What Data for This Category Looks Like
Data for auto service intelligent due diligence comes from vehicle registration archives from vehicle management offices, brand after-sales maintenance systems, used car trading platform archives, insurance company claim records, and paper or electronic reports from offline vehicle inspection institutions. Update cadences vary by data type: registration archives update in real time with vehicle transactions and annual inspections, maintenance records update after each repair submission, and claim records sync in real time with policy endorsements.
Document structure includes two types of content: structured fields such as 17-digit VIN code, mileage (unit: kilometers), license plate registration date, number of repairs, number of claims, annual inspection status; unstructured content such as text descriptions of maintenance work orders, OCR recognition text from inspection photos, and accident scene photo notes. The number of documents associated with a single due diligence report is usually large, including multiple scattered sub-files.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Pulling multi-source heterogeneous data requires splitting nodes by data source type. Different data sources have different update frequencies, so incremental sync trigger conditions must be configured to avoid repeated full data pulls. The 17-digit VIN code is the unique identifier for a vehicle, so a validation node must be added at the start of the workflow to filter invalid non-standard VIN data.
Unstructured inspection photos require calling an OCR parsing node first before associating with structured fields, which increases the complexity of workflow nodes. The large number of documents associated with a single due diligence report requires configuring pagination pull and batch parsing parameters to avoid workflow interruptions caused by excessive single-processing data volume. Differences in field naming across data sources require configuring a field mapping node to unify formats and ensure consistent fields for subsequent processing.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Auto service due diligence documents often contain multi-page maintenance work orders and high-definition inspection photos, which take a long time to parse. 300 seconds covers most scenarios |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single vehicle archive may include dozens of high-definition inspection images and complete maintenance records, so large file upload support is required |
Chunk size | 800–1200 characters | Auto service due diligence data includes structured fields and unstructured maintenance descriptions. This segment length balances context association and model processing efficiency |
Recall count | Top 8 entries | Due diligence reports need to cover three core types of data: maintenance, claims, and collateral. Excessive recall increases model processing load |
Similarity threshold | 0.75 | Low-match cross-model irrelevant data must be filtered, while valid records for the same model across different batches must be retained |
http_request_timeout | 60 seconds | Sufficient response time must be reserved when calling external data sources such as vehicle management office interfaces, to avoid workflow interruptions due to mid-process timeouts |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on local samples before finalizing values.
Three Common Mistakes
- The symptom is a
408 Request Timeouterror returned during workflow execution. The cause is that reasonable timeout parameters were not configured for pulling multi-source data from auto service scenarios, leading to timeout of external interface calls. - The symptom is that the original files associated with the generated due diligence report cannot be previewed, and fields are empty. The cause is that the scope of the
file link variablewas not configured correctly, and the original file path was not correctly passed to the preview node. - The symptom is abnormal base64 encoding format output by the HTTP request node. The cause is that
Content-Type: application/jsonwas not correctly set in the request header, causing FastGPT to fail to correctly parse the returned image data.
How to Confirm Configuration Is Correct
- Execute the test node of the workflow, check the configured value of
PARSE_FILE_TIMEOUT_SECONDSin the logs, and confirm that no timeout error was triggered during the parsing phase. - Upload a test archive containing a VIN code and supporting inspection photos, generate a due diligence report, and click the associated file link to confirm that the original document can be previewed online.
- Trigger the HTTP request node to call the local image-to-base64 interface, check whether the returned result contains the standard
data:image/png;base64,prefix, and confirm that the format is correct. - Adjust the
Similarity thresholdto 0.6, re-execute the workflow, and check the change in the number of recalled data entries to confirm that the threshold configuration takes effect for controlling recall results.
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.