What this category of data looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on the vector models and indexing link
Declaration materials mix long text and structured tables. Single text blocks often exceed embedding model token limits. Reasonable document splitting is required to preserve semantic integrity. Large volumes of historical file data imported in concentrated batches place demands on parallel processing capabilities for index initialization. Full synchronization must be avoided to prevent system resource overload. Sporadic updates require real-time processing, so indexes need incremental synchronization mechanisms to reduce resource waste from repeated indexing. Semantic weights vary across fields. For example, fields like investment amount and approval nodes have higher audit relevance. Targeted configuration of vector index recall rules is required.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of declaration materials, prevents single blocks from exceeding embedding model token limits |
| Overlap Length | 100–150 characters | Preserves cross-block context, ensures coherent approval logic in declaration materials is not broken by splitting |
maxToken | 8192 | Adapts to the maximum token limit of mainstream open-source embedding models, supports vectorization needs for financial tables and long text |
| Similarity Threshold | 0.72–0.80 | Differentiates compliance differences in declaration materials, filters low-relevance redundant file content |
| Single Search Limit | Top 10 entries | Covers relevant approval nodes and historical declaration records required for file review |
| Reranking | Enabled | Optimizes relevance ranking of retrieval results, improves accuracy of audit judgments |
Three Common Mistakes
- The knowledge base upload status remains in indexing for a long time. The cause is large volumes of historical file data imported in batches, and no batch indexing processing mechanism is configured. This leads to excessive system resource usage.
- Chunk loss occurs when chunk length is set to 3000 characters. The cause is a large number of formatted financial tables in declaration materials. The token count of text blocks exceeds the embedding model limit, resulting in truncation and loss of partial content.
- An embedding rate limit error occurs during vectorization. The cause is that parallel processing thread counts are not adjusted. This leads to API call frequencies exceeding service provider limits.
How to Confirm Proper Configuration
- A single typical declaration material is uploaded. The chunk preview interface is checked to confirm all text blocks have no truncated or lost content.
- Index logs are viewed to confirm that the token count of split text blocks does not exceed the set
maxTokenvalue. - A simulated audit request is submitted. The number of returned file entries is checked to match the single search limit setting.
- 10 historical files are imported in a batch. Index progress is checked to confirm no long-term stagnation, and completion time meets expectations.
The configuration values listed above are common starting points. They 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.