What the Data for This Category Looks Like
Documents and materials for new energy development mainly come from approval documents and execution materials across the entire project lifecycle. These include project application reports, environmental impact assessment reports, grid connection acceptance opinions, land pre-examination opinions, and more. Data updates align with project phase progression, with staged additions or revisions within a single project cycle. Most documents have multi-chapter structured content, with fixed fields such as project number, construction location, installed capacity, and investment amount. The word count of some core single reports can reach tens of thousands of characters.
Constraints Imposed on Vector Models and Indexing
The structured fields and staged update characteristics of new energy development documents create multiple constraints for the vector indexing process. Multi-chapter structured content requires retaining field associations during chunking to avoid splitting that breaks field integrity. The high word count of long documents requires chunk lengths adapted to model input limits, while setting a reasonable overlap length to maintain context coherence. Staged update data requires indexes to support incremental updates, reducing resource consumption from full index reconstruction. Unit differences in fixed fields require preprocessing to unify unit expressions, avoiding deviations in vector matching caused by inconsistent units.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the average length of single chapters in new energy development documents, avoid splitting that destroys structured field associations |
| Overlap Length | 100–200 characters | Ensure context coherence between adjacent chunks, cover cross-chapter associated content in long documents |
| Similarity Threshold | 0.75–0.85 | Filter low-match irrelevant content, adapt to the semantic matching accuracy of professional terms in new energy documents |
| Single Search Limit | Top 8 entries | Cover multi-dimensional professional information in project documents, avoid overly scattered recalled content |
maxToken | 4096 | Adapt to the input length limit of mainstream open-source models, carry chunked context content of new energy documents |
defaultToken | 1024 | Ensure context filling for basic question-and-answer scenarios, meet information needs for general consultations |
Three Common Misconfigurations
- Phenomenon: Vector indexing tasks stay in a pending state with no progress. Cause: Incremental indexing trigger rules are not configured, and full indexing loads an excessive number of historical project documents, exceeding system resource limits.
- Phenomenon: Knowledge base search returns content unrelated to the query. Cause: The similarity threshold is set too low, or associated structured fields are split during chunking, leading to deviations in semantic matching.
- Phenomenon: Fields are lost when importing or exporting applications. Cause: Metadata for core fields such as project number and installed capacity is not retained in chunking configuration, resulting in metadata loss during import.
How to Verify Proper Configuration
- View indexing task progress logs, confirm that the number of chunks matches the expected number of documents, with no abnormal errors.
- Submit a test query, verify that returned result fields include project-related professional information, with no obvious irrelevant content.
- Perform an import and export test for the application, confirm that all configuration items and metadata are fully retained with no missing content.
- Test the incremental update function, confirm that newly added project documents can be indexed normally without requiring full index reconstruction.
The values provided are common starting points and should be measured 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.