What the data for this category looks like
Data primarily comes from office documents in internal OA systems, employee handbooks, group policy compilations, department meeting minutes, and new hire training materials. Update cycles include scheduled policy revisions, and real-time addition and archiving of daily office documents. Unified document structure includes title, main content, publishing department, and effective date fields. Some documents have security classification markings. Each document has clear hierarchical paragraph divisions.
What constraints these characteristics impose on vector models and indexing
The wide range of document lengths requires a flexible chunking strategy. This avoids over-splitting short documents or losing content from long document chunks. Some documents have security classification markings. Permission fields must be associated during indexing. This ensures retrieval only returns vector data within the corresponding permission scope. Real-time addition and archiving of daily office documents requires an indexing update process that supports incremental synchronization. This avoids resource usage from full index rebuilding. The unified document structure simplifies extracting standardized text content before vectorization. This reduces additional preprocessing overhead.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic units of internal documents for state-owned capital investment companies. Balances chunk completeness and total index count. |
| Chunk Overlap Length | 100–150 characters | Prevents semantic breaks between adjacent text blocks. Also controls redundant vector storage from overlapping content. |
maxToken | 8000 token | Covers typical context requirements for internal office scenarios. Matches the common content length of employee inquiries. |
| Single Vector Generation Thread Count | 2–4 | Adapts to the concurrent carrying capacity of embedding interfaces. Avoids rate limit errors. |
| Vector Recall Count | Top 6 results | Matches the focused nature of internal employee inquiries. Reduces return of irrelevant results. |
defaultToken | Use model default values | Meets parameter needs for basic vectorization tasks. No extra adjustments required. |
Three common mistakes
- Phenomenon: Setting chunk length to 3000 characters results in lost trailing text blocks for some long documents. Cause: Chunk length does not match the semantic distribution of internal documents. No reasonable chunk overlap length is configured. This prevents the system from correctly identifying block start and end ranges.
- Phenomenon: Rate limit errors occur during vectorization. Tasks cannot complete normally. Cause: Single vector generation thread count is set too high. This exceeds the concurrent carrying limit of the current embedding service. Thread count is not adjusted based on actual service capacity.
- Phenomenon: Knowledge base retrieval response times are too long. Or document indexing tasks stay in a not ready state for extended periods. Cause: Incremental index updates are not enabled. Full index rebuilding uses excessive system resources. No limit is set on the number of documents processed per indexing task.
How to confirm configurations are correct
- Upload a test document with both short and long text. Verify chunked results. Confirm there are no abnormal lost chunks or over-splitting.
- Run a batch vectorization task. Check run logs. Confirm no rate limit related errors appear.
- Initiate a test retrieval for internal policies. Verify returned result relevance. Confirm response meets expectations.
- Add a new test document. Check if index update triggers automatically. Confirm no long periods of stagnation.
The 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.