What the data for this category looks like
Marketing content data for computer equipment primarily comes from official product parameter pages, e-commerce platform detail pages, technical specification whitepapers, and offline promotional materials. Update frequency varies based on new product launches, parameter adjustments, or promotional activities, with no fixed cycle. Each document typically includes a title, core performance parameters, applicable scenario descriptions, pricing information, and after-sales notes. Fields include standardized parameters such as product model, CPU clock speed (unit: GHz), memory capacity (unit: GB), selling price (unit: yuan), alongside unstructured sales copy.
Constraints for Vector Models and Indexing
The mixed data structure (structured parameters + unstructured copy) requires vector models to adapt to both standardized numerical fields and natural language descriptions. This balances parameter accuracy and semantic understanding. No fixed update cycle requires the indexing system to support incremental synchronization, avoiding resource consumption from full reindexing. Fields with clear built-in units require the vectorization step to retain unit information, preventing confusion between similar-sounding parameters. Additionally, differences in copy across channels for the same product model require light deduplication before indexing, reducing duplicate vector storage usage.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Local open-source vector model or compliant commercial vector model, such as bge-large-zh-v1.5 | Adapts to semantic and parameter understanding needs for mixed data, balancing local deployment and call costs |
chunk_size | 800–1200 characters | Balances the length of structured parameters and semantic integrity of unstructured copy, avoiding split breaks |
recall_top_k | 10–15 results | Matches the precise matching requirements of marketing content, reducing redundant recall results |
similarity_threshold | 0.72–0.85 | Filters low-similarity irrelevant content, retaining marketing materials highly matched to target queries |
index_batch_size | 50–100 entries per batch | Adapts to per-batch data volume, avoiding memory overflow during indexing |
enable_structured_index | Enabled | Creates a dedicated index for standardized parameter fields, improving recall efficiency for parameter-based queries |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, 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 Configuration Mistakes
- A
503 Service Unavailableerror appears, with the prompt that no available text-embedding model nodes exist under thedefaultgroup. This occurs when insufficient vector model call resources are configured, or model instances in the group experience abnormal restarts. - Knowledge base indexing tasks remain in a running state with no progress updates. This happens when the
index_batch_sizeparameter is not set appropriately during Docker deployment, with excessive per-batch processing data leading to memory exhaustion and task hanging. - A large number of irrelevant computer equipment parameters appear in retrieval results, or marketing content for specified models cannot be matched. This occurs when no dedicated weight is configured for structured parameter fields, or unit information for parameter fields is not retained, leading to ambiguity during vectorization.
How to Verify Proper Configuration
- Review vector model call logs to confirm each call’s returned results include correct parameter and copy vectors, with no null values or abnormal dimensions.
- Run an indexing test with a small batch of data to confirm the task completes within the preset time, with no continuous running or errors.
- Submit a test request including parameter queries to confirm the number and matching degree of recall results meet the preset
similarity_thresholdandrecall_top_krequirements. - Check the vector dimensions stored in the index to confirm they match the output dimensions of the currently used
embedding_model, with no dimension mismatch issues.
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.