What the data for this category looks like
Home goods research report data primarily comes from securities firm light manufacturing industry research reports, monthly monitoring data from the China Home Products Association, public financial reports and new product launch announcements from leading home goods brands. Update cycles fall into two categories: scheduled and ad-hoc. Scheduled reports follow monthly or quarterly cycles, while ad-hoc reports are released in real time alongside industry policy adjustments, new product launches, or supply chain fluctuations.
Document structures include overall industry overview, market performance of segmented categories such as soft furniture, home textiles, and kitchen appliances, supply chain cost analysis, competitor dynamics, and policy impact interpretations. Fields cover individual product selling prices, monthly shipment volumes, online channel share, raw material cost share, and more. Statistical dimensions vary slightly across different segmented categories.
What constraints these characteristics impose on model access and configuration
The multi-dimensional fields and mixed unit requirements of home goods research reports mean models must have structured data parsing capabilities. Parsing parameters adapted for table and multi-unit recognition need to be configured. Differences in update cycles require configuration items that support incremental synchronization and scheduled full updates. This prevents repeated imports of old data or missed emergency reports released temporarily.
The diversity of segmented categories requires precise keyword matching during the recall phase. This prevents irrelevant research report content from cross categories from being included in the context. It also requires limiting the per-round context length to fit the model’s input upper limit, preventing call failures caused by overly long text.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_ENABLED | Enabled | Home goods research reports contain large amounts of structured tabular data such as unit prices and shipment volumes. Table parsing must be enabled to extract fields that can be directly used for question answering |
maxContext | 8000–12000 characters | Single in-depth home goods research reports have long text lengths. Long context support is needed to fully retain core information |
RECALL_TOP_K | Top 6–8 entries | There are many segmented categories of home goods. A sufficient number of relevant fragments must be recalled to cover query needs across different segmented scenarios |
SIMILARITY_THRESHOLD | 0.75–0.85 | Keyword recognition for segmented categories in home goods research reports is relatively distinct. A reasonable threshold must be set to filter irrelevant recall results |
UPLOAD_FILE_MAX_SIZE | 500 MB | PDF or document files for single in-depth research reports have large sizes. The upload limit must be relaxed to support complete document imports |
PROMPT_TEMPLATE | Use the format "Please answer the question based on the following home goods research report data: {context}, user question: {question}" | The model must be explicitly instructed to reference home category data to avoid confusion with research report content from other industries |
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
- Symptom: An error
chat:ai_input_is_eis returned when calling the model. Cause: Structured research report results generated by code execution are not correctly spliced into the model input context, resulting in non-compliant input format. - Symptom: Ollama models deployed locally without Docker cannot be accessed by FastGPT instances deployed via Docker Compose. Cause: Correct network access rules are not configured, and the default port of the Ollama service is not opened for FastGPT calls.
- Symptom: The model does not output customized answers as expected after a prompt is added. Cause: The prompt template does not correctly bind the
{context}and{question}placeholders, so the model cannot retrieve research report data. - Symptom: A "required field missing" message is displayed after submitting the model configuration. Cause: The
API_BASE_URLandMODEL_NAMEparameters are not filled out as required by the interface, violating FastGPT's required model access configuration rules.
How to confirm the configuration is complete
- Upload a home goods research report document, check if the parsing result correctly extracts fields such as unit prices and shipment volumes from tables, to verify that the table parsing configuration takes effect.
- Enter a test query, check if the number of recall results matches the range set by
RECALL_TOP_K, to verify that the recall parameter configuration is correct. - Initiate a model call test, check if the returned result references the uploaded research report content, to verify that the prompt template binding logic is correct.
- View the FastGPT model connection status logs, confirm there are no errors such as
invalid API_KEYorconnection timeout, to verify that network and parameter configurations are 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.