What Data for This Category Looks Like
Power marketing content sources primarily include policy notices from internal marketing systems, online and offline event materials, standard script templates for account managers, and promotional copy related to user feedback. Update cycles are not fixed. Bulk updates occur when electricity price policies are adjusted or seasonal marketing campaigns launch. Daily script iteration frequency is lower. Document structures include fields such as title, publish time, applicable region, audience type, and content body. Applicable region uses administrative division codes or province and city names as units. Audience type is split into two categories: residential users and industrial and commercial users. Content body length ranges from short notices of a few dozen characters to thousands of-word policy interpretation documents.
Constraints Imposed on Vector Models and Indexing
The structured fields and update characteristics of power marketing content impose multiple constraints on the vector models and indexing link. First, structured metadata such as applicable region and audience type requires indexes to support metadata filtering. Recall operations must combine semantic similarity and metadata conditions for precise matching to prevent irrelevant content from being retrieved. Second, non-fixed update cycles and time-sensitive event content require indexes to support incremental updates, avoiding performance losses from full reconstruction. Third, the wide range of content lengths requires adapting segmentation strategies for short copy to long texts, ensuring semantic integrity of individual segments. Fourth, large volumes of duplicate content in standard script templates require optimizing vector storage logic to avoid duplicate vector generation occupying index resources.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | qwen3-embedding-8b or locally deployed m3e-large | Adapts to structured fields and long-text semantic understanding of power marketing content, supports local deployment to reduce deployment costs |
chunk_size | 800–1200 characters | Adapts to the length of power policy texts, avoids semantic fragmentation in single segments, and meets segmentation requirements for short copy |
chunk_overlap | 100–150 characters | Retains contextual association between adjacent segments, ensuring semantic coherence of fields such as applicable region and audience type |
filter_metadata_fields | ["applicable_region", "audience_type", "publish_time"] | Enables structured metadata filtering to precisely match the targeted customer acquisition needs of power marketing scenarios |
recall_top_k | Top 8–12 results | Balances recall accuracy and response speed, adapting to the precise outreach needs of power marketing scenarios |
index_refresh_interval | Every hour | Adapts to the irregular update rhythm of marketing content, ensuring content timeliness while reducing index load |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: After configuring
qwen3-embedding-8b, adding another model with the same name overwrites the original configuration. Cause: In FastGPT v4.9.11 and later versions, model configurations use names as unique identifiers, and no multi-instance same-name configuration option is reserved. - Issue: Locally deployed M3E models via Docker cannot complete indexing tasks. Cause: The API address and port of the local model were not correctly filled in the FastGPT configuration, or cross-domain access permissions for the model service were not enabled.
- Issue: Knowledge base recall results for the same text differ across deployment environments. Cause: Minor differences exist between the public cloud and local versions in vector model versions and segmentation parameter configurations, or the public cloud version has enabled reranking functionality that was not configured.
How to Verify Correct Configuration
- Access the vector model configuration page of the FastGPT knowledge base, and verify that the
embedding_modelfield matches the preset configuration. - Upload a test power marketing text, and check whether the segmentation results align with the
chunk_sizeandchunk_overlapsettings. - Run a recall test to verify that content matching the specified conditions can be filtered based on metadata such as
applicable_region. - View the index update log to confirm whether the incremental update task runs periodically according to the
index_refresh_intervalsetting.
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.