Data Characteristics of This Category
Data for optoelectronics intelligent due diligence reports comes primarily from public industry association statistics, quarterly financial reports of listed companies, supply chain quotation databases, patent retrieval platforms, and production line inspection logs. Update frequencies vary by data type: financial reports are updated quarterly, industry supply and demand data monthly, and patent and production line inspection data are synchronized in real time. Document structures mix structured tables and long-form text, including device specification parameters, production capacity data, supply chain link information, patent claims, and more. Most fields have clear physical units such as nm, mA, %, and units per hour.
Constraints for Vector Models and Indexing
The mixed document structure and unit-attached numerical fields of optoelectronics due diligence reports require vector models to support both unstructured text encoding and structured numerical feature mapping, to avoid interference from numerical units on vector similarity calculations. Long text passages such as patent claims and production line logs account for a large share of content, so longer segment cutting boundaries must be supported to avoid truncation of key technical parameters. Data sources with multiple update frequencies require a strategy combining incremental indexing and full indexing: enable high-frequency incremental synchronization for real-time production line data, enable regular full refreshes for quarterly financial reports, and ensure data with different update frequencies can be accurately recalled in the index.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Optoelectronics documents often contain long technical passages and parameter blocks with units. 800-1200 characters can fully cover a single set of device specifications or patent sections, avoiding truncation of critical information |
embedding_thread_count | 2–4 threads | Optoelectronics data sources are mostly a mix of batch structured tables and long text. Too many threads may trigger embedding rate limits, while too few threads will extend indexing time |
recall_top_k | 10–15 results | Due diligence reports need to cover multi-dimensional information such as supply chain, production capacity, and patents. 10-15 results balances recall completeness and retrieval efficiency |
similarity_threshold | 0.72–0.85 | Numerical parameters with units are prone to high similarity matches. The threshold must be higher than general scenarios to filter irrelevant unit matching results |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Optoelectronics due diligence reports often include batch production line inspection data and full patent texts, so the upload limit needs to be increased |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing batch structured tables takes a long time, to avoid timeout for large file parsing |
Three Common Misconfigurations
- The knowledge base upload status remains stuck in indexing for extended periods: This occurs when
embedding_thread_countis set too high, and optoelectronics due diligence files containing multiple sets of structured tables are uploaded in batch, leading to backlog in the embedding queue. - Text block loss occurs when segment length is set to 3000 characters: This occurs because the mixed structure of long technical passages and parameter blocks in optoelectronics documents is not accounted for. Excessively long segments trigger the system's automatic truncation threshold, causing key parameter blocks to be skipped.
- Slow retrieval speed and poor result relevance: This occurs when
similarity_thresholdandrecall_top_kare not adjusted, incremental indexing is not enabled, the full indexing scan range is too large, and irrelevant unit matching results are not filtered.
How to Confirm Proper Configuration
- Upload a single typical optoelectronics due diligence file, check the number of parsed text blocks, confirm there is no obvious truncation or loss, and match the preset segment configuration.
- Check the embedding running logs, confirm the actual thread count does not exceed the configured
embedding_thread_count, and there are no rate limit error messages. - Initiate a retrieval test, input core parameter keywords, verify the number and relevance of recall results, and adjust relevant configurations to meet business requirements.
- Compare indexing update times for different data sources, confirm that real-time data and scheduled data use the correct index refresh strategy.
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
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.