Model Access and Configuration for White Appliance Research

What Data for This Category Looks Like

Data for this category comes from four main sources:

Public research reports from securities firms
Public reports from third-party home appliance industry monitoring agencies
Annual technical white papers from leading home appliance brands
Online and offline retail monitoring data

Update frequency varies by content type:

Securities firm research reports release alongside industry events and earnings cycles
Third-party monitoring data updates monthly
Brand white papers release quarterly or annually

Most documents include these sections:

Core conclusions
Market size data
Channel performance
Technology iteration directions
Competitive landscape analysis
Risk warnings

Common fields include:

Offline retail sales
Online sales revenue
New product launch cycles
Covered home appliance categories

Units include hundreds of millions of yuan, ten thousand yuan, months, and similar units.

Constraints on Model Access and Configuration

This category’s data has multiple sources and formats. Formats include PDF research reports, Excel monitoring spreadsheets, PDF white papers, and more. Content breaks down into specific home appliance sub-categories like drum washing machines and built-in refrigerators. Update frequencies differ widely.

These traits create four key requirements for model access and configuration:

Support multi-format parsing. Configure parsing rules for pdf, docx, csv, and other common formats.
Select vector models adapted to the home appliance industry. Match models to the semantic characteristics of specific sub-categories.
Set differentiated synchronization triggers. Sync third-party monitoring data monthly. Trigger securities firm research report updates based on industry events.
Configure field extraction rules. Accurately extract core category-related fields. Avoid irrelevant content disrupting retrieval.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_SUPPORTED_TYPES`	`["pdf", "docx", "csv", "xlsx"]`	Data for this category includes PDF research reports, Word-format brand white papers, and Excel-format retail monitoring data, covering commonly used document parsing types
`embedding_model`	`bge-large-zh-v1.5`	Research reports for this category contain professional semantics for specific home appliance sub-categories. This model is adapted to Chinese long text and can accurately capture semantic associations within the domain
`rerank_model`	`bge-reranker-large`	Research reports for this category have multiple content dimensions. The reranking model can filter low-relevance recall results and improve retrieval accuracy
`chunk_size`	`800–1200 characters`	The core content of individual sections in white appliance research reports mostly falls within this range. Avoiding overly fragmented chunks that damage semantic integrity, or overly long chunks that cause context overflow
`RECALL_TOP_N`	`Top 10 results`	Research reports for this category have many sub-dimensions. Sufficient candidate results must be recalled before filtering via reranking, to avoid missing relevant content
`SYNC_INTERVAL_HOURS`	`24 hours`	Third-party monitoring data is updated on a scheduled cycle. Daily synchronization ensures data timeliness while avoiding frequent requests that exceed interface limits

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

Phenomenon: An error occurs when calling the reranker model after offline access. The interface returns rerank error fastgpt | { message: { detail: ''. Cause: The interface address and access key for the rerank_model are not configured. Or the port of the offline deployed reranking model is not open to the network where the platform resides. This prevents the platform from calling the reranking service normally.
Phenomenon: When using the bge-m3 vector model, semantic retrieval returns abnormally high similarity scores. Cause: The output results of the vector model are not normalized. Or the configured similarity threshold does not align with the output score range of this model. This causes the retrieval result filtering logic to fail.
Phenomenon: An error returns when calling the deployed vector model interface via curl. Connectivity tests work normally via oneapi. Cause: The full path of API_PATH is not correctly set in the FastGPT model configuration. Or correct authentication parameters are missing from the request header. This prevents the model service from recognizing the curl request.

How to Confirm Proper Configuration

Run a model connectivity test. Check that the embedding model and reranking model return status code 200. Confirm the configured keys and addresses are valid.
Upload a sample white appliance research report. Verify that parsed chunked content covers core data fields. Confirm segmentation and parsing rules match the category’s data characteristics.
Enter a search term for a specific home appliance sub-category. Check that the number of recall results matches the RECALL_TOP_N configuration. Confirm the recall logic is active.
Trigger a data synchronization task. Review synchronization logs for format parsing failures or interface call errors. Confirm synchronization rule configuration is correct.

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.