What the data for this category looks like
Data for in-terminal natural language search comes from single user search requests on financial terminals, insurance sales tools, wealth management advisory apps, and similar devices. Each data unit is a natural language query text entered by the user, with a small amount of metadata including session ID, query timestamp, terminal device identifier, and other fields. Data is generated in real time as users perform operations, with no bulk import scenarios. The document structure is flat: a single text entry plus metadata fields, with no nested levels. Query text lengths vary widely. It is recommended to calculate or test based on one’s own samples before setting values. All metadata fields use the string data type.
What constraints these characteristics impose on the vector model and indexing pipeline
Because data is generated in real time and search results must be returned quickly, the vector model and indexing pipeline must meet low-latency requirements to avoid impacting user experience in scenarios like financial terminals and insurance sales tools. Because most query texts are short, the maximum sequence length of the vector model must fit this length range to prevent key information from being truncated. Because query content includes colloquial expressions, typos, and other non-standard text, the vector model must have adaptation capabilities for Chinese colloquial scenarios. Because data includes metadata fields, the index must support fast filtering based on metadata to narrow the recall scope. Because there are no bulk import scenarios for the data, the index must support incremental updates to avoid the high overhead of full index rebuilding.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model_name | Qwen/Qwen3-Embedding-8B | Supports Chinese natural language search, is compatible with indexing and reranking tasks, and is lightweight for in-terminal deployment |
chunk_max_length | 800–1024 characters | Matches the maximum sequence length limits of most open-source vector models to avoid text truncation |
recall_top_k | Top 8–12 results | Balances the response speed of terminal search and recall coverage, and fits single-turn query scenarios |
similarity_threshold | 0.70–0.82 | Filters low-match results, and aligns with the clear intent of terminal user queries |
index_update_mode | Incremental update | Fits the real-time update rhythm of terminal search data, and reduces the overhead of full index rebuilding |
rerank_top_n | Top 3–5 results | Focuses on high-match results, and improves result display efficiency in terminal scenarios |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Each scenario requires specific analysis. It is recommended to test on one’s own samples before finalizing settings.
Three common configuration mistakes
- Issue: After configuring a vector model with the same name, existing configurations are overwritten, and multiple instances of the same model cannot be used simultaneously. Cause: Multi-model instance isolation configuration is not enabled. The system uses the model name as the unique identifier by default.
- Issue: In public network deployment scenarios of version 4.9.12, terminal search results display malformed Markdown content, with first and second level headings displayed incorrectly. Cause: No preprocessing is performed on source data format markers, and the data is passed directly to the vector model for index generation.
- Issue: After importing Excel source data, individual data blocks are too large, leading to vector generation timeouts or reduced recall accuracy. Cause: Default chunking parameters are used, and the chunking threshold is not adjusted based on the actual length of Excel row data.
How to confirm configurations are correctly set
- A test query is initiated, system logs are reviewed, and the configured
embedding_model_nameis confirmed to be called correctly. - The index monitoring panel is viewed, incremental update tasks are confirmed to execute normally per the configured update strategy, with no failed errors reported.
- Test Excel data is imported, the chunked text length is reviewed, and the value is confirmed to match the configured
chunk_max_length. - Multiple concurrent queries are triggered, system response latency is checked, and compliance with the performance requirements of terminal scenarios is confirmed.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.