What Data for This Category Looks Like
Data sources for snack food investment research include category trend reports released by the China Food Industry Association, quarterly financial reports publicly disclosed by leading brands, POS sales data from offline chain supermarkets, product details and user reviews from mainstream e-commerce platforms, and spot price information for upstream grain, oil and sugar raw materials.
Update cycles vary. Industry reports are updated quarterly. E-commerce and sales data is updated daily. Brand financial reports are released quarterly.
Documents often include SKU codes, individual product selling prices, raw material cost ratios, online channel ratios, and consumer age group fields. Units include category-specific units such as grams, yuan per kilogram, and pieces.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Differences in data update cycles cause inconsistent timeliness of knowledge recalled during multi-turn dialogue. Prompts must clearly define data time ranges to avoid cross-cycle information confusion.
The large number of SKUs and detailed fields require multi-turn dialogue to track specific SKUs that users focus on. This prevents query bias caused by lost context.
Closely related field attributes require prompts to predefine logical relationships between fields. This reduces ambiguity in cross-field queries.
Unstructured user review data requires multi-turn dialogue to support clustering and summarization. Prompts must clearly define processing rules for unstructured text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–10000 characters | Snack food investment research requires tracking multi-SKU multi-turn queries. Sufficient context retains key information such as SKU numbers and channel data, preventing context loss |
similarity_threshold | 0.78–0.82 | Snack food SKU names are similar and have many field dimensions. A threshold that is too low will recall irrelevant SKU data. A threshold that is too high will miss valid investment research information |
RECALL_TOP_N | Top 6–8 entries | Investment research data covers multiple categories including raw material costs, channel ratios, and consumer profiles. Recalling 6-8 entries covers core investment research needs |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Some large supermarket sales data documents have long lengths. 300 seconds allows complete parsing and avoids parsing interruptions |
CHAT_DEFAULT_STREAM | Enabled by default | Investment research dialogue needs to display multi-dimensional data step-by-step. Streaming output improves interaction fluency and aligns with information acquisition habits in investment research scenarios |
CUSTOM_QUESTION_TRIGGER | Enabled and configure preset questions | Quickly trigger investment research workflows. This meets user needs for quick queries of specific SKUs or data. Preset questions can cover common investment research scenarios |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Symptom: After adjusting the number of displayed chat records, multi-turn dialogue still fails to retain context information for SKU tracking. Cause: Only the number of displayed chat records was adjusted. The
maxContextparameter was not configured to match the context needs of multi-SKU tracking. This causes recalled knowledge to not be associated with the current conversation context. - Symptom: Non-logged-in chat windows return results without streaming loading effects. Cause: The
CHAT_DEFAULT_STREAMconfiguration item was not enabled. Non-streaming output is used by default, which does not meet the need for step-by-step information display in investment research scenarios. - Symptom: Clicking a custom preset question fails to trigger the corresponding investment research workflow. Cause: The trigger rules for
CUSTOM_QUESTION_TRIGGERwere not configured, or preset questions were not bound to labels in the investment research knowledge base. This prevents association with corresponding knowledge recall.
How to Verify Successful Configuration
- Initiate consecutive queries that include multiple SKU numbers. Check whether the conversation interface retains SKU information from previous queries to confirm normal context association.
- Open a non-logged-in chat window. Observe the result return format to confirm whether streaming output is enabled.
- Click a preset custom question. Check whether it directly jumps to the corresponding conversation and loads relevant investment research data.
- Upload a large sales data document. Check whether the parsing progress completes within the preset timeout period to confirm valid parsing configuration.
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.