What this category of data looks like
Data for this category 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 records, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and delivery receipts. Each individual document has fixed fields: law enforcement subject identifier, timestamp, party information, description of illegal facts, reference to legal provisions, document number, and more. Image data includes shooting device identifier and shooting duration parameters.
What constraints do these characteristics impose on the vector model and indexing process?
Mixed structured, semi-structured, and unstructured data from multiple sources require indexes to support unified embedding adaptation for multiple content types. Daily batch-updated business data needs incremental indexing logic to avoid computing resource usage from full reconstruction. Fixed associated fields exist in individual documents. For example, penalty decisions and corresponding evidence lists must be bound together. Indexes need to support cross-document associated retrieval. Unstructured law enforcement recorder footage must first undergo text transcription before entering the vector embedding process. Adaptation rules for automatically triggering indexing after transcription need to be configured. Unique identifier fields must be stored as metadata for subsequent traceability and deduplication operations.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 token | The average text length of core documents in this category, such as penalty decisions, ranges from 3000 to 5000 characters. This setting reserves sufficient context space |
| Chunk Length | 800–1200 characters | This range adapts to the single-paragraph logical length of transcript-style documents, avoiding semantic fragmentation |
| Overlap Length | 100–150 characters | Ensures semantic continuity between adjacent chunks, preventing key information from being truncated |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance law enforcement data, retaining highly matched illegal fact and evidence associations |
| Single Search Limit | Top 10 results | Case handling requires precise matching of associated evidence. Excessive results increase review burden |
| Embedding Model Selection | Domestic vector models supported | Law enforcement data in this category involves government information. Domestic models that meet compliance requirements must be selected |
defaultToken | 2048 token | Balances context requirements for daily retrieval and computing costs |
Three Common Mistakes
- Symptom: Attempting to directly manage the vector database via MongoDB causes index synchronization failures or permission errors after performing add or delete operations. Cause: FastGPT’s vector database management must be completed through official built-in interfaces. Directly operating underlying storage will destroy the binding relationship between metadata and vectors, leading to abnormal retrieval logic.
- Symptom: When connecting to a vector model deployed via Ollama, FastGPT returns a connection refused error, but using the curl tool can normally obtain embedding results. Cause: FastGPT’s vector model calls must adapt to the request format and authentication rules specified by the platform. Ollama’s default port or path does not match the platform’s embedding request requirements, resulting in verification failure.
- Symptom: After performing an index merge operation, duplicate evidence list or penalty decision entries still appear in search results. Cause: Deduplication rules based on unique identifier fields are not configured. Only using text similarity for deduplication cannot identify different document copies with the same number.
How to Confirm the Configuration is Correct
- Upload a single penalty decision and its associated evidence list, perform a retrieval operation, and verify that the results return both corresponding documents. Confirm that the cross-document association configuration is active.
- Trigger an incremental indexing task, check that background logs only process newly added law enforcement data from the current day. Confirm that the incremental indexing logic is running normally.
- Adjust the similarity threshold and perform a retrieval operation. Observe changes in the number of returned results. Confirm that the threshold configuration is active.
- Call the vector model embedding interface, input text transcribed from law enforcement recorder footage, and verify that the returned embedding results have no format errors. Confirm that the multimodal adaptation configuration is correct.
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.