What the data for this category looks like
Data sources for grid equipment due diligence reports include factory inspection reports, operation logs, inspection records, bidding compliance documents, and more. Update rhythms vary significantly: factory inspection reports are one-time static data, operation logs are updated quarterly, and inspection records are updated daily or in real time. Single documents contain fields such as equipment model, rated voltage, rated capacity, test items, and fault level. Each field has clear attached units, such as kV, MVA, hours. Some documents include structured tables and long paragraph parameter descriptions.
Constraints on vector models and indexing
Multi-source heterogeneous data formats require vector models to support parsing of PDF, Excel, Word, and other document types. This prevents information loss from structured tables and long paragraph parameter descriptions. Data sources with differing update frequencies require indexes to support incremental writes. This avoids triggering full index rebuilds for high-frequency updated inspection data. The wide range of document lengths requires flexible chunking strategies. These strategies adapt to text volumes from a few pages of factory reports to dozens of pages of operation logs. Fields with clear units require vector encoding to retain contextual associations. This prevents semantic confusion caused by missing units, which reduces retrieval matching accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Adapts to long paragraph test data and operation records in grid equipment documents, avoids unnecessary splitting of single-piece information |
CHUNK_OVERLAP | 100–150 characters | Retains contextual connections between chunks, matches the continuous description logic of equipment parameters |
VECTOR_DB_TYPE | pgvector | Supports structured field association, adapts to retrieval requirements for fields such as equipment model and rated parameters |
RECALL_TOP_K | Top 8–12 results | Covers multi-dimensional equipment data required for a single due diligence report, avoids missing critical parameters due to insufficient recall count |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance non-grid equipment documents, retains highly matched equipment inspection and operation data |
INCREMENTAL_INDEX_ENABLE | Enabled | Adapts to operation and inspection data with different update frequencies, avoids excessive time spent on full index rebuilds |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: When importing structured table documents for grid equipment, automatically extracted text blocks lose unit information such as kV or MVA. This causes semantic confusion in vector retrieval. Cause: Structured table parsing configuration is not enabled. Only plain text is chunked, and contextual associations between fields and units are not retained.
- Phenomenon: Vector retrieval returns results that do not meet expectations. Either a large number of irrelevant non-target equipment documents are included, or core operation parameters are missed. Cause: Reasonable
SIMILARITY_THRESHOLDandRECALL_TOP_Kparameters are not set based on the field characteristics of grid equipment documents, or structured field association retrieval is not enabled. - Phenomenon: Index update lag occurs when incrementally updating operation and inspection data. Full index processing time exceeds expected limits. Cause: Incremental index function is not enabled. Full index mode is still used to process high-frequency updated inspection data, leading to repeated indexing of existing documents.
How to verify proper configuration
A single grid equipment factory inspection report is uploaded. Parsed text chunks are reviewed to confirm long paragraph test data is not overly split, and unit information is fully retained in corresponding text blocks. The keyword "10kV dry-type transformer DC resistance test" is used for retrieval. Returned results are checked for equipment model and parameter matching accuracy, to confirm the number of recalled results falls within the preset range. A new monthly operation log is uploaded. Index update status is checked to confirm only newly added documents are included in the index, and no full index rebuild is triggered. The vector database type is switched to pgvector. Retrieval with structured fields is performed to verify that grid equipment parameters for a specified model can be matched accurately.
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.