What data for this category looks like
The data for this category primarily comes from audio files, exported content from annotation platforms, proprietary vocabularies, and spot-check samples. Daily processed documents include transcription specifications, audio lists, quality inspection reports, and speaker annotation tables. Data updates follow a daily or per-batch task schedule. Most documents contain structured fields such as audio duration, speaker ID, annotation tags, and quality inspection scores. Some documents are accompanied by metadata associated with the original audio. Fields and units must match the exclusive rules of the annotation task, and there is no unified universal format.
What constraints do these characteristics impose on document parsing and chunking
The data characteristics of this category impose multiple constraints on document parsing and chunking. First, data sources include audio-associated metadata. Parsing processes must prioritize associating context with the corresponding audio. This prevents metadata and transcription content from being separated during chunking. Update frequency follows a daily or per-batch task schedule. Systems must support batch parsing of multiple documents of the same type. Chunking rules must adapt to structural differences across documents. Documents contain exclusive fields such as proprietary vocabularies and speaker annotations. Chunking processes must retain field association relationships. This prevents the loss of correspondence between proprietary words and speakers after splitting. For documents involving noise segment judgment and speaker differentiation, chunking must retain key context. This ensures that complete annotation logic can be associated during subsequent retrieval.
How to configure settings
All configurations below are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents contains proprietary words and speaker annotations. Chunk length must cover complete annotation logic units to avoid splitting proprietary word association relationships |
chunkSplitter | By paragraph + semantic boundary | Most documents in this category contain structured fields and associated metadata. Splitting by paragraph preserves complete field associations, and semantic boundaries avoid separating context |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Some documents in this category are accompanied by original audio files. Parsing requires loading associated resources, so the timeout setting must adapt to the processing rhythm of batch tasks |
customPdfParse | Enabled | Transcription specifications and quality inspection reports for this category are mostly in PDF format. Enabling custom parsing preserves the layout and association relationships of structured fields |
chunkSettingMode | Manual configuration | Documents in this category have large structural differences. Manual configuration allows flexible adjustment of chunking rules to adapt to different task types |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some documents in this category are accompanied by large-volume audio files. Upload limits must adapt to the file scale of batch tasks |
Three common misconfigurations
- Phenomenon: After uploading an Excel-format audio list or speaker annotation table, the field correspondence displayed in the knowledge base does not match expectations. Associated audio cannot be matched during retrieval. Cause: The
customPdfParseconfiguration is not enabled. The default parsing rule does not retain the column association relationship of the table. This results in loss of field correspondence information after chunking. - Phenomenon: After parsing a transcription specification document, the chunked content contains redundant text for noise segment judgment. Irrelevant segments are returned during retrieval. Cause: The
chunkSizesetting is too small. This separates the complete context logic of noise judgment, resulting in incomplete retrieval results. - Phenomenon: When batch uploading multiple quality inspection reports, some tasks fail to parse and show a timeout status. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting is too small. This does not adapt to the parsing time of documents accompanied by audio files.
How to confirm configurations are correctly set
- Upload a single typical document, such as a single audio list. View the parsed chunked content to confirm that fields such as proprietary words and speaker ID are fully retained without separation.
- Batch upload documents of the same type. Check the status of parsing tasks to confirm that there are no batch timeouts or parsing failures. Adjust the timeout setting to adapt to task scale.
- Simulate a retrieval scenario. Input a query term containing a proprietary word or speaker ID. Confirm that the returned chunked content contains complete associated context without missing or redundant segments.
- Upload an Excel-format annotation document. Check the field mapping relationship in the knowledge base to confirm that it matches the column correspondence of the original table.
The values given here 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 2026-10-19.