Vector Models and Indexing for Advertising and Marketing

Data Characteristics of This Category

Data sources for advertising and marketing intelligent due diligence reports include original delivery logs from advertising placement platforms, qualification documents from cooperating parties, third-party public opinion monitoring datasets, and archived industry marketing case files.

Documents use a mixed format of structured tables and compliance explanation text. A single report includes multiple business modules: delivery effectiveness, compliance review, and partner qualifications. Fields include 素材ID, 投放周期, 曝光量级, 转化数据, 合规备案编号, 合作方名称, and others. Common units include times, yuan, and hours.

Update rhythms vary: structured delivery data is updated daily, public opinion monitoring data is updated minute-by-minute, qualification documents are updated when partner contracts renew, and annual archived reports are updated quarterly.

Constraints for Vector Models and Indexing

The mixed structure and varied update frequencies of this category's data create multiple constraints for vector models and indexing.

Documents mixing structured numerical fields and compliance text require vector models to adapt to vector mapping for both text semantics and structured features. Minute-by-minute updated public opinion data and daily updated delivery ledgers require indexes to support both incremental and full updates running in parallel.

Long document segmentation must retain contextual links for key fields such as 投放周期 and 合规备案编号. This avoids losing business relevance after splitting. Duplicate entries across multiple data sources must be removed using unique identifier fields to ensure the accuracy of recall results.

Configuration Recommendations

The following table outlines configuration items, recommended values, and their rationales:

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Key business fields of advertising and marketing due diligence reports have strong relevance. A segment length in this range balances semantic integrity and retrieval efficiency
`chunk_overlap`	`100–150 characters`	Retains cross-segment key information such as `投放周期` and `素材ID`. This avoids breaking business context coherence after splitting
`vector_model`	`Domestic multimodal vector model support`	Adapts to due diligence report data mixing text and structured fields. It meets domestic compliance and performance requirements
`index_type`	`HNSW index`	Balances recall speed and accuracy. It adapts to minute-by-minute updated public opinion data and batch-updated ledger data
`recall_top_k`	`Top 10–15 entries`	Covers multi-dimensional delivery data needed for due diligence reports. It avoids excessive results increasing context processing pressure
`dedup_enabled`	`Enabled`	Eliminates duplicate `素材ID` records from multiple data sources. This improves the accuracy of recall results

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Common Misconfigurations

Three frequent misconfigurations are listed below:

Symptom: A 401 Unauthorized error is returned when calling the vector model. Curl testing works normally, but FastGPT calls fail. Cause: Vector model access keys or proxy settings are not configured correctly. FastGPT request headers do not carry valid authentication information.
Symptom: A 401 error is displayed when accessing the m3e vector model, and vector generation cannot complete. Cause: The model service API key is configured incorrectly, or access permissions for the target model are not enabled.
Symptom: Duplicate 素材ID records remain after knowledge base index merging. Redundant content appears in recall results. Cause: The dedup_enabled configuration item is not enabled, or the deduplication unique identifier field is not set to 素材ID.

How to Validate Successful Configuration

Use the following steps to confirm that configurations are set correctly:

Review vector model call logs. Confirm that every request carries valid authentication information, and no 401 or 403 errors are returned.
Run an incremental index update. Check whether index update duration and updated data volume match the configured update rhythm.
Randomly select a due diligence report. Split the report and verify that segmentation results retain contextual links for key fields such as 投放周期 and 合规备案编号.
Trigger an index merge operation. Confirm that the number of records after deduplication aligns with the preset deduplication logic.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.