieval scope complies with regulatory requirements |
Three common mistakes
- Phenomenon: Some policy clauses with fixed wording are incorrectly split by the system, and complete content cannot be retrieved. Cause: No chunking disable mark is set for policy paragraphs containing statutory wording, and the system splits precise policy clauses by default according to character count.
- Phenomenon: Retrieval result order does not change significantly after enabling
usingReRank. Cause: The pre-recall number of the re-ranking model is not configured, or the pre-recall results do not include core policy clauses, so the re-ranking function cannot work. - Phenomenon: Images attached to uploaded bidding documents cannot be displayed in search results. Cause: Image text extraction and associated display configuration are not enabled, only image files are uploaded without synchronously extracting policy text content from the images.
How to confirm the configuration is complete
- Manually upload a government procurement bidding document, use core policy clauses as query terms, check whether the system returns results according to the exact match rules specified in
searchMode. - After enabling
usingReRank, compare the result rankings before and after re-ranking to confirm that the position of core policy clauses is adjusted reasonably. - View the dataset update log to confirm that the daily incremental synchronization task is running normally, and the latest winning bid announcements and objection responses have been successfully collected.
- Retrieve the preset non-chunked policy paragraphs, confirm that the returned content is a complete paragraph and is not split by the system.
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.