What this category of data looks like
General equipment investment research data comes primarily from monthly operation briefs published by industry associations, quarterly financial reports of listed companies, technical parameter manuals from equipment suppliers, bidding project documents, and real-time operating condition logs from equipment terminals. Update frequencies vary across sources: industry reports and financial reports are updated quarterly or annually, technical parameter manuals receive static updates, and operating condition logs are added in real time. Document structure falls into three categories: structured parameter tables (containing equipment model, manufacturer, rated parameters, and similar fields), unstructured long technical analysis texts, and bidding requirement documents. Most fields have clear units: rated power uses kilowatts, operating efficiency uses percentages, and service life uses years, for example.
What constraints these characteristics impose on vector models and indexing
The mixed structure and multi-unit field features of general equipment investment research data require vector models to support multi-field weighted embedding. This prevents similarity calculation deviations caused by unit confusion. The high-frequency addition of real-time operating condition logs requires indexes to support incremental updates. This reduces resource usage from full index rebuilding. Structured parameter tables require exact matching, so indexes must support both vector recall and exact queries. This adapts to mixed data retrieval needs. Long technical analysis texts require proper segmentation. This avoids breaking contextual links between parameters and descriptions, while ensuring embedded vectors fully carry technical details.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
EMBEDDING_MODEL | External vector model that supports multi-field weighted embedding | General equipment investment research data includes structured parameters and unstructured analysis content; multi-field weighted embedding improves parameter matching accuracy |
CHUNK_SIZE | 800–1200 characters | General equipment technical documents often contain continuous parameter descriptions and technical details; this segment length balances contextual integrity and embedding efficiency |
INDEX_TYPE | PGVector composite index | Balances exact matching needs for structured parameter tables and vector recall needs for unstructured text, adapting to mixed data structures |
RECALL_TOP_K | Top 10 entries | Investment research scenarios require a balance between recall comprehensiveness and processing efficiency; excessive recall increases subsequent analysis overhead |
SIMILARITY_THRESHOLD | 0.72–0.80 | Exact matching of equipment parameters requires a high similarity threshold, to avoid accidental recall of unrelated equipment models |
INCREMENTAL_UPDATE_INTERVAL | 3600 seconds | Balances real-time performance of operating condition data and resource usage of index updates, adapting to the quarterly update cycle of industry reports |
The parameter values provided on this page are common starting points for configuration setup. 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.
Three Common Configuration Mistakes
- Symptom: Index creation fails when deploying a PostgreSQL database via Docker, with logs returning
index creation timed out. Cause: Structured parameter tables and unstructured text are not indexed separately, and the single index data volume exceeds the default timeout threshold. - Symptom: After importing Feishu Excel and multi-dimensional documents, recall results have no matching content, and parsed fields are empty. Cause:
EXCEL_PARSE_SCHEMAis not configured to match the preset field names of equipment parameters, resulting in failed vector generation from parsed text. - Symptom: Frequent
504 Gateway Timeouterrors occur when deploying an external vector API on an ARM soft router. Cause: Insufficient network bandwidth of the soft router cannot support high-frequency calls to the vector API, and the timeout setting forAPI_REQUEST_TIMEOUTis not extended.
How to Confirm Proper Configuration
- Upload a single equipment parameter Excel file, check if parsed fields include preset fields such as
设备型号,额定功率, to confirm the parsing configuration is effective. - Enter query text containing clear parameters, such as "ventilation equipment with a rated speed of 1500 rpm", check if the similarity scores of recall results fall within the preset threshold interval, to confirm the similarity threshold configuration is correct.
- Submit new operating condition log data, check that the index update log only contains new data and has no full rebuilding records, to confirm the incremental update rule is effective.
- Check the external vector API call logs, confirm there are no
429 Too Many Requestserrors, to confirm hardware resources and API call thresholds are matched.
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.