What the Data for This Category Looks Like
Condiment research report data mainly comes from public securities firm industry research reports, industry dynamic documents released by the China Condiment Association, public annual reports of leading enterprises, and meeting minutes of performance briefings. Update frequency adjusts based on industry events. Update rates increase during new product launches, raw material price fluctuations, and earnings disclosure periods. Most documents combine structured and semi-structured formats, with fields including single-category output, revenue scale, channel share, and more. Output is measured in thousand tons, revenue scale in ten thousand yuan, and channel share as relative percentage. Some documents also include segmented data on enterprise regional market distribution.
Constraints Imposed on Model Connection and Configuration
The diversity of data sources and mixed structure of condiment research reports require parsing and adaptation rules for multi-source data access, to adapt to extraction logic for structured fields and semi-structured text. The uncertain update rhythm requires dual modes of scheduled synchronization and manual trigger synchronization, to meet fast update demands for sudden industry events. Unit differences in single-category quantitative fields require standardized mapping rules for numeric fields, to avoid unit conversion errors. The existence of regional market segmented data requires recall filtering rules based on regional dimensions, to ensure retrieval results match user-specified regional scenarios. Additionally, research report document lengths vary widely, so adaptive adjustment parameters for segment length need to be configured, to prevent information loss from long text truncation.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some condiment research report documents have long length, so sufficient time must be reserved for parsing and text extraction |
maxContext | 800–1200 characters | Core data of condiment research reports is concentrated in medium and short paragraphs. Excessively long context will introduce irrelevant cross-industry information |
Recall Count | Top 8 entries | Valid research report information for segmented categories is concentrated. Excessive recall will dilute the relevance of retrieval results |
Similarity Threshold | 0.75–0.85 | Low-relevance cross-industry research reports must be filtered out, to retain content directly related to the condiment category |
Custom Request URL | Fill in the dedicated access address provided by the model service provider | Adapt to deployment requirements of domestic compliance nodes for some models, to avoid cross-region call restrictions |
PROXY_URL | Calibrate based on actual testing | Adapt to proxy configuration for internal enterprise network environments, to ensure normal model call links |
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 targeted analysis. Testing on available local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Confusion between the fill content of
Custom Request URLandPROXY_URLduring model configuration results in model call failure, with connection timeout or 403 status code returned. Cause:Custom Request URLis the dedicated access endpoint provided by the model service provider, whilePROXY_URLis the transit proxy address for network requests. The two have completely different functions and application scenarios. - Phenomenon: During workflow testing, the AI reply always carries the phrase "FastGPT is a knowledge base question answering system based on large language models (LLM)" at the end, which does not match the expected output scope. Cause: The default self-introduction configuration is not disabled in the system prompt, or additional content in the reply is not clearly restricted.
- Phenomenon: Setting
Recall Countto 15 entries or more during configuration results in a large number of cross-industry research reports being mixed into retrieval results, with redundant information returned after user queries. Cause: The recall parameter is not adjusted based on the information concentration of condiment research reports. Excessively high recall counts will introduce content unrelated to the target category.
How to Confirm Successful Configuration
- A single condiment research report is submitted for parsing testing. Parsed fields are checked to confirm inclusion of preset category-related content, and unit conversion aligns with configuration rules.
- A retrieval request for a single condiment category is initiated. Returned result count is verified to match the configured recall count, and similarity meets threshold requirements.
- Model call logs are reviewed. Configuration of
Custom Request URLandPROXY_URLis confirmed to match the actual call link, with no network errors present. - The system prompt is adjusted, and a test conversation is initiated. The reply is checked to confirm absence of preset self-introduction text, meeting output requirements.
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.