Data Characteristics of This Category
Investment research data for the plastics and rubber industry covers upstream raw material quotes, futures market trends, industry association supply and demand reports, downstream product manufacturing plant operating data, and customs import and export statistics. Data update frequencies vary: spot prices and futures market prices update daily. Weekly industry dynamic reports are published weekly. Annual supply and demand reports update monthly or quarterly. Document structures include structured tables, semi-structured research report paragraphs, and plain text analysis content. Most fields include standard units such as yuan/ton, ten thousand tons, and percentage.
Constraints Imposed on Vector Models and Indexing
A high proportion of structured fields with standard units requires field standardization before vectorization. This prevents unit differences or field confusion from polluting vector semantics. Large differences in update frequencies across data sources require flexible incremental indexing synchronization strategies. This avoids excessive resource usage from full indexing operations. Mixed storage of long-text research reports and short-field market data requires reasonable segmentation length settings. These settings must retain contextual connections for professional terms, while avoiding over-splitting of short fields. Datasets with over 100,000 entries require properly configured index sharding to balance storage and retrieval performance. Systems must also support structured field filtering indexes to meet precise retrieval needs in investment research scenarios.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the paragraph length of professional plastics and rubber research reports, retains contextual semantics while avoiding excessive vector redundancy from overly long single data blocks |
overlap_ratio | 10%–15% | Balances contextual continuity of long-text segmentation and vector storage efficiency, adapts to datasets mixing structured fields and long text |
recall_top_k | Top 10–15 results | Balances recall coverage and retrieval efficiency for investment research scenarios, avoids excessive irrelevant results interfering with analysis logic |
similarity_threshold | 0.72–0.85 | Adapts semantic similarity judgment for industry-specific terms, avoids introducing irrelevant data with too low a threshold, or missing information related to specialized product grades with too high a threshold |
index_shard_num | Calibrated via actual testing; set to 2–4 for 100,000+ entries | Distributed index sharding balances storage usage and retrieval latency, adapts to datasets of different sizes |
incremental_sync_interval | 1 hour (market data), 24 hours (research report data) | Matches update frequencies of different data sources, reduces resource consumption and time costs of full indexing |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Phenomenon: After uploading 100,000 CSV-format investment research data entries, only slightly more than 90,000 records appear after vectorization. Cause: Some data rows contain unescaped line breaks or half-width commas, causing the parsing process to interrupt and preventing correct vectorization and storage.
- Phenomenon: Collection creation shows success, but the index construction status remains marked as incomplete on the page. Cause: Failure to configure
index_shard_numcauses the single shard data volume to exceed the threshold, or failure to set a retry mechanism for model API calls, triggering a timeout without automatic recovery. - Phenomenon: Vector retrieval functions normally locally, but after packaging into a Docker image and running, vector scores remain consistent and retrieval latency increases significantly. Cause: The container environment is not configured with sufficient temporary storage to cache vector intermediate results, or the
MAX_BATCH_SIZEparameter is restricted to an overly small default value, reducing batch processing efficiency.
How to Verify Proper Configuration
- Upload 100 test datasets with clear fields, verify that the number of vectorized data entries matches the source data exactly, confirming no missing entries in the parsing process.
- Run an incremental synchronization task, check the synchronization trigger time interval in system logs, confirm it matches the configured
incremental_sync_interval. - Initiate a mixed retrieval request, compare the latency of vector retrieval and structured filtered retrieval, confirm that the
index_shard_numconfiguration does not cause retrieval latency to exceed a reasonable range. - Adjust the
similarity_thresholdvalue, verify changes in retrieval result relevance, confirm the threshold adapts to the semantic characteristics of industry terms.
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.