What data looks like for this category
This category of data is primarily sourced from the 12345 citizen hotline platform, contractor business systems, internal knowledge bases, follow-up evaluation data, and local hotline management regulations. Data is updated daily, and covers document types including request registration forms, dispatch records, handling responses, follow-up records, delay applications, return explanations, and knowledge base entries. Fields include request number, contracting unit, request content, handling status, follow-up result, and others. Some documents are structured forms, while others are unstructured colloquial text.
Constraints imposed by characteristics on vector models and indexing
Daily updated data requires incremental indexing configurations adapted to high-frequency synchronization scenarios, to avoid performance losses from full reindexing. Mixed input of multiple document types requires vector models and indexing workflows to balance precise matching of structured fields and semantic understanding of unstructured colloquial text. Decentralized data sources require a unified preprocessing workflow to ensure consistent formatting of ticket data from different sources. Index construction stability directly affects ticket processing efficiency; any delay or error may cause tickets to exceed deadlines or responses to be rejected.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to the length of colloquial text in hotline requests, avoid semantic fragmentation |
| Overlap length | 100–150 characters | Preserve contextual connections across chunks, improve semantic coherence |
| Similarity threshold | 0.72–0.85 | Calibrated based on actual testing, balances recall accuracy and recall coverage |
| Single retrieval maximum | Top 10 entries | Control computational load per retrieval round, adapts to real-time processing requirements for hotline tickets |
defaultToken | 10000 | Adapt to the total token limit for batch ticket documents, avoid truncation of critical information |
maxToken | 12000 | Matches the context window limit of large language models, ensures complete semantic input |
Three common mistakes
- Uploading PDF-formatted ticket documents results in excessively long vectorization task duration. The cause is failure to optimize chunking strategies for short-text requests, leading to more generated chunks than expected.
- After importing 100,000 CSV-formatted ticket datasets, only 90,000 entries are displayed after vectorization completes. The cause is some data with formatting errors or empty fields, and automatic filtering of abnormal data was not enabled.
- A collection is shown as created successfully, but the index status always displays as not established. The cause is inconsistent versioning of vector model dependencies between container and local environments, leading to abnormal index construction logic.
How to confirm configurations are properly set
- Upload a single typical 12345 request document, verify vectorization duration aligns with preset performance expectations.
- Import a test ticket dataset of fixed scale, confirm the number of entries after vectorization matches the source data exactly.
- Submit a retrieval request targeting a specific request, check that the semantic matching degree between returned index results and the request content meets the preset threshold.
- Run a batch vectorization task in the container environment, confirm results are consistent with those from local runs.
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.