What data for this category looks like
Sources of seasoning industry research reports include public securities firm food and beverage industry research reports, specialized seasoning industry research reports, and regular operating announcements from leading seasoning enterprises. Update cadence primarily follows quarterly deep research reports and monthly tracking reports. Temporary analysis documents are produced for sudden industry events such as raw material price fluctuations.
Documents typically include sections on industry macro environment, supply and demand data for segmented categories, operating indicators of key enterprises, price trends, and investment advice. Included fields cover publishing institution, publishing date, monthly shipment volume of core enterprises, ex-factory price per product, and channel structure related data. Units for related indicators include tons, yuan per kilogram, and similar units.
What constraints these characteristics impose on multi-turn conversation and prompt engineering
The multi-source nature of seasoning research reports requires multi-turn conversations to support users in gradually narrowing down report source scope, avoiding retrieval of irrelevant enterprise announcements or broad industry reports.
Documents with varying update cadences require the conversation module to support configurable rules for filtering by publication time range, prioritizing retrieval of content within the user-specified period.
Complex document structures and diverse field units require prompt engineering to clearly specify the format for extracting target fields, and guide users to supplement unit information during multi-turn interactions to prevent mixed units in returned data.
Some research reports include targeted segmented category analysis, so conversations must support users specifying specific seasoning categories to narrow retrieval scope.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Seasoning industry research reports have long individual content, requiring a sufficient context window to accommodate retrieved report fragments and conversation history |
Recall count | Top 10-15 entries | Seasoning industry research reports have rich fields; excessive retrieval will cause context overflow and reduce answer accuracy |
similarityThreshold | 0.75-0.85 | Balance the relevance and coverage of research report retrieval, avoiding retrieval of irrelevant broad industry reports |
Time Range Filter | Last 3-6 months | The effective reference period for tracking reports in the seasoning industry is 3 to 6 months; expired reports have low reference value |
promptTemplate | First clarify the required seasoning segmented category, research report time range, and specific data indicators from the user, then gradually answer based on retrieved research report content. Returned data must indicate the publishing institution and date of the source research report | Guide the interaction logic of multi-turn conversations, clarify the answer format and information sources |
parseChunkSize | 1000-1500 characters | Adapt to the chapter structure of research reports; overly long segments reduce retrieval accuracy, while overly short segments cause context fragmentation |
The parameter values provided on this page are common 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.
Three Common Mistakes
- Calling the conversation API returns an
unAuthChaterror, even though the apikey is correctly configured. Cause: The parameter in the formatAuthorization: Bearer ${apikey}was not correctly carried in the request header, or the application corresponding to the apikey is not bound to the seasoning industry research report retrieval knowledge base. - The conversation returned content contains unescaped newline characters, causing downstream JSON request failures. Cause: The prompt template did not require returned content to use escape characters for newline processing, or unified escape processing was not performed on returned content before API calling.
- System-side parameters were not correctly passed in the workflow, causing global variables to be unrecognized. Cause: The output mapping of system parameters was not configured in the workflow's trigger node, or parameters were not bound to corresponding fields of global variables.
How to Verify Successful Configuration
- Initiate a test conversation, input a query that includes specific seasoning categories and time ranges, check if retrieved research reports match preset filtering conditions, and verify if the number of returned results matches the configured retrieval count.
- View the total length of the conversation context, confirm that retrieved research report fragments and historical conversation content do not exceed the configured context window limit.
- Verify the content format returned by the API, confirm that newline characters have been correctly processed, or that the prompt template has clearly required returning content compliant with JSON specifications.
- Trigger a workflow test, confirm that system-side parameters can be correctly passed and recognized by global variables, and check if the workflow output includes expected conversation generation content.
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.