What the Data for This Category Looks Like
Data sources for the brand agency operation scenario include brand e-commerce backend transaction data, social media platform user interaction data, public industry research reports for the beauty and personal care track, and public competitor operation data. Update frequencies are divided into multiple tiers: e-commerce transaction data is updated daily, social media interaction data is updated hourly, industry research reports are released irregularly, and competitor operation data is crawled weekly. Document structures include structured fields (such as SKU code, transaction amount, interaction volume) and unstructured text (such as social media copy, user reviews, research report chapters). Some documents are accompanied by text summaries of multimodal content.
What Constraints These Characteristics Impose on Vector Models and Indexing
The data types in the brand agency operation scenario are mixed, with both structured transaction and interaction fields and unstructured text content. This requires vector indexes to support hybrid retrieval. The update frequencies of different data sources vary widely. Social media data with high real-time requirements needs near-real-time indexing, while static industry research reports can use batch full indexing. This requires the system to support flexible switching between incremental and full indexing. The document length spans a wide range, from short user comments of a few dozen characters to industry reports of tens of thousands of words. This requires adaptive adjustment capabilities for chunking parameters. Structured data with multiple fields requires vector indexes to support field-level filtering to improve the accuracy of research and retrieval.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers the text length range from short user comments to long industry reports in the brand agency operation scenario, balancing semantic integrity and retrieval density |
overlap_ratio | 10%–15% | Retains key context between chunks, preventing core fields such as SKU codes and interaction data from being truncated at chunk boundaries |
vector_model | bge-large-zh-1.5 or a compatible model with an embedding dimension of 1024, adapted to the deployment environment of FastGPT v4.8.7 and above | Adapts to professional terminology in Chinese e-commerce, social media, and beauty and personal care tracks, ensuring the accuracy of vector representations of research and investment texts |
index_type | hybrid hybrid index | Supports joint retrieval of structured fields (SKU, transaction amount) and unstructured text, matching the mixed characteristics of scenario data |
recall_top_k | Top 10–15 results | Balances retrieval relevance and result diversity, meeting the demand for multi-dimensional reference in research and investment scenarios |
index_batch_size | 50–100 items per batch | Balances index construction speed and server resource usage, adapting to the batch import requirements of multiple data sources |
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 specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Misconfigurations
- Symptom: A single file remains in the "indexing" state for an extended period, and the backend log displays the
vector_generation_timeouterror code. Cause: The file contains an extremely long text paragraph, and thechunk_sizeparameter is not adjusted to adapt, resulting in vector generation timeout. - Symptom: After uploading locally generated vector data, retrieval return results have extremely low relevance to expected outcomes. Cause: The vector model embedding dimensions used locally and on the server are inconsistent, preventing vectors from matching in a unified semantic space.
- Symptom: The interface only displays a single vector model option, and independent models cannot be configured for different data sources, failing to meet the requirement of mapping one set of data to multiple sets of vectors. Cause: The system configuration switch for multi-vector indexing is not enabled. Enable this function via the corresponding parameter, or unify the vector models for all data sources.
How to Confirm the Configuration Is Complete
- Upload a test file containing short user comments and long industry reports, and verify that the length of segmented text blocks matches the preset
chunk_sizerange. - Submit a retrieval query containing SKU codes and interaction volume, and verify that retrieval results can be sorted jointly by structured fields and text relevance.
- Submit a batch indexing task, observe that the indexing progress has no abnormal stalling, and the backend has no errors of the
vector_generation_timeouttype. - Switch test data from different data sources, and confirm that the embedding dimensions of vector generation results remain consistent.
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.