What Data for This Category Looks Like
This category of data primarily comes from the "Random Inspection, Public Disclosure" supervision platform, enterprise credit information public disclosure system, industry supervision systems, and law enforcement officer directory. Daily documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, inspection result public notices, and similar content. The data is a mix of structured fields and unstructured text, with a typical update frequency of monthly.
What Constraints Do These Characteristics Impose on the Vector Models and Indexing Link
The monthly updated data characteristics require the index to support incremental synchronization mechanisms, to avoid redundant computation caused by full index rebuilding. The mixed structure of multiple document types requires adapting chunking logic for structured fields and unstructured text, ensuring that content such as inspection items and target lists can be accurately split and indexed. High-frequency accessed law enforcement officer directory data requires limiting index resource usage to guarantee retrieval response speed. The policy Q&A scenario needs to match the semantic recall accuracy of supervision policies, preventing interference from irrelevant inspection records or public notice content in results.
How to Configure Settings
FastGPT v4.17.0 supports precise adjustment of the following configuration items:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of daily documents, covering typical content such as inspection item lists and on-site inspection records |
| Maximum Paragraph Depth | 3 levels | Matches conventional usage scenarios for automatic paragraph recognition, avoiding excessive splitting or missing key information |
| Index Dimension | 128 dimensions | Meets the recall requirements for high-frequency retrieval in this category, balancing semantic accuracy and system resource usage |
defaultToken | Calibrated based on actual testing | Adapts to the input format requirements of multimodal embedding models, avoiding token limit overflow errors |
maxToken | 8192 token | Matches the context window upper limit of mainstream large models, ensuring complete recall for policy Q&A |
| Similarity Threshold | 0.75–0.85 | Ensures semantic matching accuracy for supervision policy Q&A, filtering low-relevance retrieval results |
Three Common Configuration Mistakes
- Symptom: An error in
{"error":{"code":"Invalid"}}format is returned when testing with a multimodal embedding model. Cause: ThedefaultTokenparameter is not configured correctly, causing the model input format to fail to meet requirements. - Symptom: The number of results returned by knowledge base retrieval is far lower than expected. Cause: The index dimension is not adjusted to a value suitable for this category of data, resulting in a too narrow recall range that fails to cover all relevant policy documents.
- Symptom: Full index rebuilding takes too long to complete monthly data update tasks on schedule. Cause: Incremental synchronization configuration is not enabled, and index generation processes are repeatedly executed on historical data, wasting system resources.
How to Confirm Correct Configuration
- Upload a single inspection item list document to verify that the chunking results match the set chunk length and maximum paragraph depth.
- Call the test interface to input questions related to supervision policies, and check that the number of returned results matches the index dimension configuration.
- Perform an incremental synchronization operation to verify that only newly added monthly data is included in the index, and historical data is not processed repeatedly.
- Initiate a test request after connecting to the multimodal embedding model, and confirm that no format error is returned.
The values provided are common starting points and should be measured 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.