What the data for this category looks like
Data for snack food smart due diligence reports comes primarily from publicly available industry materials from the China Food Industry Association, compliance disclosure information from the State Administration for Market Regulation, supermarket retail monitoring APIs, and public annual reports of listed snack food enterprises. Update cycles follow three schedules: quarterly, monthly, and annual. Industry association reports update quarterly. Retail terminal monitoring data updates monthly. Corporate annual reports update annually. Supply chain purchase ledgers synchronize updates with each batch procurement cycle. Documents combine structured tables and auxiliary text, and include fields such as production entity name, raw material category, purchase unit price (yuan/kg), sales channel proportion weight, and list of compliance inspection items. No unified fixed format exists. Some enterprises attach offline dealer directories.
What constraints these characteristics impose on vector models and indexing
Snack food due diligence report data comes from multiple public channels and structured ledgers. Vector models must support multi-format parsing, especially structured extraction of table fields, to avoid bias from unstructured text embedding. Update frequencies vary across data sources. Retail monitoring data updates monthly, while industry reports update quarterly. This requires support for incremental indexing mechanisms to avoid resource consumption from full reindexing. Field units and types mix different measurement dimensions, such as unit price and sales volume. Field standardization processing must complete before indexing to ensure dimensional consistency of embedded vectors. Some documents include long list text, such as dealer directories. A segmentation strategy must adapt to avoid exceeding the model's context window. Data such as lists of compliance inspection items must support vectorization of list fields, rather than relying solely on full text embedding.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Snack food due diligence reports contain short fields and long-text dealer directories. This range balances field integrity and contextual coherence |
chunk_overlap | 50–100 characters | Prevents core fields from being truncated after segmentation. The overlapping portion retains contextual connections across segments |
embedding_model | qwen3-embedding-8b or locally deployed m3e series models | Adapts to Chinese professional domain text, and supports vectorization of structured fields and multi-format documents |
retrieve_top_k | Top 6–10 results | The volume of associated data for snack food due diligence reports is moderate. This range balances recall coverage and information purity |
similarity_threshold | 0.72–0.80 | Distinguishes similarity differences between compliant and non-compliant items, and avoids false recall of low-relevance data |
enable_incremental_index | Enabled | Adapts to scenarios with different update frequencies across multiple data sources, reducing computational resource consumption from full index reconstruction |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. Testing on independent samples is recommended before finalizing settings.
Three common mistakes
- Issue: In FastGPT v4.9.11, after configuring
qwen3-embedding-8b, the file indexing status continuously displays "Indexing" with no progress updates. Cause: The model's API call key or local deployment port mapping is not configured correctly, resulting in failed embedding requests. - Issue: After adding a
qwen3-embedding-8bmodel configuration, submitting a model configuration with the same name again overwrites the existing settings, and multi-instance calls cannot be retained. Cause: The system uses the model name as the unique identifier for the embedding model, and does not support aliases to distinguish different deployment nodes. - Issue: Structured field information such as raw material purchase unit price is missing from recall results. Cause: The
chunk_sizesetting is too small, causing fields in table documents to be truncated during segmentation, making it impossible to fully extract structured data.
How to confirm the configuration is complete
- Navigate to the FastGPT system model configuration page, verify that the configuration name of
embedding_modelmatches the deployed model, and check that the API key or local port configuration is correct. - Upload a test snack food due diligence report document, view the segmented content generated after indexing is complete, and confirm that core business fields are not truncated during segmentation.
- Initiate a retrieval request for this type of document, verify that the number of recall results matches the
retrieve_top_ksetting, and adjust the similarity threshold to adapt to business screening requirements. - View system operation logs, confirm that there are no error records for embedding request failures, index timeouts, or model call failures.
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.