Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data for this category typically originates from urban management law enforcement platforms, city operation grid platforms, video surveillance systems, and public complaint tickets. Updates occur daily. Common documents include inspection records, case filing approval forms, on-site photos, rectification notices, penalty decisions, and closing reports. Fields covered include case number, violation type, responsible entity, rectification execution status, and more. Most materials are a mix of structured and unstructured business documents.
How These Data Characteristics Impact Vector Models and Indexing
Daily high-frequency data updates require indexes to support incremental synchronization, to avoid excessive resource usage from full reindexing. Mixed structured and unstructured documents require adapted vector extraction logic for different formats. Multimedia data such as on-site photos needs additional preprocessing. Inconsistent fields across multiple data sources require unified mapping before index construction. The timeliness of law enforcement cases requires vector retrieval response speed to align with business processes, without lagging behind ledger summary requirements. Case-related evidence requires precise matching, with clear requirements for recall accuracy and recall scope.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
defaultToken | 4096 | Matches the input limit of most domestic vector models, ensures complete document embedding |
maxToken | 8192 | Adapts to multi-document splicing needs during ledger summary, avoids truncation of critical law enforcement information |
| Chunk Length | 800–1200 characters | Adapts to the average length of documents such as inspection records and penalty decisions, prevents single-block information breakage |
| Overlap Length | 100–150 characters | Retains contextual association between chunks, prevents cross-chunk case logic from being split |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances precision and recall rate for evidence matching in law enforcement cases |
| Single Search Limit | Top 10 entries | Controls the volume of retrieval results, adapts to the rapid ledger summary needs of law enforcement work |
Three Common Configuration Mistakes
- Vector retrieval results do not match expected counts. The cause is failing to configure an incremental indexing strategy for daily incrementally updated data sources, and mistakenly using full indexing which causes data to lag behind business ledger updates.
- Permission errors occur when non-admin accounts call the vector model. The FastGPT interface returns a permission exception prompt. The cause is failing to configure multi-role access permissions for the vector database, only allowing the root account to use it.
- Vector model calls are rejected, but curl tests pass. The FastGPT interface returns a connection timeout or parameter error. The cause is incorrect configuration of the
defaultTokenparameter, or failure to correctly set the vector model access key in environment variables.
How to Verify Correct Configuration
- Manually import a typical inspection record document, check if core case information is fully retained after vector embedding, with no obvious truncation.
- Submit the day's complaint ticket data, verify that the index completes incremental updates automatically, with no full reindexing prompts.
- Adjust the similarity threshold, retrieve similar violation cases, check that the coverage of recall results matches business expectations.
- View the vector model call logs, confirm that returned results match the output of curl tests, with no abnormal errors.
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.