What this category of data looks like
This category of data comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. The update frequency is daily. Daily documents include on-site inspection records, interrogation transcripts, evidence lists, administrative penalty prior notice letters, hearing notice letters, penalty decisions, delivery receipts, and more. Core content focuses on illegal facts and penalty decisions, requiring the formation of traceable penalty evidence chains and official documents. Fields typically include law enforcement subjects, illegal circumstances, discretion basis, document numbers, and other related items.
What constraints do these characteristics impose on the vector models and indexes link
Data sources are diverse and include both structured and unstructured content. It must adapt to both system-exported documents and transcribed text from law enforcement footage. Indexes must support incremental updates to match the daily update rhythm. Long-text documents account for a large proportion, so vector models must adapt to longer contexts to fully capture core information such as illegal facts and discretion basis. Different documents need to be linked to form evidence chains, requiring indexes to recall associated content across documents, while avoiding interference from irrelevant law enforcement records in matching results.
Configuration Settings
The following configuration items apply to FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average content length of common documents such as on-site inspection records and penalty decisions |
| Overlap Length | 100–200 characters | Prevents context breaks caused by chunk cutting, retains cross-chunk association information about illegal facts |
defaultToken | 8192 token | Meets the vector encoding requirements for single long documents, covering the full content of a single document |
maxToken | 16384 token | Supports splicing multiple associated documents to fully build context information for evidence chains |
| Similarity Threshold | 0.75–0.85 | Calibrated based on actual testing, filters irrelevant law enforcement records and accurately matches penalty-related content |
| Single Search Limit | Top 10 results | Covers sufficient associated evidence documents to avoid missing key discretion basis or illegal facts |
Three Common Misconfigurations
- Only one vector model option is displayed in the interface, but multiple vector groups correspond to a single data set. This occurs because no chunk association rules are configured. After a single long document is split into multiple vector chunks, no association identifiers are bound, making unified recall impossible.
- A custom vector model is used locally to encode documents, but the official vector model is used after uploading to the server, resulting in abnormal matching results. This occurs because embedding dimensions differ between vector models, leading to mismatched vector spaces and incorrect similarity calculations.
- A single law enforcement transcript file remains in the index for an extended period with no progress updates. This occurs because the chunk length is set too large, exceeding the
maxTokenlimit of the vector model, causing the encoding process to time out without completion.
How to Verify Proper Configuration
- Upload a single typical law enforcement transcript document, check the number of vector chunks after splitting, and confirm it matches the configured chunk length setting.
- Import test data from different sources, verify that the index incremental update function triggers normally and meets the daily update rhythm requirements.
- Enter a query related to illegal facts, check the document types and quantities of recalled results, and confirm that the configuration effects of the similarity threshold and single search limit meet expectations.
- Call the vector encoding interface, check that the returned vector dimensions match the standard dimensions of the model corresponding to the configured
defaultToken.
The values provided here are common starting points and should be evaluated against the user'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.