Data Profile for This Category
General equipment investment research data comes primarily from public industry association reports, official manufacturer product manuals, patent databases, supply chain ledgers, and real-time equipment operating parameters. Update cycles vary widely: industry reports are updated quarterly or semi-annually, product manuals are updated when new models launch, and patents and real-time operating parameters are updated in real time. Document structures include structured parameter tables, semi-structured technical white papers, unstructured fault cases, and industry news. Fields include equipment model, rated power (unit: kW), maximum rotational speed (unit: rpm), operating temperature range (unit: ℃), supplier information, release date, and more.
Constraints on Database and Operations
The characteristics of general equipment investment research data create multiple constraints for database and operations workflows. Structured parameters require strongly typed fields to ensure accurate retrieval. Real-time operating parameters need support for high-concurrency writes and low-latency queries. Long documents and short parameters coexist, requiring a hybrid indexing strategy. Differences in update frequencies across data sources require a staged incremental update mechanism. Multi-unit fields need unified labeling or conversion to avoid retrieval discrepancies caused by unit mismatches. Additionally, the large number of general equipment models and associated data volumes require the database to support horizontal scaling to accommodate future data growth.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MILVUS_COLLECTION_DIM | 1536 | Matches the output dimension of mainstream general technical text embedding models, covering vector conversion needs for equipment parameters, white papers, and other data types |
UPLOAD_FILE_MAX_SIZE | 1000 MB | General equipment technical white papers and patent documents have large individual file sizes, requiring adaptation for long-text upload scenarios |
VECTOR_SEARCH_TOP_K | 10-20 | General equipment investment research requires precise matching of specific model parameters; excessive recall increases context processing load |
DB_POOL_MAX_SIZE | 64 | Supports high-concurrency access for structured parameter queries and real-time operating data |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Text parsing and vectorization processing for large equipment manuals take extended time; this setting prevents task interruptions mid-execution |
REDIS_CACHE_TTL | 86400 seconds | Most general equipment industry report update cycles are quarterly; short-term caching meets high-frequency retrieval needs |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- The symptom is missing parameters for specific equipment models in knowledge base retrieval results, or recalled results that do not match expected fields. The cause is failure to create an independent index mapping for structured parameter fields, resulting in vector retrieval only matching text content and being unable to accurately locate structured parameters.
- The symptom is database data loss after a Docker container restarts, or failure to connect to the service normally. The cause is failure to configure persistent data volumes for services such as Mongo and Milvus in docker-compose.yml, resulting in data inside the container being lost when the container is destroyed.
- The symptom is embedding model call timeouts or abnormal memory usage. The cause is failure to allocate container memory based on the resource requirements of the vector database. Even if the host has 192GB DDR5 memory, failure to allocate resources specifically will result in task failures caused by resource contention.
How to Verify Proper Configuration
- View the collection configuration information of the vector database, and verify that the
MILVUS_COLLECTION_DIMparameter matches the output dimension of the embedding model in use. - Upload a standard general equipment technical manual, and check whether the parsing task execution time is within the range set by
PARSE_FILE_TIMEOUT_SECONDS. - Initiate a retrieval for a specific equipment model, and verify that the fields included in the recalled results match the preset structured parameters.
- View the container resource monitoring panel, and confirm that the memory usage of the database container does not exceed the pre-allocated threshold.
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.