What the data for this category looks like
This category's data mainly comes from manufacturing enterprise purchase orders, drawings and specifications, supplier quality certificates, inspection equipment, historical quality data, and supplier portals. Daily processed documents include inspection specifications, sampling plans, incoming material reports, supplier audit reports, exception contact sheets, 8D reports, concession acceptance approval forms, etc. Documents contain fields such as batch identification, inspection parameters, supplier qualifications, and quality judgment results. Units match the document type: for example, inspection reports use pieces and batches as units, while quality certificates use specification models as identifiers. Data update frequency varies by business scenario: new inspection data is generated for each batch of incoming materials, supplier quality evaluations are conducted monthly or quarterly, and special audits are carried out irregularly.
What constraints do these characteristics impose on model access and configuration
The data characteristics of this category impose multi-dimensional constraints on model access and configuration. First, multi-source and scattered data sources require configuring reading rules adapted to different data sources, and support for synchronizing data from different channels such as procurement systems and supplier portals. Second, the high-frequency and dynamic update rhythm requires configuring trigger-based knowledge base updates to adapt to real-time data update requirements for each batch of incoming materials and irregular audits. Diverse document structures require the model's context processing to adapt to different formats of quality documents, and accurately identify key identifiers such as batches and suppliers. Finally, bid document and quotation preparation must strictly associate historical quality data, requiring the model to accurately match batch and supplier information during retrieval to avoid content deviations caused by generalized retrieval.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Incoming material inspection-related documents contain multi-batch data and associated information, requiring sufficient context to match batch and supplier identifiers |
maxTokens | 2000–3000 token | Bid documents and quotation preparation require complete quality basis descriptions to be output, avoiding truncation of key content |
quoteMaxToken | 1500 token | Referenced content such as inspection reports and 8D reports is relatively long, limiting referenced tokens to avoid context overflow |
| Workflow/API tool selection | auto | Automatically call the knowledge base based on retrieved quality data, adapting to dynamic incoming material inspection and supplier information |
temperature | 0.3–0.5 | Ensure consistency between quotations and inspection data, avoiding generating content that deviates from reality |
AIPROXY_API_ENDPOINT | Fill in the proxy address deployed within the enterprise | Used to connect to external model services, adapting to enterprise intranet access restrictions |
Three common mistakes
- Phenomenon: The model generates content without calling the knowledge base, directly outputting generic descriptions. Cause: The
toolChoiceparameter is incorrectly set to a fixed mode, failing to trigger knowledge base retrieval, causing the model to skip data calls. - Phenomenon: The metadata panel fails to load table models of various databases, displaying empty or throwing errors. Cause: The metadata reading permission of the data source is not configured, or the metadata viewing switch of the knowledge base is not enabled.
- Phenomenon: Loading the configuration page throws an error in a specific browser, prompting that the browser version is incompatible. Cause: A browser version supported by FastGPT v4.17.0 is not used, and compatible adaptation is not completed.
How to confirm the configuration is complete
- Enter a query containing a specific incoming material batch and supplier name, check whether the returned result is associated with the inspection document of the corresponding batch and the supplier's historical quality data.
- View the metadata panel of the knowledge base, confirm that the table structure information of each data source can be loaded.
- Initiate a model call test, confirm that the interface returns no connection exceptions and that knowledge base retrieval is triggered.
- Adjust the
temperatureparameter, observe the change in the rigor of the generated content, and confirm that the parameter configuration takes effect.
The values given are common starting points and should be measured against the reader's own samples.