Characteristics of this category’s data
Optical module investment and research data comes from public communications industry standard documents, official manufacturer specification sheets, third-party test reports, and carrier procurement announcements. Data update timelines adjust alongside new product launches and industry standard iterations, with no fixed schedule. Each structured data entry includes fields such as model, transmission rate, operating wavelength, power consumption, physical dimensions, certification status, and release date. Rate uses Gbps as its unit, wavelength uses nm, power consumption uses W, and dimensions use mm. Unstructured documents vary in length, from a few pages of parameter summaries to dozens of pages of detailed test results.
Constraints on database and operations workflows
The multi-source nature of optical module data requires cross-source deduplication and field association before data ingestion, to avoid duplicate redundant data for the same model. Fixed field units demand strict matching during data verification, to prevent investment and research errors caused by mixed units. Wide variation in unstructured document length requires tailored vector segmentation strategies to maintain consistent semantic recall performance. No fixed update cycle requires the database to support elastic scaling connection pool configurations, to handle sudden spikes in data imports and query requests during new product launches.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
db_connection_pool_size | 10-20 | Adapts to medium concurrent query and batch import needs for optical module investment and research scenarios, and avoids connection exhaustion |
vector_store_segment_length | 800-1200 characters | Balances semantic recall performance for short optical module parameter entries and long test reports, and reduces segment breaks |
mcp_request_timeout | 30-60 seconds | Addresses request delays during high concurrent calls, and prevents empty result returns caused by timeouts |
db_batch_insert_size | 50-100 entries | Optimizes import efficiency for batch ingestion of manufacturer specification lists, and reduces database overhead from single-entry inserts |
parse_file_max_size | 1000 MB | Supports importing large-capacity optical module test report documents, and covers all business scenarios |
rag_recall_top_k | Top 5-8 entries | Focuses on precise parameter and document information, and avoids excessive recalled content interfering with investment and research judgments |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: The MCP node returns empty results when the workflow is called 2-3 times per second. Cause: The
mcp_request_timeoutconfiguration was not adjusted, and the timeout threshold was set too low, causing requests to be terminated before completion under high concurrency. - Symptom: After calling the database node, the original JSON string is returned, and field values cannot be extracted directly. Cause: The automatic field parsing configuration of the database node was not enabled, resulting in unstructured query results.
- Symptom: After importing optical module specification documents, some parameter field units are incorrect. Cause: No pre-ingestion unit verification rules were configured, leading to uncorrected unit formats across different data sources.
How to confirm configurations are correctly set
- Execute a single parameter query through the database node, and verify that the returned result fields match the original optical module data.
- Trigger a batch import of optical module specification data, and confirm that all data can be imported normally without format error alerts.
- Simulate high-concurrency workflow calls, and check that all requests return valid results without exceptions.
- View the vector database segmentation logs, and confirm that documents are correctly segmented within the configured length range.
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.