What this category’s data looks like
Snack food investment research data primarily comes from public production and sales monitoring documents released by industry associations, supply chain quotation sheets disclosed by brand owners, desensitized terminal sales datasets from e-commerce platforms, and regular operating data from listed companies. Update cycles vary. E-commerce sales data updates daily. Raw material quotations update weekly. Industry research reports release monthly. Listed company financial reports update quarterly. Documents include structured dimension tables (such as SKU, sales channel, region), unstructured market analysis text, and semi-structured weekly and monthly report PDFs. Fields include SKU identifiers, region codes, sales unit prices, and shipment volumes, with units of pieces, yuan, and tons.
Constraints imposed on the context and token workflow
The multi-source nature and differing update cycles of snack food investment research data require a recall strategy that distinguishes between hot and cold contexts. High-frequency data should be prioritized for inclusion in the current context to avoid unnecessary token consumption. The mixed format of multi-dimensional structured data and long documents will cause single batches of uploaded or recalled content to use large numbers of tokens, exceeding the model’s context window. The large number of SKUs and fine-grained data characteristics can introduce large volumes of irrelevant entries if recall scope is not limited. This exacerbates token redundancy and slows query response speed. The wide variation in document length also requires reasonable segmentation of long texts. This prevents parsing failures caused by excessively high token usage per segment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContextToken | 16000–24000 token | Adapts to the multi-dimensional content of snack food investment research data, retains core production and sales, channel, and research report information, and avoids exceeding the model context window |
recallTopK | Top 8–12 entries | Filters redundant SKU data of the same category, balances recall coverage and token consumption |
chunkSize | 800–1000 characters | Adapts to mixed-structured investment research documents, avoids excessive token usage per segment, and retains data association logic |
rerankerTopN | Top 4–6 entries | Further screens highly relevant investment research information, reducing token usage during the reranking stage |
UPLOAD_FILE_MAX_SIZE | 500 MB | Limits single-file upload size to avoid excessive system resource and token usage during long document parsing |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing time of long documents, preventing data loss due to timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: The knowledge base returns truncated content, with only partial investment research conclusions included. Cause: The
maxContextTokenparameter is not configured. Recalled structured sales data and unstructured research report content exceed the model context window, leading to automatic truncation. - Phenomenon: Single investment research query cycles are overly long. Background logs show token usage exceeds the model’s preset threshold. Cause: The
recallTopKandrerankerTopNparameters are not set. Too many irrelevant SKU data entries are recalled, resulting in redundant context tokens. - Phenomenon: The Reranker model returns an ACCESS Token invalid error. Cause: The
ACCESS_TOKENparameter for model calls is not configured correctly, or the token has expired and was not updated.
How to Verify Proper Configuration
- Upload a single snack food industry research report and e-commerce sales details. Check the number of parsed segments and per-segment length to confirm alignment with the
chunkSizesetting logic. - Initiate a core investment research query. Review background token usage logs to confirm it does not exceed the model context window limit.
- Test a Reranker model call. Verify that the number of returned reranked results matches the
rerankerTopNconfiguration. - Test the voice input function. Check if a token verification failure error appears, confirming the
ACCESS_TOKENparameter is configured correctly.
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.