Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
This category’s typical data comes from daily work outputs of urban management law enforcement bureaus, street comprehensive law enforcement teams, sanitation centers, and grid inspectors. Daily documents include inspection records, case filing approval forms, on-site photos, order for correction notices, penalty decisions, case closure reports, and more. Data sources cover urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, and public complaint tickets. Data updates occur daily, covering the latest information across the entire process of on-site evidence collection, case circulation, and rectification execution. Document structures include structured fields, unstructured text, and mixed image content, with some fields linked to specific business logic for law enforcement scenarios.
Constraints on Knowledge Base Retrieval and Recall
Multi-source mixed data requires retrieval to support joint recall of cross-platform, multi-type documents, to avoid missing key clues from grid tickets or video surveillance. Daily data updates require the retrieval link to support incremental synchronization, ensuring that the latest case records and rectification information can be quickly added to the retrieval scope. Mixed documents of unstructured on-site photos and text require retrieval to cover text semantics, as well as associate image metadata or associated description content. The precision requirement for law enforcement scenarios means retrieval results must strictly match the violation type and responsible subject of the case, to avoid irrelevant content that disrupts on-site disposal judgment. The party building learning Q&A scenario also requires associating law enforcement regulations with party building learning content, ensuring responses meet both business requirements and party building orientation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precision requirement for urban management law enforcement cases, avoids recalling irrelevant daily inspection records, while retaining valid case-related content |
limit | Top 8 entries | Balances context length and information coverage, adapts to the multi-dimensional reference needs of law enforcement scenarios, avoids overly long contexts causing model confusion |
searchMode | Hybrid retrieval | Supports joint recall of text, structured fields, and image metadata, adapts to the retrieval needs of multi-type documents for this category |
usingReRank | Enabled | Re-ranks initial recall results, corrects semantic deviations, and improves the accuracy of violation type judgment and responsible subject matching |
datasetSearchUsingExtensionQuery | Enabled | Generates extended query terms based on the party building learning Q&A scenario, associates law enforcement regulations with party building requirement content, and meets scenario-based retrieval needs |
collectionIds | Bind collection IDs of corresponding urban management law enforcement and party building knowledge bases | Limits the retrieval scope, avoids mixing irrelevant business data, and ensures retrieval results focus on the target scenario |
Three Common Mistakes
- Complex law enforcement scenario questions receive inaccurate responses, with a low effective matching rate for recall results. Cause: The
similaritythreshold is not adjusted. An overly high threshold causes valid case records to be missed, while an overly low threshold mixes in irrelevant inspection content. - Semantic retrieval fails to locate knowledge blocks containing specific business keywords. Cause:
datasetSearchUsingExtensionQueryis not enabled, and no extended query terms are added for the party building learning Q&A scenario, leading to failure to correctly identify target keywords. - An error indicating no answer found is displayed when retrieving image addresses, and normal responses are returned after changing the conversation template. Cause: Multimodal retrieval support is not configured. Only semantic matching for pure text content is performed, and description text or metadata associated with image addresses is not processed.
How to Verify Correct Configuration
- Submit 5 typical law enforcement scenario questions and 3 party building learning related questions, and check whether recall results cover target business and party building content.
- View the knowledge base synchronization log to confirm whether daily newly added case documents have automatically completed retrieval index updates.
- Pass the specified
collectionIdsparameter when calling the API, and verify that the retrieval scope only includes urban management law enforcement and party building related knowledge base collections. - Upload test on-site photo associated documents, and verify that corresponding law enforcement regulation content can be recalled through image metadata.
The values provided here are common starting points and should be evaluated 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.