What the data for this category looks like
E-commerce service investment research data primarily originates from public e-commerce platform product catalogs, real-time transaction APIs, user review crawlers, and publicly available industry supply chain documents. Data update cycles vary widely: basic product information is synchronized daily, real-time sales volume and user reviews are updated in real time alongside transactions, and supply chain cycle data is updated weekly. Most individual documents are structured product cards, containing fields such as SKU code, primary/secondary category hierarchy, selling price, monthly sales volume, user rating, supplier onboarding timeliness, logistics cycle, and more. Units include yuan, pieces, days, percentage, and others.
What constraints these characteristics impose on model integration and configuration
The structured fields of e-commerce service investment research data are numerous and carry specific units. This requires configuring standardized field mapping rules during the model integration phase to prevent distortion of investment research data caused by unit conversion errors. Real-time updated transaction and review data requires setting reasonable timeout and retry parameters for API calls, to adapt to volatility risks during high-frequency pulling and prevent data pulling interruptions. Long-text product details and user review content requires configuring appropriate segmentation rules to match the model's context window limits, avoiding content truncation that impacts retrieval performance. The multi-dimensional investment research indicator system requires configuring recall and re-ranking weight rules to ensure core business indicators are prioritized for retrieval.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the average total length of e-commerce product details and user reviews, matches the context window limits of mainstream large models |
recallTopK | Top 20–30 entries | Covers the basic retrieval scope across multiple categories and SKUs required for e-commerce investment research, avoids missing core products |
rerankTopN | Top 5–10 entries | Focuses on core highly relevant products, meets the demand for precise results in investment research reports, and reduces redundant information interference |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing complexity of batch-imported structured SKU lists and supply chain documents, prevents false timeouts for small documents |
similarityThreshold | 0.65–0.75 | Matches the category and attribute similarity judgment standards for e-commerce products, filters low-relevance retrieval results |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to the common file sizes of batch-imported product catalogs and industry reports, meets the needs of batch investment research data imports |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to conduct tests on your own samples before finalizing settings.
Three common configuration errors
- When testing a re-ranking model, a
Model not founderror is returned, while configurations on other platforms work normally. This occurs because only a universal call key was filled in, and the dedicated API address and token for the re-ranking model were not configured in FastGPT. - The interface displays
No available models, and pre-configured e-commerce investment research-specific models cannot be selected. This happens because the model was not bound to the vector database configuration of the current knowledge base, or access permissions for the model call API were not enabled. - After filling in the token key generated by OneAPI, model calls return an
empty responseorinvalid keyerror. This occurs because an independent API path was not configured for the e-commerce-specific model in OneAPI, or the token's permission scope does not cover model calls.
How to confirm successful configuration
- Call the model test interface, enter search terms related to e-commerce products, and verify that returned result fields include preset core business information.
- Upload batch product documents, check parsing progress and results, and confirm no exceptions are triggered by parsing timeout or file size configurations.
- Adjust recall and re-ranking parameters, compare the number of retrieval results across different values, and confirm parameters take effect.
- Check model call logs to confirm interface requests carry correct tokens and API addresses, with no null values or incorrect fields.
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.