What Data for This Category Looks Like
Data sources for condiment due diligence reports include public category monitoring data released by industry associations, public compliance documents from manufacturing enterprises, and anonymous sales data from terminal retail. Update cycles are divided into monthly sales data updates, quarterly compliance report updates, and annual industry panorama report updates. The document structure of a single due diligence report includes production license documents, raw material traceability details, batch quality inspection records, sales channel data, and competitor comparison entries. Fields include raw material purchase weight, packaging specification, inspection qualification number, number of covered terminal stores, and monthly revenue amount. Corresponding units are kilograms, milliliters/grams, identification number, quantity, and yuan. Most document formats are a mix of PDF and structured Excel.
Constraints Imposed by These Characteristics on Model Access and Configuration
Multi-source heterogeneous data sources require the model access layer to support batch parsing of multiple formats including PDF, Excel, and structured text, to avoid limitations of single-format adaptation. Data sources with different update cycles need matching scheduled synchronization configurations, to ensure the latest data from the corresponding cycle is used when calling the model. Document structures that mix structured tables and unstructured text require configuring segmented parsing rules, to separately extract field-type information and descriptive information. Specific field units require the model to retain the association between units and fields during parsing, to avoid parsing results with unit confusion. Content from compliance documents requires enabling sensitive information filtering in access configurations, to ensure use of due diligence data complies with industry regulations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Condiment due diligence reports include multi-page quality inspection tables and inventory ledgers, with longer parsing times than general documents |
maxContext | 8000–12000 characters | Due diligence reports need to retain cross-field association information for raw material batches and sales data, requiring sufficient context |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch import of full due diligence report files for a single batch, adapting to multi-document import scenarios |
Number of retrieved entries | Top 5 entries | Core information of due diligence reports is concentrated in the first half of the document; excessive retrieved entries will introduce non-core redundant content |
Similarity threshold | 0.72–0.78 | Accurately match raw material traceability numbers and sales channel fields in due diligence reports, to avoid mismatching |
Segment length | 1000–1500 characters | Adapts to the length differences between table paragraphs and text paragraphs in due diligence reports, improving segmented parsing accuracy |
The parameter values provided on this page are general recommendations for establishing configuration baselines. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing configurations.
Three Common Configuration Mistakes
- Phenomenon: Model calls return
500 Internal Server Error, and container logs show port binding failure. Cause: A local model service occupies the default port, or the listening port of the custom model is not specified in FastGPT configurations. - Phenomenon: Custom API models cannot be selected, or calls return the
model not founderror. Cause: The API address and key of the custom model are not correctly filled in the FastGPT model management interface, or the enable switch for the custom model is not turned on. - Phenomenon: Parsing results for raw material weight and packaging specification fields in due diligence reports lack units. Cause: Unit retention rules for document parsing are not configured, causing unit information associated with fields to be automatically stripped during parsing.
How to Verify Configurations Are Correct
- Enter the FastGPT model management interface, check that the API address, key, and port configuration of the custom model match the locally deployed model service.
- Upload a single condiment due diligence report, and verify that the parsed fields fully retain relevant information for raw materials, packaging, and sales, along with their corresponding units.
- Initiate a model call for a due diligence report, and check that the returned results include preset core due diligence fields, with no obvious field missing or unit errors.
- View system logs, confirm there are no error messages such as port binding failure or model call timeout, and verify that the configured timeout time matches actual parsing times.
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.