What this category's data looks like
Data for this category primarily comes from advertising platforms, attribution systems, CRM leads, and landing page data. Update frequencies are typically daily or real-time. Commonly processed documents include structured daily and weekly reports, price adjustment records, material test sheets, and unstructured optimization review texts. Data fields include metrics such as impressions, clicks, conversion cost, and budget balance. Units include counts, monetary amounts, and others. Document structure combines tabular metric data and paragraph-style analysis content. This content aligns with the vector model and index configuration logic of FastGPT v4.17.0.
What constraints do these characteristics impose on vector models and indexing?
Since data update frequencies range from real-time to daily, indexes must support incremental updates to accommodate high-frequency delivery data synchronization. Documents contain both structured metrics and unstructured review texts. Field associations must be preserved during vector chunking to avoid separating metrics from their corresponding review logic. Multi-field metric data creates significant context load. The context handling capacity of the vector model must match business requirements. Retrieval result accuracy directly impacts optimization decisions. Index recall rules must be tailored to delivery effect optimization scenarios to avoid introducing irrelevant historical data that disrupts analysis.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the per-paragraph information density of daily reports and review documents, avoids splicing across fields |
| Overlap Length | 120–180 characters | Preserves operational context for price adjustment records and material test sheets, prevents logic separation from chunking |
| Similarity Threshold | 0.78–0.85 | Filters irrelevant historical data, ensures retrieval results match current optimization scenarios |
| Single Search Limit | Top 6 entries | Balances information comprehensiveness and retrieval speed, meets the needs of optimizers for quick problem location |
defaultToken | 4096 | Accommodates multi-field advertising metrics and review texts, avoids content truncation |
| Maximum Context | Calibrated via actual testing | Matches the capabilities of the deployed embedding model, ensures full use of retrieval results during Q&A |
Three common errors
- Symptom: Retrieval results do not include content related to image materials, or images cannot be retrieved normally. Cause: Image vectorization configuration is not enabled, or the embedding model does not support image modalities.
- Symptom: The specified embedding model cannot be selected in the interface, such as the latest text embedding model. Cause: The embedding model API configuration is not correctly associated, or the locally deployed model version is incompatible.
- Symptom: A connection timeout error is returned when calling the vector model, and network connectivity and token configuration have been confirmed to be correct. Cause: The
defaultTokenconfiguration exceeds the model's supported upper limit, or the vector service port is not open.
How to confirm the configuration is properly set
- Upload a typical price adjustment record document, run a retrieval test, and confirm that the similarity of returned results falls within the preset threshold range.
- View the vector service runtime logs, confirm that there are no errors in embedding model calls, and that
defaultTokenusage does not exceed the configured upper limit. - Import a material test sheet containing custom fields, check that field information is fully retained after index creation.
- Trigger an incremental update task, confirm that real-time updated advertising data can be retrieved normally.
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 2026-10-19.