What this category's data looks like
Computer equipment investment research data primarily comes from official manufacturer specification documents, third-party performance test reports, industry compliance standard documents, and supply chain quotation data. Update frequency aligns with hardware iteration cycles. Core model parameters are updated quarterly. Test reports are released alongside new product launches. Document structures combine structured tables and long-form text. Fields include model, core hardware parameters, power consumption, interface specifications, test scores, and more. Common units are GHz, W, TB, bps, and others.
Constraints imposed by these characteristics on vector models and indexing
The mixed format of structured parameters and unstructured test text requires vector models to support both structured field encoding and long-text semantic extraction. This prevents loss of parameter precision from single-mode encoding. The high-frequency, small-batch update rhythm requires indexes to support incremental updates. This reduces computational overhead from full index reconstruction. The multi-field, multi-unit structure requires a preprocessing step to standardize units and align fields. This ensures consistency in vector encoding. Long test reports require a chunking strategy adapted to long-text splitting. This prevents loss of parameter information from semantic truncation.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the semantic unit length of computer equipment parameter documents and test reports, avoids truncating core hardware parameters and score data |
chunk_overlap | 50–80 characters | Maintains semantic coherence between adjacent chunks, covers model association and parameter comparison information that spans chunks |
index_type | HNSW | Balances retrieval efficiency and recall accuracy for high-frequency incremental updates, adapts to the quarterly update cycle of hardware parameters |
recall_top_k | 10–15 results | Covers multi-dimensional parameter comparison needs, prevents missing key configuration items from too few recalled results |
similarity_threshold | 0.75–0.85 | Filters low-relevance non-target model documents, retains valid recalled results for cross-model performance reference |
enable_incremental_index | Enabled | Adapts to the high-frequency, small-batch update characteristics of hardware parameters, reduces computational resource consumption from full index reconstruction |
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. Each scenario requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common implementation errors
- Symptom: The index shows a "not ready" status in the interface, and retrieval requests cannot be initiated. Cause: The incremental index switch is not enabled, or the full index construction timed out before completion, so the vector database has not finished importing vector data.
- Symptom: Retrieval results only return matching content from specified columns, and cannot cover complete hardware parameter association information. Cause: Vector encoding is only generated for single-column text, and multi-field fusion processing is not performed, resulting in loss of cross-field semantic associations.
- Symptom: An error that the model is not supported is returned when calling the
Doubao-embeddingmodel. Cause: The access key and service endpoint for the corresponding model are not added in the vector model configuration, or structured field encoding adaptation is not enabled.
How to confirm successful configuration
- View the vector model configuration page, confirm that a model adapted to multi-field encoding is selected, and that the access key and service endpoint are configured correctly.
- Submit a test retrieval, enter a query containing hardware model and parameter information, and verify the field completeness and relevance matching of the recalled results.
- Submit a new hardware parameter document, confirm that the index completes incremental updates automatically, and no error logs are generated.
- Check the index status panel, confirm that it shows a ready status, and that retrieval latency meets business requirements.
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.