What this category of data looks like
Data for this category comes from the national integrated government service platform item database, administrative licensing item list, departmental business approval systems, and electronic certificate repository. Documents processed daily include service guides, application forms, commitment letters, acceptance notices, correction notices, administrative licensing approval decisions, and license certificates. The data update frequency is daily. Fields include item code, handling conditions, application material list, approval process nodes, and no extra custom statistical units. All data is generated around the full processing workflow of legal licensing items.
What constraints these characteristics impose on the vector model and indexing link
The data characteristics of this category impose multiple constraints on the vector model and indexing link. First, daily updated data sources require incremental indexing configuration to avoid resource consumption and time delays caused by full reindexing. Second, multiple document types including service guides, application forms, and electronic certificates require adapted text extraction and standardization processing for different formats, to ensure unstructured content can be effectively vectorized. Third, data is strictly generated around legal licensing items, requiring vector retrieval to be limited to the corresponding item scope, to avoid process guidance deviations from cross-item matching. Fourth, significant field differences exist between application materials and service guides, requiring targeted text splitting rules to retain key business information.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to the single-paragraph business information volume of administrative licensing documents, avoid splitting that breaks item and process boundaries |
| Overlap length | 100–200 characters | Retain contextual association between adjacent split chunks, ensure coherence of approval process nodes |
| Similarity threshold | 0.75–0.85 | Match the matching requirements of administrative licensing items, filter low-relevance non-target item content |
| Single search limit | Top 10 entries | Cover the number of core nodes in common approval processes, ensure retrieval results sufficiently support complete guidance |
maxToken | 4096 | Adapt to the input limit of the Qwen3-Embedding-8B model, comply with the parameter specifications of FastGPT v4.17.0 |
defaultToken | 512 | Configure the default embedding vector dimension, adapt to the text characteristics of government affairs documents |
Three common mistakes
- Phenomenon: A database connection error occurs when deploying Milvus, with logs showing that PostgreSQL-related dependencies cannot be loaded. Cause: The default Compose file with integrated PostgreSQL metadata storage was used, and configurations were not modified based on the actual deployment environment, leading to startup failure when no local PostgreSQL environment exists.
- Phenomenon: Connection timeouts or format verification failures are returned when connecting to the Qwen3-Embedding-8B model deployed via VLLM. Cause: The local access address and port of the model were not correctly filled in the FastGPT configuration interface, or cross-process access permissions for the model were not enabled.
- Phenomenon: The disk usage of the local FastGPT knowledge base cannot be viewed, and the storage proportions of original files, split chunks, and embedded vectors cannot be distinguished. Cause: The system's disk monitoring and statistics function was not enabled, or access permissions for the storage path were not configured to obtain usage data.
How to confirm the configuration is correct
- Upload a standard service guide document, run the vectorization process, and verify that the length of the split text blocks matches the configured chunk length range.
- Initiate a query for a specific licensing item, and verify that the number of retrieval results conforms to the single search limit configuration.
- View the vector database connection logs, confirm that the connection to the Qwen3-Embedding-8B model is normal, with no timeout errors.
- Check the system storage directory, confirm that original files, split chunks, and embedded vectors are stored in their preset paths respectively, and the usage proportions of each part can be viewed via system tools.
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.