What Data Looks Like for This Category
Data sources for this category include national public resource trading platforms, local trading platforms, engineering approval systems, and bidding supervision systems. The update frequency is daily. Daily generated documents include bidding announcements, qualification pre-qualification documents, bidding documents, bid submission documents, bid evaluation reports, public announcements of successful bidders, and successful bid result announcements. Core fields involve section division, qualification conditions, bid evaluation records, and other related content. The data is centered around engineering project construction and the bidding law framework. Involved entities include development and reform departments, industry competent departments such as housing and urban-rural development, transportation, and water conservancy, public resource trading centers, and bidding agencies.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Daily updated high-volume documents require indexes to support incremental processing, to avoid resource consumption from full reconstruction. There are significant structural differences across document types: bidding announcements focus on section information, while bid submission documents focus on qualification and performance records. Chunking must be adapted to the semantic units of different documents to prevent invalid cross-document cutting. Multi-source data must first undergo format normalization, otherwise vector embedding deviations will occur. Some documents contain sensitive information, so sensitive word filtering rules must be configured before indexing is performed. The accuracy of core fields directly affects the quality of consultation responses, so ensure that key semantic content such as sections and qualification conditions is retained during chunking.
Configuration Settings
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single documents such as bidding announcements and bid submission documents, retains core semantic units such as section division and qualification requirements |
| Overlap Length | 100–150 characters | Prevents chunk truncation of cross-section and cross-clause related information, ensures semantic coherence |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant results, reduces invalid interference from objection and complaint-related consultations |
| Maximum Search Results per Query | Top 6 entries | Controls context length, focuses on core bidding document content related to user consultations |
defaultToken | 4096 | Adapts to processing needs of long texts such as bid evaluation reports and complete bid submission documents, ensures key information is not truncated |
| Reranking | Enabled | Optimizes sorting logic for candidate results, prioritizes returning highly relevant content related to qualification conditions and bill of quantities |
Three Common Misconfigurations
- Phenomenon: The number of chunks displayed when uploading a single document does not match the number of chunks displayed after the document is ready, with duplicate chunks appearing. Cause: No chunking rules adapted to bidding documents are configured. The system's default chunking logic fails to identify structured content such as sections and clauses, leading to repeated cutting of semantic units.
- Phenomenon: After upgrading from an older version to FastGPT v4.17.0, previously queryable bidding documents can no longer be retrieved. Cause: Vector embedding model parameters in the new version have been adjusted, and the original index data has not been reindexed, resulting in mismatched embedding vectors that cannot match retrieval requests.
- Phenomenon: Batch triggering of vector model retraining is not supported, only manual parameter adjustment and re-uploading for single files are available. Cause: Batch indexing configuration is not enabled. The system defaults to only supporting single-file incremental updates, and does not adapt to the daily batch document update requirements of the bidding scenario.
How to Confirm Proper Configuration
- Upload a single bidding announcement document, verify that the number of chunks matches the expected number of semantic units, and confirm that the chunking rules adapt to the document structure.
- Simulate high-frequency user consultations such as qualification requirements and successful bid results, check that the sorting of retrieval results conforms to business priorities, and confirm that the reranking configuration is active.
- Upload an updated bidding document, check that the index update time conforms to the daily batch processing rhythm, and confirm that the incremental indexing configuration is correct.
- Test a bidding document containing sensitive information, confirm that the sensitive word filtering rules are active, and there is no risk of sensitive information leakage.
The values provided here 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.