Data Characteristics of This Category
Power grid equipment investment research data primarily comes from public bidding announcements, equipment operation and maintenance logs, industry technical standards, supplier technical documents, and investment research reports. Bidding announcements are updated monthly or by project batch, and include fields such as project number, equipment model, voltage level, budget amount, and more. Operation and maintenance logs are updated in real time or daily, and include fields such as equipment ID, operating duration, fault records, maintenance time, and more. Technical standard documents are updated irregularly, and include fields such as standard number, applicable scenarios, performance indicators, and more. Most field units follow general power industry standards: voltage level uses kilovolts (kV), equipment capacity uses megawatts (MW), and operating duration uses hours (h).
Constraints for Vector Models and Indexing Workflows
Multi-source heterogeneous data formats and field differences require vector models to have embedding capabilities adapted to power industry professional terminology, to avoid semantic deviation from general models for terms such as "110kV transformer" and "disconnector". Wide variation in document length, from tens of characters in operation and maintenance logs to thousands of characters in bidding announcements, requires flexible adjustment of segment configuration to prevent professional parameters from being split incorrectly. Different data sources have varying update rhythms: real-time operation and maintenance logs require indexes to support incremental updates, while regularly updated standard documents require full index scheduling to avoid inefficient full reindexing. Precise matching requirements for professional fields require the recall step to allow configuring weights for key fields such as voltage and capacity, to improve the recall accuracy of professional content.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | text-embedding-v3 or a local open-source embedding model | Ensures embedding accuracy adapted to power grid equipment professional terminology, avoids semantic deviation from general-purpose models |
chunk_size | 800–1200 characters | Balances parameter integrity for long documents and semantic coherence for short logs, prevents splitting of professional fields |
index_batch_size | 50–100 items per batch | Adapts to the memory limit of an 8-core 16-gigabyte virtual machine, prevents container resource exhaustion from oversized single-batch index data |
recall_top_k | Top 8–12 results | Matches the multi-dimensional information needs of investment research scenarios, avoids excessive redundant recall data or too few results that miss critical parameters |
similarity_threshold | 0.72–0.85 | Filters low-similarity irrelevant documents, retains professionally matched results |
INDEX_UPDATE_MODE | Incremental update + scheduled full synchronization | Adapts to update rhythms of different data sources: use incremental updates for operation and maintenance logs, use scheduled full synchronization for standard documents |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A 503 error occurs after startup, and the log prompts "No available channel for model text-embedding-v3 under default group". Cause: The index model was not configured separately, and the channel configuration of the conversation model was reused, resulting in the embedding model not being bound to an independent group.
- Symptom: Index creation fails during PostgreSQL database deployment, and an out-of-memory error appears in container logs. Cause: The
index_batch_sizeparameter was not adjusted, and the single-batch index data volume exceeded the available memory limit of an 8-core 16-gigabyte virtual machine. - Symptom: Environment variables modified in docker-compose.yml do not take effect. Cause: The container rebuild command was not executed, and the old container still loads the initial environment variables.
How to Verify Successful Configuration
- Upload a power grid equipment bidding announcement document, check the parsed segment results, and confirm that professional fields such as voltage level and equipment model are not split.
- Initiate a knowledge base recall test, check the similarity scores of returned results, and confirm that the scores fall within the preset threshold range.
- Add a new operation and maintenance log document, wait for the index update to complete, retrieve the equipment ID of this document, and confirm that the corresponding data can be recalled.
- Check container resource monitoring, confirm that CPU and memory usage during index construction do not exceed the available limits of the host machine.
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.