What Data Looks Like for This Category
Data sources for this category include China Government Procurement Network, local government procurement websites, fiscal integration systems, purchaser budget systems, and supplier credit records. Update frequency is daily. Documents processed daily cover procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, responses to objections, and more. The main data subjects include purchaser units, the government procurement supervision division of finance departments, procurement agencies, and evaluation experts. Documents are generated across the entire government procurement process, and include core fields such as procurement project number, budget amount, supplier qualifications, and evaluation results.
What Constraints These Characteristics Impose on Vector Models and Indexes
Daily updated data requires indexes to support incremental synchronization. This avoids full reconstruction that consumes excessive system resources. Multiple types of documents cover long-text bidding documents and short-text winning bid announcements. Adaptive chunking strategies are needed to ensure semantic integrity. Documents across the entire government procurement process must retain metadata such as project number and evaluation results. Indexes need to associate metadata fields for subsequent compliance traceability. Structured supplier credit records and unstructured procurement documents are stored together. Unified vector mapping rules are required to ensure consistent retrieval across different data types.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of long-text documents such as bidding documents and tender documents, while avoiding single chunks exceeding the maxToken limit |
| Overlap Length | 100–200 characters | Retains contextual connection between chunks, preventing semantic breaks across chunks |
defaultToken | 4096 | Matches the average token count of most government procurement documents, ensuring contextual capacity for single-batch retrieval |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance non-procurement documents, meeting the compliance retrieval accuracy requirements for government procurement data |
| Single Search Limit | Top 10 results | Adapts to the rapid retrieval needs of evaluation experts and supervision staff, avoiding redundant results |
maxToken | 8192 | Adapts to complete vector storage of long bid evaluation reports, ensuring contextual integrity during retrieval |
Three Common Misconfigurations
- Symptom: After uploading procurement requirements or bidding documents, no corresponding entries appear in the index list. Background logs show index task timeout. Cause: Chunk parameters adapted to government procurement document lengths are not configured. This causes single chunks to exceed the
maxTokenlimit, interrupting index tasks. - Symptom: A prompt indicates no available channels when calling the embedding model. Embedding model options are empty in the interface. Cause: Channel configuration for the corresponding model is not completed. A legitimate access path is not registered in the system.
- Symptom: Retrieving supplier credit records returns a large number of irrelevant results, hitting non-government procurement documents. Cause: The similarity threshold is set too low. This fails to filter low-relevance non-target data, and project number metadata is not associated for secondary filtering.
How to Confirm Configuration Is Complete
- Upload a single procurement bidding document. Check the index generation status, and confirm there are no timeout errors in the task.
- Enter procurement requirement keywords. Confirm that corresponding documents are prioritized in retrieval results, and check that returned result metadata includes core fields such as project number.
- Test model access. Confirm that the no available channels prompt disappears, and model options display normally.
- Simulate daily incremental updates. Check that the index system only synchronizes newly added procurement documents, with no full reconstruction logs.
Reference only; verify against the target FastGPT release and deployment documentation.
The values listed above are common starting points, and should be evaluated 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.