What the Data for This Category Looks Like
Data for this category primarily comes from the China Government Procurement Network, local government procurement websites, fiscal integration systems, procurer budget systems, and supplier credit records. The update frequency is daily. Documents processed daily include procurement requirements, procurement plans, bidding documents, tender submissions, bid evaluation reports, winning bid announcements, procurement contracts, and objection responses. The data links procuring entities, government procurement supervision divisions under finance departments, procurement agencies, and review experts. Most documents are a mix of structured and semi-structured data, with fields such as project ID, supplier qualifications, scoring rules, and credit ratings.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Daily updated data requires incremental indexing logic to avoid performance loss from full reindexing. In scenarios with mixed heterogeneous documents, chunking must match the inherent field boundaries of documents to prevent vector deviation caused by cross-document semantic breaks. Heterogeneous data from multiple systems needs unified field mapping rules to ensure semantic consistency during vector generation. Compliance requirements for government data restrict the mixed indexing scope of sensitive internal data and public data, while enabling accurate recall of associated procurement documents and credit records to fit the business association logic of government procurement scenarios.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the paragraph semantic boundaries of government procurement documents, avoids semantic loss caused by overly fragmented or overly long chunks |
| Overlap Length | 50–100 characters | Retains semantic connections between adjacent chunks, prevents key information from breaking across chunks |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance non-government procurement documents, ensures recall results align with business scenarios |
defaultToken | 4096 | Fits the context window of most large models, meets vector processing requirements for long procurement documents |
maxToken | 8192 | Covers vector generation requirements for longer documents such as complete bid evaluation reports and objection responses |
| Single Search Limit | Top 10 entries | Balances recall accuracy and query performance, fits multi-document association queries for government procurement projects |
Three Common Configuration Mistakes
- Issue: A custom Qdrant vector database fails to mount normally, with a connection failure prompt displayed. Cause: Database index mapping is not configured per the government procurement scenario’s data source format, resulting in failure to correctly parse government data fields.
- Issue: Channel configuration cannot be completed after enabling the indexing model, with a parameter missing prompt shown on the interface. Cause: Vector model key and interface address configuration was not completed in advance, or the interface call frequency limit for government procurement scenarios was not matched.
- Issue: A large number of non-government procurement documents appear in recall results, with low accuracy. Cause: The similarity threshold is set too low, failing to filter historical documents unrelated to the current procurement project.
How to Confirm Correct Configuration
- Upload a single government procurement bidding document, trigger vector generation, and check whether the chunked text retains the semantic integrity of core fields such as procurement requirements and scoring rules.
- Run a query associated with supplier credit records, verify that recall results include the corresponding supplier’s credit information, and that the number of results matches the set single search limit.
- Review indexing logs to confirm that the incremental update task runs automatically every day, with no abnormal delays caused by full reindexing.
- Check the vector database’s storage capacity, confirm that it supports the daily update volume of government procurement data, and that no insufficient storage space alerts are displayed.
The values listed here 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.