What the data for this category looks like
Data sources for consumer electronics marketing content primarily include official product manuals, parameter specification sheets, marketing copy, user real-world feedback, new product launch announcements, and accessory compatibility documents. Update rhythm adjusts based on new product release cycles. Regular quarterly updates are performed, and full product line documents are updated synchronously during major new product launches. Each individual document includes fields for product SKU, core parameters such as screen size, battery capacity, processor model, marketing language, after-sales policies, and compatible devices. Units mostly use standardized physical and commercial units like millimeters, milliampere-hours, hours, and yuan.
How these characteristics create constraints for retrieval and recall
Mixed multi-source document structures require the retrieval step to differentiate recall weights for parameter-based and marketing content, to avoid interference from non-target information. High-frequency updates require index construction to support incremental updates, reducing resource consumption from full reindexing. Multi-field parameter differences require filtering by product SKU during retrieval, to prevent returning irrelevant content across different models. Mixed long-form content requires a proper chunking strategy to ensure semantic unit integrity, avoiding splitting parameters and marketing language into separate chunks which causes retrieval failures.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Consumer electronics documents contain both structured parameters and unstructured marketing content. This range balances semantic integrity and chunk granularity, preventing mixing of cross-type content |
RECALL_TOP_K | Top 8–12 results | Consumer electronics product lines have many models. Retrieving a sufficient number of candidate results first before filtering by SKU avoids missing target content due to insufficient initial recall |
SIMILARITY_THRESHOLD | 0.75–0.85 | Semantic similarity of marketing content can be disrupted by copy style. This threshold is higher than general scenarios, filtering low-relevance non-target documents |
UPLOAD_INCREMENTAL_ENABLE | Enabled | Consumer electronics products are updated frequently. Incremental updates significantly reduce index reindexing time, adapting to high-frequency iteration needs |
VECTOR_STORE_TYPE | HNSW | Consumer electronics knowledge bases typically have large data volumes. HNSW vector index retrieval efficiency outperforms other types, meeting real-time recall requirements |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large product manuals include multiple pages of parameters and copy. This timeout covers standard parsing durations, preventing mid-process interruptions |
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. It is recommended to test on your own samples before finalizing values.
Three common mistakes
- Significant differences exist between return results from online chat and API calls. Online chat enables context association and reranking by default. API calls require manual configuration of the
rerank_top_kandcontext_windowparameters to align results. - Chinese garbled characters appear after importing a CSV knowledge base in the latest version. The cause is failing to specify the CSV file encoding as UTF-8. Some editors save files in GBK encoding by default, leading to character mapping errors during parsing.
- After updating from version 4.9 to 4.10, the original indexed knowledge base cannot return search results. The cause is that this version range changed the underlying storage structure of the vector index. The original index format is no longer compatible, so index construction must be re-run.
How to confirm configurations are set properly
- Upload a test document containing product parameters and marketing copy, verify that parsed chunks are split by semantic units, with no mixing of cross-category parameters and marketing content.
- Call the API interface with a test query, compare results with online chat, confirm that the
recall_top_kandsimilarity_thresholdconfiguration parameters are active. - Upload a UTF-8 encoded CSV test file, check that imported Chinese content has no garbled characters, and field mapping matches the original document.
- Check the vector database monitoring dashboard, confirm that index construction progress is normal, with no timeout or error logs.
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.