Model Access and Configuration for Home Goods Investment

What the data for this category looks like

Home goods investment research data sources include upstream raw material supplier quotation documents, offline supermarket sales performance reports, brand SKU update announcements, industry association category reports, and real-time e-commerce platform sales data. Update rhythms vary: raw material quotes are updated weekly, offline sales data is updated daily, brand SKU updates have no fixed cycle, and industry reports are released monthly. Document structures include structured parameter tables, such as SKU codes, fabric weight, and terminal selling prices, as well as unstructured new product launch meeting minutes and consumer survey content. Most fields have clear units: fabric weight is measured in g/㎡, and selling price is measured in yuan per item.

Constraints imposed by these characteristics on model access and configuration

Multi-source heterogeneous data structures require model access to support multi-format parsing, and adapt to data sources with different update frequencies. This avoids interfering with investment research results from documents with poor real-time performance. Most home goods fields include units and have unit differences, such as g/㎡ and ounces per square yard for fabric weight. A unified unit standardization logic must be configured to prevent retrieval failure due to unit mismatches. The number of SKUs is large and updates occur frequently. The knowledge base's incremental update configuration must adapt to high-frequency, small-batch updates. During segmented parsing, the association between SKUs and their corresponding parameters must be retained to prevent context breaks from affecting model inference.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Product parameter documents and sales performance reports for home goods have moderate information density per segment. Splitting them this way retains the association between SKUs and their corresponding parameters, avoiding context breaks
`similarity_threshold`	0.72–0.78	Material and functional parameters of home SKUs have high similarity. A threshold that is too low will introduce irrelevant competitor data, while a threshold that is too high will fail to recall similar category alternative items
`recall_top_k`	Top 6–8 results	Investment research analysis requires balancing single-item details and industry trends. Too many recalled results will cause context redundancy and reduce model inference efficiency
`parse_file_timeout`	300 seconds	Parsing large brand product manuals and industry standard documents takes a long time. This setting prevents parsing failures due to timeouts
`unit_conversion_enabled`	Enabled	Home goods data includes multiple units, such as g/㎡ and ounces per square yard for fabric weight. Automatic standardization unifies the matching dimensions for vector retrieval
`incremental_update_interval`	1 hour	Offline sales data is updated daily and raw material quotes are updated weekly. High-frequency incremental updates meet real-time investment research requirements

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

When calling a model deployed via a third-party API, the content within the returned <think> tag is not correctly recognized, resulting in redundant tag text in model outputs. Cause: The correct wrapping tags are not configured in reasoning_tag_config, so the model's thinking content is not properly stripped.
After selecting a home-specialized model deployed via ollama, the preset investment research guidance prompts do not take effect, and model outputs do not follow specified format requirements. Cause: The corresponding category guidance prompts are not bound in the model_system_prompt configuration item, or the configured guidance prompts do not match the model's role trigger logic.
When parsing PDF tables of home goods products, errors occur with empty fields or missing units. Cause: unit_conversion_enabled is not enabled, and structured table parsing mode is not activated.

How to Confirm the Configuration Is Complete

Upload a structured quotation sheet for home SKUs. Check if the parsed fields are complete and units are unified. Verify that field mappings in the parse_result log are correct.
Initiate an investment research query, such as querying the fabric parameters of current mainstream fabric sofas. Check if the recalled documents include the latest data for the corresponding category, and verify that the update time of the retrieval results matches the configured update interval requirements.
Test the preset investment research guidance prompts, such as requiring the model to output sales analysis categorized by SKU. Check if the model output follows the format requirements of the guidance prompts, and verify that the system prompt in the chat_history is correctly loaded.
Simulate an incremental update, upload new SKU data. Check if the knowledge base update status is normal, and verify that the task execution status in the update_log is successful.

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.