Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data sources for this category include enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. Update frequency is daily. Daily documents include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance receipts, and supplier complaint materials. Most document structures include fields such as procurement subject, budget amount, procurement method, scoring rules, and supplier qualification requirements. There is no unified fixed unit; values change dynamically based on the business attributes of each individual document.
Constraints for Vector Models and Indexing
Daily updated data sources require indexes to support incremental synchronization, avoiding full reconstruction that consumes excessive system resources. Mixed multi-type documents need to adapt to chunking logic for different text structures, ensuring consistent vectorization effects between long texts such as bid evaluation records and contracts, and short texts such as supplier complaint materials. Documents include key business fields such as budget amount, procurement method, and scoring rules, so field metadata must be retained during indexing for subsequent precise matching. Historical data from audit issue databases needs to support version retrospective retrieval; indexes must be associated with the business identifiers of original data to ensure retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text density of common documents such as procurement plans and bidding documents, avoiding overly fragmented or overly long single chunks that harm vectorization effects |
| Overlap Length | 100–200 characters | Compensates for context breaks caused by chunking, ensuring complete recognition of cross-chunk business logic such as scoring methods |
defaultToken | 1500 | Controls the maximum input length of a single training data entry, avoiding errors caused by exceeding interface limits |
| Single Search Limit | Top 10 entries | Covers the number of associated documents required for common compliance checks, balancing retrieval efficiency and recall scope |
maxToken | 8000 | Limits the total context length of a single retrieval, adapting to the input requirements of most open-source vector models |
Three Common Misconfigurations
- Symptom: Some vector models cannot be selected in the interface, such as the latest Embedding-3 from Zhipu. Cause: Valid interface information for the corresponding model has not been entered in the system configuration, or the deployed version does not support the latest interface specifications of this model.
- Symptom: Retrieval results only include text content, and scanned copies of bidding documents in image format cannot be recalled. Cause: Relevant configuration for image vectorization has not been enabled, and the metadata association field for images has not been bound during the indexing process.
- Symptom: Tasks remain in the pending state for a long time after creating a training order. Cause: The
defaultTokenparameter is not set reasonably, causing single training data entries to exceed the model input limit and blocking the task.
How to Confirm Correct Configuration
A typical bidding document is uploaded, indexing operations are performed, and the number of chunked text blocks is checked against expectations. A compliance check retrieval is initiated, and the number of returned results is verified against the single search limit configuration. The training order creation interface is called, test data containing business fields is passed, and normal interface return without parameter errors is confirmed. Vector model call logs are viewed, and the input token count is confirmed to not exceed the preset maxToken limit.
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.