What the Data for This Category Looks Like
Small home appliance research report data primarily comes from public industry databases, official brand disclosure documents, and third-party consumer testing agency reports. Update frequency fluctuates with new product launch seasons, quarterly earnings reporting periods, and energy efficiency policy adjustments. Most documents are in PDF format, containing structured parameter tables, measured performance data, and market analysis summaries. Core fields include rated power (unit: W), standby power consumption (W), noise level (dB(A)), external dimensions (mm), and price range (CNY). Some research reports include Excel-format attachments with detailed parameter breakdowns.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Small home appliance research reports contain numerous fine-grained parameters with clear units. Multi-turn dialogue must maintain unit consistency to avoid confusing the power and size units of different models across conversation turns. Most documents contain nested structured tables, so prompts must explicitly require prioritizing extraction of structured fields, avoid scattered descriptions, and prevent returning unrelated text fragments. The update frequency of research reports fluctuates greatly, so multi-turn dialogue must allow users to trigger knowledge base refresh commands, while prompts must limit retrieval to the latest version of research report data to avoid returning outdated parameter information.
How to Configure Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Small home appliance research reports contain numerous fine-grained parameters with units and context for multi-turn follow-up questions, so sufficient historical dialogue and retrieval snippets must be retained |
segment length | 1000–1500 characters | Structured table sections in small home appliance research reports are lengthy. Excessively long segments will reduce retrieval accuracy, while excessively short segments will destroy the integrity of parameter associations |
retrieval count | Top 6–8 results | Small home appliance research reports have multiple parameter dimensions, so enough relevant snippets must be retrieved to cover fields such as power, size, and energy efficiency |
similarity threshold | 0.72–0.80 | Avoid retrieving research reports for unrelated appliance categories, while retaining parameter comparison information for different models in the same category |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Small home appliance research reports often contain nested Excel attachment parsing, so sufficient parsing time is required to avoid timeout failures |
maxUploadFileSize | 500 MB | Some small home appliance research reports include multiple test report attachments, so a larger file upload capacity must be allowed |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling a locally deployed model during debug preview, the returned results show a non-local model identifier, or a model incompatibility error occurs. Cause: The local model's interface address and API key were not correctly bound in FastGPT's LLM configuration, causing the system to default to calling another model.
- Symptom: After uploading a small home appliance research report with numerous nested tables, the parsed text snippets are missing or truncated. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, or a reasonablesegment lengthwas not set, leading to long table parsing timeouts or truncation. - Symptom: When asking about the energy efficiency parameters of a specific small home appliance during multi-turn dialogue, the returned results include outdated legacy data. Cause: No automatic refresh rule was configured for the knowledge base, or the prompt did not limit retrieval to the latest version of research reports, resulting in retrieval of non-current valid data.
How to Verify Proper Configuration
- Navigate to the LLM configuration page, verify that the bound model interface address and API key match the locally deployed model, initiate a call in the debug preview interface, and check if the model identifier in the returned results matches expectations.
- Upload a small home appliance research report containing multiple nested test tables, and check if the parsed text snippets are complete, with no timeout or truncation error prompts.
- Initiate two rounds of dialogue: first query the core parameters of a specific small home appliance, then ask for the source document of that parameter, and verify that the returned results are linked to the correct research report snippet.
- Adjust the number of associated small home appliance research report knowledge bases, initiate multi-turn follow-up questions, and verify that the returned results cover relevant content from all associated knowledge bases with no obvious filtering omissions.
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.