What this category of data looks like
This category of data originates from stock documents including internal official document templates, compliance document libraries, project declaration materials, and contract templates within government agencies and state-owned enterprises. Update schedules align with internal compliance requirements: regular batch updates occur each quarter, and emergency updates trigger when temporary compliance changes take effect. A single document’s structure includes fields such as document number, issuing unit, completion date, main text, and attachments. Most fields use units like characters, copies, and serial numbers.
What constraints these characteristics impose on vector models and indexing
Fixed format fields bind to a document’s main text. When splitting text chunks, retain field associations to avoid breaking context integrity. Updates combine batch compliance updates and temporary change triggers, so indexing systems must support incremental synchronization and rapid rebuilding. Single document lengths vary widely. Long compliance files require adaptation for long-text vector splitting, while indexing systems must support text extraction from multiple attachment types.
How to set the configuration
This content adapts to FastGPT v4.17.0 vector model and indexing configurations. The specific recommendations are as follows:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph and section boundaries of government and state-owned enterprise official documents, avoiding damage to complete semantics |
| Overlap Length | 100–150 characters | Compensates for context gaps after long text chunking, adapting to the long paragraph structure of compliance documents |
defaultToken | 4096 token | Matches the default token limit of mainstream open-source vector models, adapting to the single-chunk length of most government and state-owned enterprise documents |
| Maximum Context | 8000–12000 characters | Accommodates the core content of a single document, meeting the context reference needs during drafting |
| Single Search Limit | Top 10 results | Balances recall efficiency and content coverage, adapting to the classified retrieval scenario of government and state-owned enterprise documents |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant templates, ensuring the compliance and relevance of recalled content |
Three common misconfigurations
- A 60-second timeout error displays on the interface when switching the knowledge base index. The root cause is that incremental synchronization is not enabled during index rebuilding. Full scanning of stock documents causes excessive resource occupation, triggering the system’s timeout limit.
- The knowledge base has a small content volume but remains in the training or rebuilding state for an extended period, and the index cannot be switched. The root cause is that a full index update strategy is configured, and the synchronization logic is not optimized for a small number of documents. The system repeatedly performs invalid full verification.
- A parameter error returns when adding indexes in batches, and storage into the index cannot be completed. The root cause is that the associated fields of document chunks are not correctly specified, leading to missing binding relationships between the index and the original text, triggering format verification failure.
How to confirm the configuration is correct
- Review the chunk length and overlap length parameters on the index configuration page, confirming that the values match the typical length of current documents.
- Run a small-batch document storage test, checking whether index rebuilding time meets expectations and no timeout errors occur.
- Initiate a retrieval request, verifying that the number of recalled results aligns with the similarity threshold setting logic.
- Review the index update log, confirming that the incremental synchronization function triggers normally and no redundant full rebuild operations occur.
The values provided are common starting points and should be measured against samples used in local deployments.
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.