What the data for this category looks like
This category's data primarily comes from state-owned assets supervision and administration commission investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. It covers documents including annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, post-investment evaluation reports and more. Data updates on a quarterly cycle. Core fields include project number, approval nodes, investment amount, post-investment operation data, post-evaluation conclusions and others. Single document length varies widely, spanning from thousands-of-word special reports to dozens-of-page comprehensive evaluation materials.
What constraints these characteristics impose on vector models and indexing workflows
Data for this category is multi-source, heterogeneous, with fixed and broad update cycles. This creates multiple constraints for vector model and indexing workflows. Documents from multiple systems have inconsistent formats, so field mapping rules for different data sources must be adapted. The quarterly update rhythm requires indexes to support regular incremental refreshes, to avoid resource consumption from full rebuilds. Single document length varies widely, so balance must be struck between the completeness of long-text splitting and the semantic integrity of short documents. Vector matching for core business fields must anchor to specific business dimensions, to avoid generalized retrieval bias.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide document length range of this category, avoids semantic fragmentation or chunk loss in long documents |
| Overlap Length | 100–150 characters | Connects the semantics of adjacent chunks, ensures continuity of vector retrieval for long documents |
defaultToken | 8192 | Matches the context window upper limit of mainstream embedding models, meets embedding requirements for most project documents |
maxToken | 16384 | Supports full embedding of ultra-long documents such as post-evaluation reports, avoids truncation of critical business content |
| Single Recall Limit | Top 10 results | Covers retrieval needs for investment decision meeting materials and multiple associated feasibility study reports, balances retrieval efficiency and recall scope |
| Index Refresh Frequency | Quarterly | Matches the quarterly data update rhythm of this category, reduces unnecessary full index rebuilds |
Three common misconfigurations
- Issue: Setting chunk length to 3000 characters causes chunk loss in some long documents. Cause: The single chunk length exceeds the
maxTokenlimit of the embedding model, leading to failed embedding and missing corresponding chunks. - Issue: Rate limit errors trigger during vectorization, with logs showing excessive request frequency. Cause: Concurrency thread configurations are not adjusted, and parallel embedding of multiple documents exceeds the service provider's interface rate limiting threshold.
- Issue: Knowledge base retrieval response times are too long, or question-answer pair extraction tasks remain in the "indexing" state for extended periods. Cause: Chunk length settings do not match business documents, leading to overly long single chunks that increase vector computation time, or index refreshes are not performed quarterly, leading to accumulation of redundant data.
How to confirm proper configuration
- Upload the longest single project feasibility study report, check that the chunk preview covers all content without truncation or loss.
- Simulate batch upload of quarterly updated documents, observe that vectorization task progress bars do not abort unexpectedly, and no rate limit errors trigger.
- Retrieve core business keywords, verify that recall results include valid chunks from the corresponding documents.
- Check index refresh logs, confirm that incremental updates trigger automatically on a quarterly cycle, with no abnormal full rebuild records.
The values provided 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.