What the data for this category looks like
Data for internal office and employee services in refinery and chemical production primarily comes from equipment operation and maintenance logs, process parameter ledgers, safety operation procedures, monthly production reports, and similar sources. Data sources cover on-site inspection terminals, DCS control systems, and enterprise intranet shared folders. Update frequencies include real-time (for process pressure and temperature parameters), daily (for production output reports), and periodic (for safety procedure revisions). Document structures include long Word manuals up to 100,000 Chinese characters, and multi-sheet structured Excel ledgers. Fields include equipment number, operating duration, pressure value, inspection time, and similar items. Units include ℃, MPa, hours, and other professional industrial units.
What constraints these characteristics impose on document parsing and chunking
Long documents and Excel files with excessive rows cause default chunking rules to fail, resulting in overly large single chunks that cannot accurately match user queries. Fixed headers and field associations in structured ledgers must be preserved; random chunking will destroy data semantic integrity. Real-time production data that times out during parsing will cause employee-acquired information to lag behind on-site operating conditions. Uploading and parsing large files requires adaptation to hardware resources; exceeding default limits will trigger parsing failures, affecting the efficiency of internal office work and employee training.
How to Configure
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of single inspection records and process parameters in refinery and chemical production, avoids semantic matching deviations caused by overly large single chunks, and controls token consumption |
chunkSplitter | Split Excel by header, split long documents by paragraph + business title | Preserves field association relationships in structured ledgers, avoids destroying business logic when splitting long documents by fixed character counts |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing duration for 100,000 Chinese character Word documents and Excel files with 15,000+ rows, prevents task interruption due to mid-parsing timeout |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Adapts to file sizes of large refinery and chemical process manuals and historical ledgers, meets batch upload requirements |
chunkSettingMode | Custom mode | Adjusts chunking rules separately for different types of refinery and chemical production documents, adapts to parsing requirements for structured and unstructured data |
Three Common Misconfigurations
- Incomplete prompt content returned after parsing Excel documents. The cause is failure to adjust chunking rules for structured Excel; the default chunking merges multiple rows of the entire table into a single chunk, preventing the large language model from identifying valid single business data entries.
- Parsing timeout occurs for 100,000 Chinese character Word documents. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter; using the default value causes parsing to be interrupted before completion. - RAG knowledge base classification matching results have overly coarse granularity. The cause is an excessively large
chunkSizesetting, where a single chunk contains multiple unrelated sets of production data, leading to insufficient matching accuracy.
How to Verify Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a single 100,000 Chinese character refinery and chemical process manual, check whether parsing is completed within the preset duration with no timeout errors.
- Upload a production ledger Excel file containing 15,000+ rows, check the number of chunks generated after parsing, confirm that single chunk data does not exceed the set
chunkSizerange. - Initiate a RAG query, input a single device inspection-related question, check whether returned results include accurate content of core fields such as equipment number and inspection time.
- Batch upload multiple different types of refinery and chemical production documents, check whether parsed chunks retain the association between fields and business logic.
The values provided are common starting points and should be validated against sample data specific to the deployment environment.
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.