Vector Models and Indexing for Power Industry Intelligent

What data for this category looks like

Data sources for power industry intelligent due diligence reports include grid dispatch logs, power plant operation ledgers, electricity transaction settlement documents, project feasibility study reports, and industry policy documents. Update cadences have layered differences: real-time operation data updates every second, monthly transaction ledgers sync daily, and policies and feasibility studies update irregularly alongside project progress. Document structures include structured parameter entries, semi-structured log snippets, and long-text reports. Fields include device_id, parameter_unit, transaction_amount, and others. Units are mostly industry-specific identifiers such as MW, g/kWh, yuan, and similar markers.

What constraints do these characteristics impose on vector models and indexing?

Layered update data sources require indexes to support mixed incremental and batch processing, to avoid resource waste from full reindexing. Multi-type document structures need adaptation for long-text segmentation and structured field encoding, to prevent splitting semantic connections between parameters and units. Industry-specific fields and units require preserving metadata during vectorization, otherwise vector confusion will occur for identical parameters across different generating units. Additionally, datasets with hundreds of thousands of entries require optimizing index retrieval efficiency, to avoid excessive search delays that impact due diligence efficiency.

How to configure the settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Power due diligence reports contain both long feasibility study texts and short parameter entries; this range balances contextual association and recall accuracy
`chunk_overlap`	100–150 characters	Structured snippets such as power equipment parameters and transaction records need to retain associated units and field names to avoid semantic splitting from segmentation
`TEXT_EMBEDDING_MODEL`	`bge-large-zh-v1.5`	This model delivers better encoding for power industry terminology, adapting to the professional text content of due diligence reports
`VECTOR_STORE_TYPE`	`pgvector`	Compatible with PostgreSQL vector storage, supporting mixed indexing of structured numerical values and unstructured texts in power due diligence data
`recall_top_k`	Top 8–12 entries	The number of core indicators for power due diligence is limited; excessive recall will introduce irrelevant log snippets and reduce retrieval precision
`SIMILARITY_THRESHOLD`	0.75–0.85	Power industry parameters have high numerical precision requirements; low-similarity irrelevant entries need to be filtered to ensure the reliability of due diligence results

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: The number of returned results from search tests is far lower than expected, or no matching content is returned. Cause: Field-level vectorization is not configured for the structured fields of power due diligence, and only full-text encoding is used, leading to loss of semantic connections between parameters and units.
Phenomenon: An error occurs when writing to the vector database, prompting that the index structure does not support numerical fields. Cause: VECTOR_STORE_TYPE is not configured as pgvector, which cannot support mixed storage of structured numerical values and unstructured texts in power due diligence data.
Phenomenon: Index tasks time out, with a status code of 504 Gateway Timeout. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter is not adjusted, and the parsing time of a single long feasibility study report exceeds the default threshold of the V4.8.20-FIX2 version.

How to confirm the configuration is correct

View the index structure of the vector database, confirm that vector encoding is included for industry-specific fields such as device_id and parameter_unit.
Submit a single power feasibility study report, check that the parsed segment length falls within the configured chunk_size range.
Initiate a search test for specific unit parameters, verify that the similarity scores of the recalled results fall within the configured SIMILARITY_THRESHOLD range.
Check the TEXT_EMBEDDING_MODEL configuration item, confirm that the model parameters adapted to power professional texts are selected.

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.