Data Profile for This Category
This category of data primarily serves central enterprise strategic investment departments, project companies, state-owned assets supervision authorities, and external consulting and evaluation institutions. Commonly processed documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and investment post-evaluation reports. Data sources cover state-owned assets supervision and administration commission investment supervision systems, enterprise ERP and project management systems, industry market data, and audit financial statements. The update frequency is quarterly. Document structures include fields such as project approval processes, investment amounts, compliance clauses, investment decision-making committee opinions, and post-evaluation performance indicators. Individual documents are lengthy, and archived files are mostly batches of submitted historical files.
Constraints on Document Parsing and Chunking
These data characteristics create multiple constraints for the document parsing and chunking process. First, multi-source heterogeneous data sources lead to significant differences in document formats: structured tables exported from systems, formally formatted Word reports, and PDF scanned documents all require adaptation to different parsing rules. Second, quarterly updated batch archived files require the parsing process to support batch processing and retain file identifiers. Third, documents contain sensitive fields related to state-owned assets and core investment decision-making content. Parsing and chunking must avoid splitting key paragraphs while ensuring compliance information is not lost. In addition, documents in this category prioritize board investment decision records, negative list implementation status, and post-evaluation data. The chunking logic must prioritize retaining such structured paragraphs.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of individual feasibility study reports and post-evaluation reports, preserves paragraph integrity and contextual relevance |
customPdfParse | Enabled | For fixed-format PDF documents such as asset appraisal reports and investment decision-making committee materials, retains original typesetting and field structure |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents task failure due to parsing timeout for individual large archived documents |
chunkSplitter | Split by heading + paragraph | Prioritizes preserving the integrity of core chapters such as project approval, investment decision opinions, and post-evaluation indicators |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch submission of multiple large documents from quarterly archives |
chunkSettingMode | Manual configuration | Allows adjustment of chunking rules for different document types, adapting to the special structure of investment decision-making materials |
Three Common Configuration Mistakes
- PDF parsing suddenly fails, with symptoms of returning
500 Internal Server Erroror empty parsing results. The cause is a recent change to custom PDF parsing interface parameters that did not synchronize updates to the FastGPTcustomPdfParseconfiguration. - When uploading multiple files, parsing results cannot be distinguished. Symptoms include all files having empty or duplicate filename fields. The cause is not enabling the filename retention switch for custom parsing, resulting in chunked results not carrying original file identifiers.
- Knowledge base search fails to match target content. Symptoms include no matching results when entering keywords related to investment decision-making committees. The cause is an excessively large
chunkSizesetting, which splits paragraphs containing core investment decision opinions into multiple unrelated segments and loses critical information.
How to Verify Correct Configuration
- Upload a single feasibility study report, check if the parsed chunks include core fields such as project investment amount and compliance clauses, to verify that the
chunkSizeandchunkSplitterconfigurations are active. - Configure a Feishu HTTP call node, pass variables exported from FastGPT, and check if the returned status code is 200, to verify that the variable transfer logic is correct.
- Batch upload 5 quarterly archived documents, check if each file's parsing result carries a unique filename identifier, to verify that the batch processing configuration is active.
- Conduct search tests by entering keywords such as "investment decision-making committee opinions" and "post-evaluation performance", to verify that corresponding chunked content can be matched and confirm that the chunking rules meet business requirements.
The values listed 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.