What the Data for This Category Looks Like
Data sources for this category include China Government Procurement Network, local government procurement websites, fiscal integration systems, purchaser budget systems, and supplier credit records. Updates occur daily. Daily documents cover procurement requirements, procurement plans, bidding documents, bid submissions, bid evaluation reports, winning bid announcements, procurement contracts, and responses to inquiries. Each document includes fixed fields such as project number, procurement category, qualification requirements, and bid evaluation rules. This data strictly follows the rules of the government procurement law system. It differs from engineering construction bidding data in regulatory rules and text structure.
Constraints for Vector Models and Indexes
Daily updated data sources require indexes to support incremental synchronization. Full reindexing uses excessive system resources, so this must be avoided. Multiple document types have distinct structural differences. Chunking logic must adapt to different fields to prevent semantic breaks across document chunks. Data links to government procurement regulatory rules. Complete clause context must be retained during chunking. Missing fragments can cause compliance risks. Supplier credit records link to procurement project data. Indexes must support cross-document associated retrieval. This ensures the accuracy of qualification verification.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|
Reference only; verify against the target FastGPT release and deployment documentation.
| Chunk overlap length | 100–200 characters | Retains context association across chunks, ensures the integrity of policy clauses, and avoids semantic fragmentation |
| Similarity threshold | 0.75–0.85, calibrated via actual testing | Adapts to the semantic similarity characteristics of government procurement policy texts, filters irrelevant documents with low matching degrees |
| Maximum search results per query | Top 10 entries | Controls the number of retrieval results, avoids overly long contexts that exceed the large language model's processing capacity |
| defaultToken | 1024 token | Matches the input limits of mainstream vector models, adapts to the conventional length of government procurement documents after chunking |
| Maximum context size | 4000–6000 characters | Combines the context windows of vector models and large language models, ensures that retrieved policy clauses are sufficient to support Q&A |
Three Common Mistakes
- Phenomenon: When uploading government procurement documents larger than 10MB, generated chunks experience vectorization anomalies, and normalcy resumes after repeated retries. Reason: No reasonable chunk overlap parameters are configured, leading to broken semantic associations for some chunks. The system automatically adjusts chunk boundaries during retries to restore normalcy.
- Phenomenon: Single chunk length exceeds the
maxTokensupported by the vector model, resulting in vectorization failure or abnormal results. Reason: Chunk parameters are not adjusted based on the actual length of government procurement documents, and default chunk settings are used directly, causing content within the block to exceed model limits. - Phenomenon: When importing Excel-format procurement project data, some row data has empty fields after vectorization. Reason: Chunking rules are not configured for the structural differences between Excel headers and business data, leading to broken associations between headers and data rows, and incomplete field extraction.
How to Verify Correct Configuration
- Upload a single typical government procurement document, check the chunk preview interface, confirm that the length of each chunk falls within the configured chunk length range.
- Initiate a query related to policy basis, check the similarity scores of retrieval results, confirm that the scores fall within the configured threshold range.
- Upload batch government procurement documents larger than 10MB, check the vectorization task logs, confirm there are no chunk vectorization anomaly errors.
- Simulate daily incremental data updates, check the time taken for index synchronization, confirm that it meets system resource carrying requirements.
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.