What the data for this category looks like
Marketing content data sources for the automotive service industry include script libraries exported from internal marketing systems, scanned offline event materials, archived customer service conversation logs, regional service package plans, and more. Update frequencies vary by business scenario: promotion plans are updated 1 to 3 days before launch, vehicle parameter manuals are adjusted with annual model updates, and customer service script libraries iterate monthly based on user hot topics. Document structures include long-text scenario scripts, structured service package parameter tables, and activity templates with placeholders. Fields cover vehicle identification codes, activity validity periods, service pricing, covered outlet counts, and more. Units include yuan, days, kilometers, units, and others.
What constraints do these characteristics impose on document parsing and chunking?
Diverse data sources produce documents with mixed formats. Some are structured tables that can be parsed directly, while others are scanned materials requiring OCR recognition. Parsing must support both text extraction and image recognition. Frequently updated content cannot use fixed chunking rules, so the system must adapt to dynamically changing document structures. Document lengths vary widely: some single scripts are only a few hundred words, while annual marketing plans can span tens of thousands of characters. Chunking rules must balance the integrity of short texts and the readability of long texts. Specific fields have fixed unit bindings, so chunking must not split associated information. For example, do not split "Maintenance package pricing: 299 yuan" into separate value and unit chunks.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the common length of automotive service marketing content, retains complete context for scenario scripts and activity rules |
chunk_overlap | 100–150 characters | Prevents splitting key associated information across chunks, such as the pre-trigger conditions and core content of a script |
parse_mode | auto | Automatically identifies mixed-format documents, processes both text blocks and structured tables |
enable_ocr | true | Supports parsing scanned documents such as offline event materials and after-sales manuals |
field_extraction_threshold | 0.75 | Balances the accuracy and recall of field recognition, ensures key information such as vehicle models and pricing is correctly extracted |
max_parsed_content_length | 50000 characters | Prevents parsing timeouts for overly long documents, fits the length range of most automotive service marketing documents |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: When calling the chunking parsing API of FastGPT 4.8.10 and above, an
invalid_chunk_indexerror code is returned, or the chunk position index cannot be obtained. Cause: The correct document identification parameter is not included in the request, or thechunk_overlapsetting is unreasonable, resulting in invalid recording of chunk boundaries. - Phenomenon: When parsing scanned vehicle parameter manuals, unit information is lost. For example, "Range 500km" is recognized as "Range 500". Cause: The
enable_ocrparameter is not enabled, or thefield_extraction_thresholdis set too high, resulting in failure to recognize unstructured unit fields. - Phenomenon: When parsing activity templates with variable placeholders, variables are split into different chunks, making it impossible to replace content completely in subsequent calls. Cause: The
chunk_sizeis set too small, splitting content with complete variable context into multiple independent chunks.
How to confirm the configuration is correct
- Upload a typical automotive service marketing document, such as an after-sales script draft, and check if the parsed chunks retain complete scenario context without splitting key scripts and trigger conditions.
- Call the chunking parsing interface, verify that the returned
chunk_indexfield matches the actual chunk position, and adjustchunk_sizeandchunk_overlapparameters to meet expectations. - Upload scanned event materials, check if the OCR recognition result completely extracts text and unit information, and confirm that the
enable_ocrparameter is enabled. - Upload a long annual marketing plan document, verify that parsing does not time out, and that the
max_parsed_content_lengthparameter matches the current document's length range.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.