What the data for this category looks like
Ledger data for new energy development primarily comes from project approval documents, grid connection acceptance reports, daily operation and maintenance logs, subsidy application materials, and similar sources. Data update frequency varies by project phase: core parameters are updated weekly during the construction phase, operating conditions are recorded daily during the operation phase, and summaries are archived monthly after project completion. Each individual document includes structured fields such as project ID, site latitude and longitude, installed capacity (unit: MW), grid connection time, operation and maintenance cycle, consumable replacement records, and also includes unstructured scanned documents and on-site photos. Some summary report documents can reach several thousand characters in length.
What constraints these characteristics impose on the vector models and indexing link
The ledger data for new energy development is scattered across structured fields and unstructured materials, which requires the indexing system to support both field-level matching and full-text semantic association. Data update frequencies differ greatly across project phases, so the system needs to support flexible switching between incremental indexing and full reindexing. Fields such as installed capacity and site latitude and longitude have clear business weights, so false recalls of low-relevance content must be avoided. The length of individual documents varies significantly, so unreasonable chunking that damages the integrity of business context must be prevented. Additionally, heterogeneous data from multiple sources must be unified into a consistent embedding format to ensure consistency in the vector space.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of individual fields and associated content in new energy ledgers, avoids splitting that disrupts business logic |
| Overlap Length | 100–150 characters | Preserves cross-chunk operation and maintenance cycle, grid connection related information, prevents context breaks |
| Similarity Threshold | 0.72–0.85 | Distinguishes core matching fields such as installed capacity and site for new energy projects, reduces false recalls |
defaultToken | 4096 token | Adapts to the context length requirements of summary reports, covers the content volume after multiple ledger entries are combined |
maxToken | 8192 token | Limits the maximum content processed in a single indexing task, prevents resource overflow |
| Reranking | Calibrated based on actual measurements | Adapts to the field weights of new energy data, optimizes search result ranking logic |
Three common mistakes
- Excessively long indexing task duration: The symptom is that the task status remains uncompleted for more than 300 seconds when calling the knowledge base interface to create an index. The cause is that the chunk length setting exceeds the system's single-processing limit, resulting in too much content in a single batch.
- Invalid old data after embedding model change: The symptom is that imported knowledge base search results are empty or have abnormal matching degrees. The cause is that vector embeddings for already imported documents are not regenerated, only the currently configured embedding model is updated.
- Search result ranking does not align with business logic: The symptom is that low-relevance site fuzzy matching results appear before high-relevance installed capacity precise matching results. The cause is that reranking rules adapted to core new energy fields are not configured.
How to confirm proper configuration
- Upload a single piece of new energy ledger test data, check whether the chunking preview interface fully retains core business fields such as project ID and installed capacity.
- After switching the embedding model, run a batch indexing task, check whether the task log shows normal vector generation progress and no error messages.
- Submit a search request that includes installed capacity and site latitude and longitude, verify whether the result ranking follows business matching priorities.
- Check the system resource monitoring panel, confirm that the content volume processed in a single indexing task does not exceed the preset threshold of FastGPT v4.17.0.
The values provided here are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.