What does data in this category look like?
State-owned capital operation company administrative inquiry and reply data comes from three main sources: policy documents issued by state-owned assets supervision and regulation authorities, internal enterprise administrative procedure manuals, historical inquiry tickets and official reply records. Data update cycles vary: policy documents are updated regularly per regulatory requirements, administrative guidelines are revised quarterly, and historical tickets are added in real time as users submit inquiries. Document structures include fields such as policy document number, issuing unit, effective date, administrative steps, required materials, and reply wording guidelines. Some long documents include chapter numbers and clause explanations, and units use standard official document formatting and common administrative terminology.
What constraints do these characteristics impose on the vector model and indexing workflow?
Three constraints are imposed on the vector model and indexing workflow by the characteristics of administrative inquiry data for state-owned capital operation companies. First, policy documents and administrative guidelines have long paragraph structures. When splitting text into chunks, semantic integrity must be prioritized to avoid losing cross-chapter associated logic. Second, varying data update cycles require indexes to support both full batch updates and incremental real-time updates, to meet synchronization needs for different data types. Third, the multi-field metadata structure requires support for filtering searches by dimensions such as document number and issuing unit, to ensure that inquiring users can obtain precise content within the target scope.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long paragraph structure of state-owned capital operation policies and administrative guidelines, avoids semantic fragmentation |
| Overlap Length | 100–150 characters | Retains cross-chunk contextual connections, ensures coherence of policy clauses and administrative steps |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance non-target policy entries, while covering inquiry questions with different phrasing |
| Single Search Limit | Top 10 entries | Focuses on core administrative reference content, avoids excessive redundant information interfering with inquiry replies |
maxToken | 8192 token | Adapts to context limits of mainstream embedding models, accommodates complete administrative procedures and policy content |
defaultToken | Set per embedding model requirements | Ensures context length of embedding requests complies with model input specifications, avoids truncation errors |
Three common mistakes
- Phenomenon: The number of search return results is far lower than expected, or an "no matching content" error appears. Cause: Chunk length is not adjusted for long documents related to state-owned capital operations, leading to loss of core semantic connections when long policies or administrative guidelines are split.
- Phenomenon: Search response times out, exceeding 10 seconds. Cause: Single search limit is not configured, and lightweight reranking models are not enabled, leading to excessive candidate entries to process after vector retrieval.
- Phenomenon: Errors still occur after configuring the embedding model. Cause: The
defaultTokenparameter is not correctly specified in system configurations, leading to embedding request context length exceeding model allowable limits.
How to confirm configurations are correct
- Upload one typical state-owned capital operation administrative guideline document, check that chunked text retains complete procedure steps without obvious semantic breaks.
- Submit a test inquiry about internal administrative procedures, verify that similarity scores of search results fall within the preset threshold range.
- Perform an incremental index update, confirm that newly uploaded policy documents can be retrieved quickly without delay.
- Check FastGPT v4.17.0 system logs, confirm that the
maxTokenparameter in embedding model requests does not trigger truncation errors.
The values provided here are common starting points, and should be measured 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.