What the data for this category looks like
Refined oil sales ledger data primarily comes from gas station POS terminals, oil tank level monitoring systems, and enterprise inventory management systems. Update cadence follows: daily ledgers are generated after each daily shift, and monthly summary reports are generated each month. Document types include structured Excel tables with fields such as site ID, fuel grade, fuel volume, and sales amount, using units of liters and yuan; semi-structured PDF monthly summary reports containing multi-page sales charts and text descriptions.
What constraints these characteristics impose on document parsing and chunking
Daily ledgers have small data volume but frequent updates. Parsing tasks must control runtime to align with business synchronization rhythms. Structured Excel tables have clear fields and fixed units. Chunking must fully retain metadata to avoid losing field association information during retrieval. Monthly PDF summary reports have a large number of pages. They require long-document parsing capabilities to prevent context breaks from overly large chunks, or retrieval redundancy from overly small chunks. Differences in structure across document types require flexible chunking strategies. A unified default configuration cannot be used.
Configuration guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | A single refined oil ledger record contains multiple associated fields. An overly large chunk will break contextual associations between fields. An overly small chunk will increase redundant overhead during retrieval |
chunkSplitter | Split by structured fields + semantic segments | This setting adapts to both column-level splitting for structured Excel and semantic paragraph splitting for semi-structured PDF, balancing field integrity and semantic coherence |
customPdfParse | Enable OCR + text extraction mode | Some scanned paper ledger uploads only contain image content. OCR parsing is required to extract text and data from images |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Monthly summary PDF documents have many pages. The default default timeout cannot complete full parsing. The duration must be extended to ensure parsing completeness |
chunkSettingMode | Manual configuration mode | Chunking parameters must be adjusted separately for Excel and PDF documents. The platform's default unified chunking configuration cannot be used |
Three common configuration mistakes
- Missing fields in retrieval results after uploading Excel ledgers. This occurs because structured data parsing mode is not enabled, and only plain text is extracted, leading to loss of field association information.
- No images displayed after uploading PDF documents. This occurs because the OCR mode of
customPdfParseis not enabled, and only plain text is extracted without processing image content. - Frequent parsing task timeouts. This occurs because the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default short timeout duration cannot handle large-capacity monthly ledger documents.
How to verify correct configuration
- Upload a single monthly PDF ledger, wait for parsing to complete, and check the parsing log to confirm that both text and images are extracted normally.
- Retrieve a specific fuel grade in the ledger, check whether the returned results include corresponding fields and numerical units, to verify that metadata retention is complete.
- Adjust the
chunkSizeparameter, compare the number of retrieval recall entries under different chunk lengths, and confirm that it meets the business requirements for contextual association. - Upload a large-capacity Excel file, confirm that the parsing runtime does not exceed the configured
PARSE_FILE_TIMEOUT_SECONDSduration, to verify the reasonableness of the timeout configuration.
The values provided 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 2026-09-28.