Process Document and Exception Report Drafting for Meat Products and Slaughter Processing: Document Parsing and Chunking

This page is compatible with FastGPT v4.17.0.

What the Data for This Category Looks Like

Daily documents for this category include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data sources include animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection equipment, and laboratory test results. Update cadence follows: batch records are updated per production run, cold chain temperature data is updated continuously, and shift summary production records are generated each work shift. Documents include plain-text process procedures and multi-row-column production tables. Fields include batch number, carcass identification, temperature values, test indicators and results. Some fields have specific units such as ℃, cfu/g.

What Constraints These Characteristics Impose on Document Parsing and Chunking

Documents in this category have large individual file sizes, numerous table rows, and tightly linked fields. Individual process documents can reach 100,000 Chinese characters. Combined production records can exceed 15,000 rows of Excel data. These factors impose requirements on parsing timeouts and upload size limits. Batch and quarantine information must be bound and linked. Complete records for the same batch must not be split during chunking. Continuous temperature control data and real-time test results use uniform formats. The parsing workflow must retain the correspondence between table rows and fields during chunking to avoid messy data splicing. Frequently updated documents also require the parsing process to deliver efficiency suitable for fast retrieval.

How to Set Configurations

Three Common Mistakes

• Issue: A 504 timeout error triggers when parsing a 100,000-character Word document. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter was not adjusted, and the default value was used, causing large document parsing to interrupt.
• Issue: After importing 15,000+ rows of Excel production records, some fields are empty or spliced across rows in a messy way. Cause: chunkSplitter was not configured to split by table row, and the default chunking logic disrupted the correspondence between table rows and fields.
• Issue: When searching for batch keywords, returned chunk results cannot link to the corresponding batch’s quarantine and production records. Cause: chunkSize was set too small, splitting complete linked records for the same batch into different chunks, and losing contextual links.

How to Confirm Proper Configuration

• Upload a single 100,000-character process document, and check if parsing completes within the time limit set by PARSE_FILE_TIMEOUT_SECONDS with no timeout errors.
• Import 15,000+ rows of Excel production records, and verify that parsed table fields are complete with no missing values or cross-row splicing errors.
• Search for keywords for a specific batch, and check if returned chunk results include complete linked records for that batch with no contextual breaks.
• Adjust the chunkSize parameter, compare retrieval recall performance across different chunk lengths, and confirm the configuration threshold that meets business requirements.

The values listed here are common starting points and should be evaluated against the user’s own samples.