Data Profile for This Category
Data sources for semiconductor investment research cover wafer fab process specification documents, EDA design files, industry association statistical reports, brokerage sector research reports, global patent databases, and upstream raw material spot price platforms. Update rhythms vary significantly: raw material price data updates daily, patents and research reports update weekly or daily, while wafer fab process documents and EDA design files update on a quarterly or annual basis.
Document structures include structured fields such as yield, process node, and revenue, with units including nm, %, 100 million yuan, USD/kg. They also contain semi-structured research report summaries, as well as unstructured full patent texts and process description documents.
Constraints on Database and Operations Work
Multi-source heterogeneous data structures require databases to support relational structured storage, vector indexing, and unstructured text storage simultaneously. This avoids retrieval gaps caused by data splitting.
Differentiated update rhythms require layered synchronization strategies. Enable real-time incremental synchronization for high-frequency raw material data, and scheduled full synchronization for low-frequency process documents. This reduces operational overhead.
Multi-unit fields need preset unified mapping rules. This prevents invalid results caused by unit mismatches during retrieval.
Highly specialized text content requires vector models to adapt to semiconductor industry terminology. Operations teams must also ensure stable index updates, to avoid retrieval delays as data volume grows.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_SHARD_KEY | {"source_type": 1, "update_time": -1} | Shard by data source and update time, to support mixed storage of multi-source data and differentiated update rhythms, and improve query efficiency |
MILVUS_COLLECTION_DIM | 1536 | Match the output dimension of common open-source embedding models, to cover semantic vector representation needs for semiconductor industry terminology |
UPLOAD_FILE_MAX_SIZE | 2048 MB | Semiconductor EDA design files and wafer fab process documents have large individual file sizes; raising the upload limit avoids file truncation |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long documents such as full patent texts and large EDA files take longer to parse; extending the timeout avoids parsing failures |
VECTOR_RECALL_TOP_K | 20–30 | Semiconductor investment research requires coverage of multi-dimensional data; this range balances recall accuracy and retrieval latency |
MONGODB_CONNECTION_POOL_SIZE | 50 | Multi-source data synchronization tasks run in parallel; sufficient connection pool size avoids connection exhaustion during concurrent read/write operations |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: A MongoDB connection timeout error appears after starting the container, with logs showing
connection timed out. Cause: The MongoDB connection pool parameters were not adjusted according to the semiconductor data sharding configuration, and concurrent synchronization tasks exhausted connection resources. - Symptom: Vector recall results lack precise matching content related to semiconductor process nodes. Cause: The
MILVUS_COLLECTION_DIMparameter was not set correctly, leading to mismatched embedding vector dimensions, which prevents correct recall of content related to professional terminology. - Symptom: Deployment from local source code fails, with prompts about port occupation or sandbox container not starting. Cause: Port mappings for MongoDB, Milvus, and sandbox containers were not configured separately, and the sandbox container was not started as needed to handle parsing tasks for large semiconductor files.
How to Verify Proper Configuration
- Run the built-in database connection test tool to verify the connection status of MongoDB and Milvus, and confirm that the connection pool parameters meet the concurrency requirements of current synchronization tasks.
- Upload a typical semiconductor process document, check that the parsed fields are complete and unit mappings match the preset rules.
- Enter semiconductor professional terminology to conduct a vector recall test, and verify that the relevance and quantity of recall results fall within the configured
VECTOR_RECALL_TOP_Krange. - Check container logs to confirm that incremental synchronization tasks run according to the set update rhythm, with no abnormal full synchronization triggers.
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.