Data profile for this category
This category covers daily business documents from procuring entities, finance department government procurement supervision offices, procurement agencies, and evaluation experts. Documents include procurement requirements, procurement plans, bidding documents, bid submissions, bid evaluation reports, winning bid announcements, procurement contracts, and responses to inquiries. Data sources include China Government Procurement Network, local government procurement websites, finance integration systems, procuring entity budget systems, and supplier credit records. Update frequency is daily. Single project document structures vary significantly, including structured tables, professional government texts, and short announcement articles.
Constraints on vector models and indexing
Daily updated data sources require indexes to support incremental synchronization, to avoid resource consumption and delays from full reindexing. Varied document structures demand adapted chunking rules to preserve semantic integrity. Unstandardized metadata formats from multiple sources require standardization for association and retrieval in vector indexes. Government procurement documents contain many professional government terms. The encoding effect of the vector model directly impacts the accuracy of subsequent ledger summary and report generation, so index parameters need targeted adjustments.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to the professional paragraph length of government procurement documents, balance semantic integrity and chunk granularity |
| Overlap length | 50–100 characters | Prevent semantic breaks after chunking, maintain coherence of cross-chunk information |
defaultToken | 4096 | Match the average content length of most government procurement documents, adapt to vector model input limits |
maxToken | 8192 | Cover long text scenarios including multi-page bid evaluation reports, meet indexing needs for large content |
| Similarity threshold | 0.72–0.85, calibrated via actual testing | Adapt to the professional term density of government procurement documents, balance recall accuracy and coverage |
| Single search limit | Top 10 entries | Meet the information density requirements of procurement project ledger summaries, avoid interference from redundant data |
Three common configuration errors
- Index creation fails, logs show no valid vectors after file parsing | Cause: Vector model parameters were not adjusted for government procurement document professional terms, leading to semantic encoding that does not match business requirements.
- Index is empty after importing Excel files and multi-dimensional documents from Feishu folders | Cause: Corresponding file format parsing switches were not enabled, and chunking rules for table documents were not configured.
- Timeout errors occur when connecting to third-party embedding models | Cause:
defaultTokenandmaxTokenparameters were not adjusted to match model input limits, leading to requests exceeding model capacity.
How to confirm correct configuration
- Upload a single government procurement bidding document, check if valid chunking records and vector IDs exist in the vector generation logs.
- Initiate a ledger summary query, verify that returned results cover target document types including procurement requirements and winning bid announcements.
- Adjust the similarity threshold, validate that the professional term matching degree of recall results meets business expectations.
- Test the incremental synchronization function, confirm that newly added government procurement announcements can automatically complete index updates.
The values listed above are common starting points, and should be evaluated against the reader’s own samples.
Reference only; verify against the target FastGPT release and deployment documentation.
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.