What the data for this category looks like
Data sources for condiment industry research reports include public broker research reports, public industry association reports, and annual and semi-annual disclosure documents from leading condiment enterprises. Updates are frequent, aligned with enterprise earnings report release cycles. Temporary additions are made for major industry events such as raw material price fluctuations and new product launches. Single documents typically include industry overall market data, revenue and production capacity data of leading enterprises in segmented categories, channel structure analysis, policy impact interpretation, and risk warning modules. Core fields include enterprise name, revenue scale, production capacity, channel share, and raw material cost share. Units are RMB, ten thousand tons, and percentage respectively.
Constraints on vector models and indexing
The multi-source, dispersed nature of condiment research report data requires vector models and indexing pipelines to support mixed structured and unstructured content parsed from multi-format documents. The fluctuating update cadence tied to earnings reports and industry events requires indexes to support incremental updates. This avoids resource consumption from full index reconstruction. Multi-dimensional fields in documents such as segmented categories, enterprise revenue, and production capacity require vector models to capture associations between numerical expressions and context. Indexes must also perform initial grouping by enterprise or segmented category to narrow recall scope. Large variations in single document length are common, with some reports containing extensive table content. Chunking logic must adapt to structured text within tables to avoid breaking field associations during long-text splitting.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large or bge-large-zh-v1.5 | Supports long-text encoding, captures associations between multi-dimensional fields in research reports, and adapts to the mixed content structure of condiment industry research reports |
| Chunk Length | 800–1200 characters | Condiment research reports include structured tables and long-text analysis. This range retains context for core data such as revenue and production capacity, and avoids breaking field associations during chunking |
| Chunk Overlap Rate | 10–15% | Segmented categories and enterprise names in research reports often span chunks. Setting an overlap ensures core entities are fully covered |
| Recall Count | Top 10–15 results | Condiment segmented tracks are relatively concentrated. Excessive recall introduces irrelevant data. This range covers core competitive landscape and revenue analysis content |
| Reranked Return Count | Top 3–5 results | User retrieval needs mostly focus on specific enterprises or categories. Streamlining reranked results improves response speed and adapts to the precision requirements of research report retrieval |
| Incremental Index Toggle | Enabled | Condiment research report updates have no fixed cycle. Incremental indexing avoids resource consumption from full reconstruction, and adapts to the update cadence of temporary additions |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Retrieval experiences long wait times during the reranking stage, the interface returns a request timeout prompt, and the console logs an
ETIMEDOUTerror. Cause: Chunk length set outside the reasonable range, or recall count configured too high. This causes the vector retrieval and reranking stages to load excessive irrelevant documents, increasing system load. - Symptom: Retrieval results include research reports from non-condiment segmented categories such as soy sauce and oyster sauce, with core fields such as revenue scale empty. Cause: Index grouping not configured by enterprise or segmented category. This leads to recall scope covering irrelevant industry research reports, and chunking breaks the association of structured fields.
- Symptom: After deploying FastGPT v4.8.21-fix via Docker, the indexing service fails to start, and logs show a
Failed to load embedding modelerror. Cause: The correct model name for integration with oneapi is not specified in the configuration items, or the API key does not have access permissions for the corresponding model.
How to confirm correct configuration
- Upload a single condiment industry research report document, review the parsed chunk results, and confirm that core business fields are not split across chunks.
- Submit a retrieval request targeting a specific condiment category or enterprise, verify that the number of vector recall and reranked results matches the configured values.
- Manually trigger an incremental index update, check system logs, and confirm that only newly added documents participate in index construction, and no full reconstruction is triggered.
- Call the bound vector model interface, input a fragment of research report text, and confirm that corresponding vector encoding results can be generated normally.
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.