What Data for This Category Looks Like
Data sources include unclassified tickets from government and public service hotlines, plus supporting knowledge base documents. Ticket data is added in real time, with bulk completed and archived tickets generated daily. Knowledge base documents are updated regularly by business module. Each individual ticket data includes fields such as original request text, caller identifier, and affiliated business line. Knowledge base documents include content such as question titles, standard answers, and applicable scenarios. Data uses individual tickets and individual knowledge base documents as basic units, with no mandatory fixed format. There is a large difference in the length of request content.
How These Characteristics Create Constraints for Vector Models and Indexes
Large differences in the length of ticket request content cause fluctuations in vector dimensions after chunking. Limit the maximum length of a single chunk to ensure vector consistency. Real-time added ticket data requires indexes to support incremental updates, avoiding performance loss from full reconstruction. Government service text contains many professional terms. Select a vector model adapted to domain semantics. Tickets include metadata such as business lines and caller identifiers. Support index filtering based on metadata to narrow the recall scope. Semantic matching accuracy between knowledge base documents and ticket requests directly affects classification effects. Optimize the recall logic of the index.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxToken | 800–1200 token | Adapts to common lengths of ticket requests and knowledge base documents, avoids truncating key semantics |
defaultToken | 200–300 token | Used for vectorization of metadata fields, ensures semantic integrity of short text such as business lines |
| Chunk Length | 800–1000 characters | Matches the average length of ticket requests, reduces semantic fragmentation within a single chunk |
| Overlap Length | 100–200 characters | Prevents loss of contextual association at chunk boundaries |
| Single Search Limit | Top 10 results | Balances recall accuracy and response speed |
| Reranking | Enabled | Improves semantic matching accuracy for ticket classification |
Reference only; verify against the target FastGPT release and deployment documentation. The values listed here are common starting points, and should be validated against local sample data.
Three Common Misconfigurations
- Phenomenon: A "No available index model detected" error pops up after page refresh. Creating a new conversation agent can call the large model normally, but cannot complete ticket classification. Cause: The vector model is not bound to the dedicated index for ticket classification, or the index configuration is not saved and activated.
- Phenomenon: Calling a multimodal Embedding model returns an error in the format {"error":{"code":"Invalid"}}, and text vectorization cannot be completed. Cause: The input parameters of the multimodal model are not configured correctly, or the input text exceeds the length range supported by the model.
- Phenomenon: The index generated after configuring chunking parameters fails to match ticket requests, and the classification result has large deviations. Cause: The chunking length is set beyond the
maxTokenlimit, causing multiple unrelated semantic units to be contained in a single chunk, destroying vector semantic consistency.
How to Verify Correct Configuration
- Enter the vector model management page, confirm that the vector model adapted to the government service domain is selected, and the model status shows normal operation.
- Import a single typical ticket data, perform a manual vectorization test, and check that the generated vector dimension is consistent with the configured index dimension.
- Initiate a ticket classification test, check that the number of recalled knowledge base documents matches the expected recall scope, and there is no obviously irrelevant content.
- Submit bulk ticket data, check that the index update progress is normal, and there are no timeout or error prompts.
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.