What the data for this category looks like
This category’s data mainly comes from the 12345 citizen hotline platform, contractor business systems, official knowledge bases, public follow-up evaluation data, and local hotline management documents. Daily generated documents include request registration forms, dispatch records, handling responses, follow-up records, deferral applications, return explanations, and knowledge base entries. The platform updates data daily. Core fields include request number, contracting entity, request content, handling progress, response content, and evaluation results. The data follows the full workflow of public request processing.
Constraints on vector models and indexes
This category’s data is multi-source and heterogeneous, including structured business documents and non-standard response texts. Daily full data updates require the index system to support incremental synchronization and multi-format adaptation. Document lengths vary widely: short request texts are only a few hundred characters, while long response documents can reach thousands of characters. This demands vector chunking logic that adapts to different lengths.
Ticket handling errors directly affect public satisfaction. Vector retrieval recall accuracy must align with the rigor of business handling processes. This avoids retrieval bias that leads to incorrect responses or overdue processing. The data flows through the full public request workflow, so retrieval targets focus on three core scenarios: request type matching, handling process standardization, and response basis matching.
Configuration settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to typical lengths of request texts and response documents, avoids semantic fragmentation from overly small chunks or vector deviation from overly large chunks |
| Overlap Length | 100–200 characters | Covers semantic connections across chunks, ensures contextual coherence for long documents |
| Similarity Threshold | 0.75–0.85 | Balances recall accuracy and recall volume, meets the rigorous retrieval requirements of hotline ticket handling |
| Single Search Limit | Top 8 entries | Controls redundancy in retrieval results, avoids exceeding the context window capacity of the large language model |
maxToken | 4000–6000 token | Matches the standard context window of large language models, supports integrated response requirements for multiple ticket documents |
| Vector Model Normalization Configuration | Enabled | Adapts to non-normalized embedding models, aligns with the new configuration specifications of FastGPT v4.17.0 |
Three common misconfigurations
- Phenomenon: Relevant ticket handling documents are retrieved, but the large language model returns no relevant content. Cause:
maxTokenis not configured correctly, causing retrieved documents to exceed the model's context window and fail to load for answer generation. - Phenomenon: Single retrieval takes too long, exceeding the response requirements of business handling. Cause: No reasonable single search limit is set, sending excessive redundant documents to the model for processing. Unoptimized chunk length also leads to overly long retrieval paths for vector indexes.
- Phenomenon: Vector retrieval recall results do not match the actual request type. Cause: Vector model normalization configuration is not enabled, leading to deviations in similarity calculation of embedding vectors and failure to accurately match request-related documents.
How to verify correct configuration
- Check the vector index update log to confirm that daily incremental sync tasks run normally, matching the daily data update requirement.
- Submit a typical public request text for simulation, verify the matching degree and number of retrieval results, adjust corresponding configuration items to meet rigorous business requirements.
- Check the model context configuration to confirm that the
maxTokenvalue adapts to the total length of current business documents, avoiding context overflow. - Verify the active status of the vector model normalization configuration, confirm that the embedding vector calculation logic aligns with the specifications of FastGPT v4.17.0.
The values given 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.