What Data Looks Like for This Category
Data for this category comes from China’s national government procurement website, local government procurement websites, fiscal integration systems, procurer budget systems, and supplier credit records. The update frequency is daily. Commonly processed documents include procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and responses to bidder inquiries. Document structures typically include fields such as project number, procurement content, budget amount, supplier qualifications, and evaluation criteria. There is no unified fixed format for these documents. Some documents require association with cross-system qualification and budget data.
Constraints Imposed on Vector Models and Indexing
Daily updated data sources require indexes to support incremental synchronization. This avoids full reindexing that uses excessive server resources. Data formats from multiple cross-system sources are inconsistent. Indexes must support custom field mapping to accommodate different document structures. Documents cover the entire procurement process. Retrieval weights for different document types must be differentiated to ensure core business documents such as bidding documents and bid evaluation reports are prioritized in recall. Supplier credit records must be strongly bound to project dimensions. Indexes must support precise filtering by fields such as project ID to prevent cross-project data confusion. Under compliance requirements, retrieval results must strictly match procurement-related business rules. This reduces irrelevant content recall.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of government procurement documents, avoids single chunks containing cross-chapter business content |
| Overlap Length | 100–200 characters | Ensures contextual coherence across chunks, especially for continuous text such as scoring rules and qualification requirements |
defaultToken | 4096 | Adapts to the input limits of most large language models, ensures that the spliced context after retrieval can be fully passed to the model |
| Single Search Limit | Top 8 results | Covers the number of core related documents for government procurement projects, avoids excessive redundant results interfering with business judgment |
maxToken | 8192 | Adapts to the total length requirement for spliced context after retrieval, meets processing needs for long documents such as complex bid evaluation reports |
| Reranking Switch | Enabled | Improves retrieval relevance for government procurement-related documents, filters low-match non-business documents |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Symptom: Knowledge base search responses time out, and the interface displays "Request processing timed out". Cause: Incremental indexing is not configured. Full reindexing uses excessive memory and CPU resources. The incremental synchronization strategy for daily updated government procurement data sources is not implemented.
- Symptom: Retrieval results include supplier credit records that do not belong to the current project. Cause: Filtering rules by fields such as project ID are not configured. Indexes are not bound to business dimension association fields, leading to cross-project data being recalled.
- Symptom: Question answering split processing does not return content that complies with procurement business rules. Cause: The division of labor between vector models and large language models is confused. The specified embedding model is not used to vectorize procurement documents, resulting in insufficient retrieval matching accuracy.
How to Confirm Configurations Are Correctly Set
- Check index synchronization logs to confirm that daily incremental synchronization tasks trigger normally, with no abnormal interruption records.
- Enter the procurement number of a specific project, confirm that retrieval results only return documents associated with that project, with no cross-project data included.
- Test the chunking effect of long texts such as bid evaluation reports, confirm that contextual coherence is maintained in overlapping sections, with no content truncation.
- Verify that parameters such as chunk length,
defaultToken, andmaxTokenin the configuration items match the preset values.
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.