What Data for This Category Looks Like
This category’s data comes from the national public resource trading platform, local trading platforms, engineering approval systems, and bidding supervision systems. The update frequency is daily. Daily documents include bidding announcements, qualification pre-qualification documents, bidding documents, bid documents, bid evaluation reports, successful bid candidate announcements, and successful bid result announcements, among others. Fields cover section information, qualification requirements, bill of quantities, bid evaluation expert records, successful bid results, and additional relevant content. All fields follow established business specifications for bidding activities, with no specially customized units.
Constraints Imposed on Vector Models and Indexes
Daily updated data requires indexes to support incremental synchronization, to avoid excessive time costs from full reconstruction. Multiple types of business documents have varying length ranges: some are hundreds of characters long, such as successful bid announcements, while others span tens of thousands of characters, such as full bidding documents. This requires adaptable text chunking rules for different text lengths. Core business fields including sections, qualification requirements, and bills of quantities must retain contextual relevance during chunking, to prevent loss of business semantics after splitting. Differences in field structures across different document types require index configurations to support custom field mapping, ensuring core business information is correctly vectorized. This document aligns with the vector index configuration logic of FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts such as bidding and bid documents, avoids overly fragmented single-block information |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, prevents cross-chunk semantic breaks |
| maxToken | 4096 token | Adapts to the input length limit of mainstream embedding models, avoids core information loss from text truncation |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Matches the semantic matching accuracy of core business fields in bidding scenarios |
| Single Recall Limit | Top 10 entries | Covers the reference document scope for scenarios including bid evaluation and objection handling, avoids excessive redundant information |
| defaultToken | 1024 token | Serves as the default context length, balancing answer accuracy and system resource usage |
Three Common Misconfigurations
- The "Indexing" status remains unchanged for more than 30 minutes, and no corresponding document data appears in the knowledge base eventually. The cause is that appropriate chunking parameters are not configured for bidding documents, leading to chunking timeouts or incorrect vectorization of chunked content.
- After uploading using the chunk mode of the pushdata API, the status always stays at "Indexing" and cannot complete. The cause is that custom field mapping is not configured according to the business document's field structure, leading to failure to correctly extract core business fields and index verification failure.
- Overall relevance of search results is low, and unable to match corresponding sections or qualification requirements. The cause is that the similarity threshold is set too low, leading to recall of a large number of documents with low semantic relevance, or core business fields such as sections and qualifications are split during chunking, damaging business semantics.
How to Verify Proper Configuration
- Upload a single typical bidding document, review the chunked results, confirm that core fields such as sections and qualification requirements are not split across different chunks.
- Trigger an incremental indexing task, check system logs for error messages including chunking timeouts or field extraction failures, confirm the indexing process completes normally.
- Input business-related queries, review the number and relevance of recalled results, adjust the similarity threshold to a range that meets business requirements.
- Check index storage space usage, confirm that configured sharding and storage parameters adapt to daily updated data volume, avoid insufficient storage space.
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.