What Data for This Category Looks Like
Data for this category originates from advertising platform backends, customer qualification upload systems, internal contract management platforms, and public regulatory rule databases. Four types of documents are processed daily: standardized advertising review forms, customer qualification attachments, delivery execution records, and rectification notices. Data updates align with individual advertising campaigns or natural monthly cycles. Each record contains fields including activity identifier, compliance check status, timestamp, and associated file hash. Most qualification attachments are non-text files in PDF or image formats.
Constraints on Vector Models and Indexes From These Data Characteristics
The data characteristics of this category impose clear constraints on the vector model and index workflow. Multi-source, multi-format input requires vector models to support adaptive processing of text, structured metadata, and non-text attachments. Non-fixed update triggers tied to campaigns require indexes to support incremental updates to reduce reconstruction overhead. Each individual compliance record includes multi-dimensional associated fields. Vector indexes must retain field-level association information to enable precise subsequent tracing. High compliance requirements for regulatory tracing require index retrieval logic to prioritize matching compliance-related keywords, ensuring relevance of retrieved results.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-block text length of advertising review forms and delivery records, avoids information fragmentation within chunks |
| Overlap Length | 100–200 characters | Preserves contextual association between adjacent chunks, ensures complete tracing of compliance records |
| Similarity Threshold | 0.75–0.85 | Balances recall rate and precision for compliance retrieval, meets evidence matching needs for regulatory verification |
| Single Search Limit | Top 10 entries | Controls the volume of returned search results, prevents information overload during compliance audits |
defaultToken | Calibrated via actual testing | Adapts to input length limits of multimodal vector models, ensures complete vectorization of qualification attachments |
maxToken | 4096 | Matches context window sizes of mainstream multimodal vector models, avoids truncation of critical compliance-related information |
Three Common Misconfigurations
- Misconfiguration: Vectorization requests only accept single text slices, with no batch processing enabled. Verification: The batch submission logic configuration for chunked data is reviewed, and whether FastGPT triggers vectorization requests per individual chunk is confirmed.
- Misconfiguration: Full associated content of chunked indexes cannot be retrieved during searches. Verification: Whether the index field binding configuration is enabled is checked, and whether chunked metadata is fully stored in the vector index is confirmed.
- Misconfiguration: The Alibaba multimodal-embedding-v1 model cannot be called normally after being added. Verification: Whether supplier credentials are filled out via the model channel configuration page is checked, and that AIPROXYAPITOKEN (AI Proxy Admin Token) is not confused with model supplier secrets is confirmed.
How to Verify Successful Configuration
- FastGPT v4.17.0 server startup logs are reviewed to confirm AIPROXYAPIENDPOINT and AIPROXYAPITOKEN are correctly configured, with no startup verification failure prompts.
- A single advertising review form is uploaded to trigger the vectorization workflow, and chunked results are checked against preset chunk length and overlap length settings.
- A compliance-related retrieval request is submitted, and the number of returned results and similarity scores are verified against preset configuration rules.
- Vectorization of multimodal qualification attachments is tested, confirming no model call errors occur, and generated vectors can participate in retrieval normally.
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 2026-10-19.