Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data primarily comes from declaration materials and archived files for new energy development projects, including project approval applications, environmental impact assessment reports, grid connection approval documents, site inspection records, and more. Update frequency follows project approval processes. The cycle from initial declaration to completion and archiving for a single project varies widely. Documents mostly combine structured forms and long-text attachments. Fields include project number, installed capacity (unit: MW), approval document number, submission date, and others. Attachments are mostly PDF or Word format technical description materials.
What constraints these characteristics impose on the vector model and indexing link
Structured form fields have fixed formats. Numeric fields such as installed capacity in MW require adaptation to vector encoding logic to avoid unit interference with semantic consistency. Long-text attachments have significant length. Reasonable chunking rules must be set to retain technical details. Project approval cycles vary widely, with staged data updates. Indexes must support incremental synchronization without full reconstruction. Text extraction results from multi-format attachments have format differences. A unified preprocessing workflow must be implemented before entering the vector modeling phase.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the long-text details of new energy technical descriptions, avoid information overload or semantic fragmentation in a single chunk |
| Overlap Length | 100–150 characters | Retain contextual association between adjacent chunks, avoid technical parameters being truncated by chunking |
maxToken | 4096 token | Adapt to the input length limit of mainstream vector models, match the chunked text length of new energy documents |
defaultToken | 512 token | Serve as the minimum retention unit for chunked text, avoid truncation of key technical information |
| Single Search Limit | Top 10 entries | Cover the number of associated materials common in file review, avoid redundant results interfering with review |
| Maximum Context | 8000 characters | Integrate retrieved relevant chunks to provide complete technical reference for review |
Three common mistakes
- Vectorization interface calls take a long time and fail to meet expected batch processing performance. This occurs because only a single text chunk is submitted for vectorization, batch submission configuration is not enabled, and the vectorization service’s batch processing capability is not utilized.
- The original document location and page number information of chunks cannot be viewed in retrieval results. This happens because the index metadata storage switch is not enabled, so the system fails to record chunk index information.
- Format anomalies appear in vectorization results after connecting a specified multimodal vector model. This occurs because correct input parameters are not configured for the model, and the system does not match the text processing logic required by the model.
How to confirm the configuration is correct
- Upload an environmental impact assessment report attachment for a new energy project, check the chunked results generated by the system, and confirm that the chunk length and overlap ratio meet the configuration requirements.
- Perform a file review retrieval, check whether the returned results include chunk metadata information, and confirm that the index configuration takes effect.
- Call the vector model interface to submit test text, verify that the returned vector data format matches the requirements of the configured model.
- Submit multiple text chunks, confirm that the vectorization service call process conforms to the expected batch processing logic.
The values listed here are common starting points and should be evaluated against the user’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.