What the Data for This Category Looks Like
Data for white goods intelligent due diligence reports in the financial sector comes primarily from brand manufacturer official product manuals, national energy efficiency label filing databases, offline third-party test reports, and mainstream e-commerce platform parameter pages. Data update rhythms align with new product launch cycles. Stock regular models have lower update frequencies. A single document typically includes fields such as product model, energy efficiency rating, core performance parameters, physical dimensions, total weight, material specifications, and warranty policies. Parameter units mostly use standardized measurement units such as watts, millimeters, kilograms, and kilowatt-hours. Some documents include compliance test numbers and batch information.
What Constraints Do These Characteristics Impose on Model Access and Configuration?
Multi-source, heterogeneous data sources require configuration that supports parsing and adaptation for multiple file formats. This prevents core parameter loss due to format differences. Non-fixed update rhythms require adjusting index trigger thresholds to accommodate quickly added new product data. Documents contain large numbers of performance parameters and compliance labels with specific units. Model configuration must retain associations between fields. This avoids truncating critical parameter combinations during splitting. Additionally, some documents include batch and test numbers that must be fully preserved. This prevents gaps in compliance checks during subsequent due diligence steps.
How to Set Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | White goods due diligence documents often include multi-page performance parameter tables and compliance attachments. Parsing takes longer, so extend the timeout to avoid interruptions. |
MAX_SEGMENT_LENGTH | 1000–1200 characters | White goods parameter documents have compact, closely linked field descriptions. Segments that are too long will break parameter context. Segments that are too short will lose associated information. |
RECALL_TOP_N | Top 8 results | Due diligence reports need to cover multi-dimensional parameters such as energy efficiency, dimensions, and performance. Too few recalled entries risk missing core metrics. Too many will add redundant content. |
SIMILARITY_THRESHOLD | 0.75–0.85 | White goods parameters mostly use standardized measurement descriptions. Set a reasonable threshold to balance precise matching and full recall requirements. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Some manufacturers provide complete product manuals with high-definition test reports and multi-page attachments. This setting accommodates large file upload scenarios. |
embedding_batch_size | 32 items per batch | Embedding processing for long text parameter documents needs to balance computing efficiency and memory usage. 32 items per batch works with most hardware configurations. |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Analyze specific issues individually, and test against your own samples before finalizing settings.
Three Common Configuration Mistakes
- Scenario: After using FastGPT v4.9.11 to access qwen3-embedding-8b, the file status remains "Indexing". Cause: The embedding model's batch size parameter is not configured correctly. Batch processing of long text parameter documents exceeds the model's load threshold, causing the indexing process to block.
- Scenario: After accessing an Azure OpenAI model, a 401 unauthorized error is returned during calls. Cause: The Azure OpenAI API key and resource endpoint are not filled in correctly, or the model ID does not follow Azure's naming rules.
- Scenario: The energy efficiency rating field in imported white goods documents is not recalled correctly. Cause: The segment length is set too short, truncating the associated context between the energy efficiency rating and corresponding parameters. This prevents the model from recognizing the field association.
How to Confirm Configuration Is Correctly Set
- Upload a standard white goods product manual. Review the parsed segment results. Confirm that core parameters are not truncated. Adjust
MAX_SEGMENT_LENGTHbased on segment performance. - Initiate a parameter recall test. Verify that the number of returned results matches the
RECALL_TOP_Nsetting. Adjust the threshold based on recall completeness. - Call the model to generate a due diligence report snippet. Check that core fields such as energy efficiency rating and dimensions are correctly associated. Adjust the similarity threshold based on results.
- View system logs. Confirm that there are no timeouts or errors in embedding model call requests. Adjust
PARSE_FILE_TIMEOUT_SECONDSbased on logs.
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.