What data looks like for this scenario
Daily documents for this scenario include territorial spatial master plans, detailed plans, special plans, planning implementation evaluation reports, public opinion summaries, and more. Data sources include the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics, and remote sensing images. Data is updated annually. Document structures cover text chapters, multi-format tables, spatial coordinate annotations, and professional control fields. Fields involved include cultivated land retention quantity, construction land scale, and spatial control scope, with common units such as square kilometers, hectares, and ten thousand people.
What constraints do these characteristics impose on document parsing and chunking
Multi-source and heterogeneous data sources lead to mixed document formats. These include plain text reports, large volumes of structured tables, and remote sensing-related data. Annual updated documents are often large in size. Some single documents can reach 100,000 Chinese characters or 15,000 rows of Excel data. Professional fields and spatially associated content require that chunks do not split context. Otherwise, retrieval will fail to associate control indicators with their corresponding spatial ranges. Large files also require longer processing durations for parsing and chunking. This avoids timeouts or chunks that are too large, which prevent the model from processing normally.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Planning documents contain professional terminology and spatial control indicators. This range avoids splitting that breaks complete business context while controlling token consumption per chunk |
chunkSplitter | Split by paragraph + table header | Planning documents have clear chapters and table headers. Splitting this way preserves professional content associations within chapters, preventing cross-chapter control indicators from being split apart |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single special planning documents may contain large numbers of summary tables and attachments. This duration meets full parsing requirements for large documents and avoids timeout errors |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some planning documents include remote sensing image attachments and multi-source data summary tables. This upper limit supports complete upload and parsing of large files |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types such as Word reports and Excel data tables to adapt to the granularity requirements of professional data |
customPdfParse | Enable table structured parsing | Control indicator tables and spatial layout tables in planning PDF documents need to fully extract headers and corresponding row data to avoid disordered fields after parsing |
Three common misconfigurations
- Symptom: After uploading an Excel file with 15,000 rows, the large model returns incomplete instruction prompts. Cause: Default chunking parameters were not adjusted. Per-chunk data volume is too large, exceeding the model's context capacity, resulting in failure to correctly match classification tasks after parsing.
- Symptom: A timeout error occurs when parsing a Word document with 100,000 Chinese characters. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default timeout duration is insufficient to complete full parsing of large documents. - Symptom: Control indicators and their corresponding spatial ranges are split into different chunks after chunking, making association and matching impossible during retrieval. Cause: The split rule by paragraph + table header was not configured. Only fixed character-based splitting was used, destroying the context relevance of professional content.
How to verify proper configuration
- Upload a single planning document with 100,000 Chinese characters. Check that parsing completes within the set timeout duration with no timeout errors.
- Extract control indicator rows from the Excel table. Verify that each data chunk after chunking contains complete headers and corresponding business data.
- Initiate a knowledge base classification matching task. Check that retrieved result chunks contain complete planning chapters or indicator context with no content splitting.
- Adjust the
chunkSizeparameter. Compare the number of retrieval recalls under different chunk lengths to confirm that granularity meets business requirements.
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.