What the data for this category looks like
Home goods investment research data primarily comes from public reports released by industry associations, regular financial reports of listed home goods enterprises, offline supermarket terminal sales monitoring data, SKU details and sales data from mainstream e-commerce platforms, and shipment records of supply chain manufacturers.
Update cycles vary significantly. Industry reports are updated quarterly or semi-annually. Listed company financial reports are disclosed annually and quarterly. E-commerce sales data is updated daily. Supply chain shipment records are mostly updated weekly.
Document structures include structured price fluctuation tables and SKU parameter tables, semi-structured competitive product benchmarking documents, and unstructured product review content. Core fields include SKU code, material, retail price, wholesale price, and monthly sales volume. Common units are yuan per piece, ten thousand units, and tons.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources and diverse formats require multi-turn dialogue to set differentiated recall weights for different data source types, to avoid information conflicts across documents of different formats.
Varying update cycles require prompt engineering to explicitly specify the time range for recalled documents. For example, when asking about short-term price trends, prioritize recalling e-commerce data from the past 30 days. When asking about annual production capacity analysis, recall annual financial reports.
The large number of SKUs and rich field details require prompt engineering to explicitly limit the extracted fields and units, to prevent the model from confusing the numerical differences between retail and wholesale prices.
A high proportion of long documents requires multi-turn dialogue to reasonably control the context length, to avoid dilution of key information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Home goods investment research documents often contain multiple sets of SKU data and competitive product comparison content. This range covers key paragraphs of a single core report, and adapts to the inference load of the qwen2.5 series quantized models |
RECALL_TOP_N | Top 10–15 entries | Investment research documents for the home goods category are scattered across multiple data sources. A sufficient number of entries must be recalled to cover three core types of investment research data: SKU, price, and supply chain |
SIMILARITY_THRESHOLD | 0.72–0.78 | Product descriptions for home goods have high similarity. For example, storage boxes made of the same material have small parameter differences. This threshold avoids mixing irrelevant documents while covering information for detailed SKUs |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Home goods supply chain documents often contain large amounts of table and image parsing content. A longer parsing timeout is required to complete full loading |
WORKFLOW_OUTPUT_LAST_NODE | Enabled | In the investment research workflow of multiple AI dialogue modules, only the conclusive output of the last node needs to be retained, to avoid redundant intermediate results |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Truncated dialogue responses. The cause is an overly small
maxContextsetting, which cannot accommodate all recalled documents and multi-turn dialogue history. This truncation issue is exacerbated when using quantized large models such as qwen2.5-14b-int4, as long contexts further strain available resources. - 500 status code returned when importing documents. The cause is an overly short
PARSE_FILE_TIMEOUT_SECONDSsetting. Home goods supply chain documents with multiple tables take longer to parse, and a timeout is triggered before loading completes. - Workflow output includes results from all AI dialogue nodes. The cause is that the
WORKFLOW_OUTPUT_LAST_NODEconfiguration is not enabled, causing the system to default to outputting execution content from all nodes.
How to confirm proper configuration
- Upload a single home goods financial report document containing more than 3 SKUs, and check if the number of recalled documents matches the set range of
RECALL_TOP_N. - Initiate an investment research query involving multi-round price comparisons and SKU parameter inquiries, and verify that the returned results include the specified fields and corresponding units.
- Run an investment research workflow with multiple AI dialogue nodes, and confirm that only the output of the last node is retained.
- Import a single home goods supply chain document with more than 10 pages, and confirm that no timeout errors occur during parsing.
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.