What the Data for This Category Looks Like
Optical module investment research data is primarily sourced from manufacturer public specifications, third-party industry test reports, supply chain quotation documents, patent texts, and regulatory policy documents. Update schedules are adjusted irregularly alongside new product launches, quarterly financial report disclosures, and industry standard updates. Document structures fall into three categories: structured parameter tables, unstructured technical descriptions, and long-text test reports. Core fields include optical module model, central wavelength (unit: nm), transmission rate (unit: Gbps), operating temperature (unit: ℃), and power consumption (unit: W). Some documents include test waveform diagrams and supply chain-related data.
Constraints Imposed on Vector Models and Indexing
The mixed structure and update characteristics of optical module data impose four constraints on the vector models and indexing workflow:
- Coexistence of structured parameters and non-text content requires support for field-level vectorization and multi-modal vector fusion.
- Irregular update frequency and incremental update requirements demand adaptation to incremental indexing mechanisms.
- Significant variation in document length, ranging from tens-of-character parameter entries to thousands-of-character test reports, requires an adaptive segmentation strategy.
- Fields carry clear physical units, which require normalization prior to vectorization to avoid vector deviation caused by unit differences.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
embedding_batch_size | 16–32 | The vectorized text length of single optical module entries is concentrated between 500–1000 characters. This range balances embedding speed and model load, and avoids triggering rate limits |
chunk_size | 800–1200 characters | Balances semantic integrity for short optical module parameter entries and long test reports. Avoids overly short splits that break parameter associations, and overly long segments that introduce redundant context |
vector_index_type | HNSW | Meets fast recall requirements for high-dimensional vectors from optical module knowledge bases (most embedding models output 1536-dimensional vectors). Has lower latency compared to Flat indexes |
recall_top_k | 20–30 | Investment research scenarios require comparison of multiple sets of optical module parameters. This value range covers core related results while reducing subsequent sorting calculation load |
embedding_rate_limit | 1000 tokens/minute | Matches the default call rate limit of most open-source embedding models, and adapts to the batch vectorization rhythm of optical module data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time required for large optical module test report PDFs, and avoids mid-process interruption of long document parsing |
The parameter values provided on this page are common starting points for configuration setup. Actual values will be affected by material format, data volume, and business rules. Each scenario requires targeted analysis. It is recommended to test on your own samples before finalizing configurations.
Three Common Mistakes
- Phenomenon: 429 Too Many Requests error triggered during vectorization, with logs showing rate limit exceeded. Cause:
embedding_rate_limitis not configured, or the set value exceeds the upper limit allowed by the embedding model. No current limiting is implemented for batch processing of optical module data. - Phenomenon: Knowledge base retrieval latency is too high, with interface loading timed out. Cause: Flat index type is selected, and
vector_index_typeis not set to HNSW. Retrieval efficiency of Flat indexes drops rapidly as data volume grows under high-dimensional vector scenarios. - Phenomenon: Dataset indexing status remains "indexing" for a long time and cannot switch to ready. Cause: Duplicate data verification is not enabled. When optical module supply chain quotation documents are updated daily, duplicate entries are not filtered via hash values, leading to repeated indexing of duplicate data.
How to Verify Proper Configuration
- Execute a batch vectorization test, check console logs for rate limit exceeded errors, and confirm that the
embedding_rate_limitvalue complies with the embedding model's official rate limits. - Submit a test query containing core optical module parameters, check the return latency of retrieval results, and confirm that latency meets expectations when
vector_index_typeis set to HNSW. - Manually add a test data entry, wait for indexing to complete, check whether the indexing status of this data in the dataset list switches to ready, and confirm that the incremental indexing trigger logic works correctly.
- Compare the number of original datasets and indexed documents, confirm that no duplicate data is indexed multiple times, and check whether field-level verification configuration is enabled.
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.