What the data for this category looks like
This category of data primarily originates from client system interfaces, API documentation, test data, and integration logs. Update frequencies are typically daily or per project phase. Commonly processed documents include interface lists, integration records, field mapping tables, and issue tracking sheets. Most are in structured or semi-structured formats, containing fields such as interface addresses, request methods, parameter names, return codes, and business meanings. Some documents include time-stamped call records and exception information. Data structures are dynamically adjusted based on project phases.
Constraints on document parsing and chunking
The structured nature of this category’s data requires chunking to preserve relational connections between fields, and avoid splitting cross-associated field mapping content. Time-series integration logs must retain the context of the call chain, to ensure the complete call flow can be reconstructed during retrieval. Frequently updated data requires chunking to support fast incremental parsing, eliminating the time delays caused by full re-parsing. When processing multiple document types together, the system must adapt to the structural traits of each document type. For example, table documents must retain row and column associations, while log documents must preserve temporal order.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | The single record length of interface lists and field mapping tables is moderate, which avoids splitting cross-associated fields and ensures contextual integrity during retrieval |
chunkSplitter | Split by structured data | This category of data is mostly structured content in table or JSON formats, retaining the associations between fields and rows to avoid breaking business logic |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Integration logs may contain time-series data from multiple stages, requiring sufficient processing time during parsing to avoid mid-parsing timeouts and interruptions |
chunkSettingMode | Manual mode | It is necessary to individually adjust chunking rules for different document types such as interface lists, integration logs, and field mapping tables, to adapt to the business characteristics of each document |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some integration logs and test data have large file sizes, so it is necessary to allow uploading documents that match business scale |
Three Common Mistakes
- Symptom: After importing an Excel-format field mapping table, complete field correspondences cannot be matched during retrieval. Cause: Chunking split cross-cell associated fields, breaking the mapping relationship so complete business logic cannot be linked during retrieval.
- Symptom: Table content displayed in the knowledge base is missing some rows or columns when output in question-and-answer responses. Cause: Chunking did not retain the full structural context of the table, only splitting individual cell content and losing row and column association information.
- Symptom: After using a local vector model to complete chunking and uploading to the server, retrieval results are empty or do not match expectations. Cause: Chunking logic and embedding rules differ across vector models, causing chunked text to not be correctly recognized by the server-side indexing system.
How to Verify Proper Configuration
- Upload a single interface list document, and check if the chunking result retains the complete request parameters, return codes, and business meanings of a single interface, with no cross-field splitting occurring.
- Import an Excel-format field mapping table, submit a retrieval request that includes field correspondences, and confirm the returned result contains complete associated information.
- Upload an integration log containing multi-stage calls, and confirm the chunking result retains the context of temporal calls, allowing the full call chain to be reconstructed during retrieval.
- In FastGPT v4.17.0, adjust the
chunkSizeparameter and re-parse the document, then verify the chunk length falls within the preset 800–1200 character range.
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 2026-10-19.