What this category of data looks like
Data sources for this category include national public resource trading platforms, local public resource trading platforms, engineering approval systems, and bidding supervision systems. The update frequency is daily. Daily documents include bidding announcements, qualification prequalification documents, bidding documents, bid documents, bid evaluation reports, successful bid candidate public notices, and successful bid result announcements. Core fields cover section identifiers, qualification requirements, bid evaluation rules, objection and complaint content, and public notice correction information. Data structure varies slightly based on project type.
Constraints on Vector Models and Indexes from These Data Characteristics
Daily updated bidding data requires indexes to support high-frequency incremental synchronization. This prevents data lag in policy-based Q&A. Multi-type non-standardized document structures require indexes to adapt to flexible text chunking logic. This accommodates paragraph differences across documents such as bidding announcements and bid evaluation reports. Professional fields exclusive to bidding, such as qualifications, performance records, and bid evaluation rules, require vector models to accurately capture term associations. Timeliness requirements for objection and complaints and public notice corrections require reducing index construction latency and avoiding Q&A matching deviations caused by incorrect section splitting.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of bidding documents, avoid splitting professional terms such as qualifications and performance records |
| Overlap Length | 100–150 characters | Retain cross-chunk section information, associated requirements, and other content to improve Q&A matching accuracy |
maxToken | 4096 token | Match the input limit of the bge-large model in FastGPT v4.17.0, adapt to long-text policy basis Q&A |
| Similarity Threshold | 0.75–0.85 calibrated via actual testing | Filter low-relevance document fragments, avoid returning invalid bidding policy content |
| Single Search Limit | Top 8 entries | Balance response speed and result completeness, cover core policy basis and related documents |
defaultToken | 2048 token | Control the context length of a single round of Q&A, avoid exceeding the model's processing capacity |
Three Common Configuration Mistakes
- Phenomenon: The exported knowledge base dataset.csv only contains the index field, with no content field. Cause: The MongoDB table and fields associated with the index are not correctly configured. Source document content is not correctly extracted as a result.
- Phenomenon: When importing text using the bge-large model, failure to adjust chunking parameters causes long text to be overly split. Cause: The default chunk length does not adapt to the professional paragraph structure of bidding documents. Key qualification or performance association information is lost as a result.
- Phenomenon: Insufficient video memory error occurs when deploying the index model locally. Cause: The graphics card device for model loading is not specified. Multiple 3090 graphics cards are not correctly allocated. A single graphics card's video memory cannot support the large model as a result.
How to Verify Correct Configuration
- Log in to the FastGPT backend to view the index synchronization log. Confirm that the daily incremental synchronization task has no errors.
- Enter a query related to bidding qualification requirements. Check whether the returned results include official document content for the corresponding project.
- Export the dataset.csv file of the knowledge base. Confirm that the file contains both index and content fields.
- View the model running log. Confirm that the specified vector model has been successfully loaded to the target device.
The values provided 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.