What Data for This Category Looks Like
Data related to service inquiries for state-owned capital investment companies comes from internal service process specifications, policy documents issued by state-owned assets supervision authorities, publicly released service guidelines, and Q&A records from subordinate enterprises and employees. Update frequency: Policy documents are updated quarterly or per regulatory requirements. Internal service processes are adjusted as needed. Inquiry records are retained in real time. Document structure includes fields such as policy document number, issuing entity, effective date, service steps, required materials, and Q&A entries. Document numbers follow the format "State-owned Assets Reform [Year] XX No.", effective dates use the YYYY-MM-DD format, and material lists are counted by individual items.
Constraints on the Vector Models and Indexing Link
Most policy documents are long texts with strict hierarchical logic. Configure chunk length to adapt to paragraph integrity and avoid splitting core service processes. Inquiry records exist as Q&A pairs. Retain context association between chunks, so set overlap length to a reasonable range. Metadata fields such as effective date and document number filter expired or irrelevant content. Configure indexing to support metadata-associated retrieval. Data updates occur in batches. Adapt incremental indexing to on-demand trigger update frequencies to avoid resource waste from full indexing.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of policy documents and service processes, avoid splitting complete logic |
| Overlap Length | 100–200 characters | Retain context association across chunks, match the Q&A coherence of inquiry records |
| Similarity Threshold | Calibrated via actual testing | Adapt to the rigor of state-owned assets policies, avoid irrelevant content matches |
| Single Search Limit | Top 10 entries | Control returned information volume, meet precise service inquiry needs |
defaultToken | 4000 token | Adapt to context splicing requirements for long-text policies |
maxToken | 8000 token | Cover complete service process and policy combination content |
Three Common Misconfigurations
- Phenomenon: Vectorization requests carry only a single text slice, and batch processing optimization for the vectorization service cannot be triggered. Cause: Fail to adjust FastGPT's slice submission logic. The default setting sends requests via single slices, which does not adapt to the batch receiving requirements of the vectorization service.
- Phenomenon: Search recall results fail to cover all relevant service inquiry documents, and accuracy is insufficient. Cause: Fail to set reasonable chunk length and overlap ratio based on the characteristics of policy documents for state-owned capital investment companies, resulting in split and broken document logic.
- Phenomenon: Matching degree between vector retrieval results and business questions does not meet expectations, and irrelevant content is recalled. Cause: Fail to configure index filtering rules combined with state-owned assets policy metadata (such as document number, effective date), and only rely on pure text vector matching.
How to Confirm Proper Configuration
- Review vectorization service logs to confirm that the slice format carried by requests meets configuration requirements, or that batch slice configuration has taken effect.
- Submit a typical service inquiry question, check whether recall results include relevant policy documents and service guidelines.
- Review index construction progress logs to confirm that incremental updates have automatically triggered at the configured update frequency.
- Check whether
defaultTokenandmaxTokenconfigurations match current business long-text processing requirements.
The values provided 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.