What the Data for This Category Looks Like
Securities marketing content data is primarily sourced from investor education materials, product manuals, offline activity scripts, online promotional copy, and compliance reminder documents that have passed internal compliance reviews. Update cycles vary based on regulatory policy shifts, product launches and removals, and adjustments to marketing campaign schedules. There is no fixed update cadence. Core compliance content is updated less frequently, while marketing campaign-related content is updated more often. Document structures include title, main body content, compliance filing number, and applicable securities category fields. Field units include character count, date-formatted release time, and numeric product codes.
Constraints Imposed on Vector Models and Indexing
Structured fields such as compliance filing numbers and product codes must be separately mapped to metadata indexes to avoid mixing marketing content for different products in the same category during retrieval. Content with no fixed update cycle must support incremental indexing logic to avoid computing resource consumption from full index rebuilds. Long-form marketing content such as investor education report excerpts must use chunking strategies to ensure core compliance prompts are not truncated. Semantic differences between content types (standardized scripts vs. in-depth investor education) require vector models to adapt to Chinese financial terminology, while indexes must support filtering retrieval results by content type.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bce-embedding-zh or text-embedding-3-large | Adapts to semantic understanding of Chinese financial terminology, supports professional vocabulary encoding for securities marketing content |
chunk_size | 800–1200 characters | Securities marketing content includes compliance prompts and product information. Segments that are too long lose contextual connections, while segments that are too short damage semantic integrity |
chunk_overlap | 100–150 characters | Prevents core compliance information from being split across two chunks after segmentation, ensuring semantic coherence during retrieval |
retrieval_top_k | Top 3–5 results | Securities marketing content has high professionality. Too many retrieved results cause contextual redundancy, while too few fail to cover core information |
index_type | FAISS | Supports fast vector retrieval, adapting to the real-time retrieval needs of securities marketing content |
metadata_filter_enable | Enabled | Supports filtering retrieval results by metadata fields such as compliance filing numbers and product codes, preventing irrelevant content from being returned |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The interface prompts
This token does not have permission to use the model: text-embedding-3-large, or the log returns a 403 status code. Cause: No valid key for the corresponding model is configured on the platform, or the key has not been granted call permissions for the target vector model. - Symptom: After configuring the bce-embedding channel, the system still prompts that no available vector channel is available. Cause: The channel configuration is not linked to the vector model settings for the current application, or the channel service address is not in the platform whitelist.
- Symptom: Knowledge base search response times exceed business expectations, or performance lag occurs. Cause: Incremental indexing is not enabled, full index rebuilds consume significant computing resources, or chunking parameters are set too large, leading to excessive vector calculation volume per batch.
How to Confirm Successful Configuration
- Upload a test securities marketing content item, check the vector generation status, and confirm the vector model configuration loads normally.
- Initiate a knowledge base retrieval, verify that retrieval results can be filtered by metadata fields, and confirm the metadata index configuration takes effect.
- Check the index update log, confirm only newly added or modified content triggers index updates, and no full rebuild is initiated.
- Split a test text, review the returned chunk results, and confirm the chunk length matches the configured settings.
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.