What this category of data looks like
The data for this category comes primarily from customer product models, mold flow or forming simulation files, CAD/CAM output files, machining and measurement records, and sample part inspection data. Daily documents include mold design task sheets, DFM reports, part drawings, machining process cards, mold trial reports, mold modification notices, and mold history records. Data is updated per individual project. New or modified content is synchronized after each mold trial or modification. Fields include mold number, mold trial batch, defect type, machining parameters, and others. There is no unified fixed format, and it is adjusted based on the project.
What constraints these characteristics impose on model access and configuration
The multi-source, heterogeneous data format of this category requires the model access link to support parsing CAD-exported structured files, simulation report text data, and measurement record numerical data. It must also adapt to non-standardized document fields. Following the update rhythm of projects, mold trials, and mold modifications, the configuration must support event-triggered data synchronization. It must pull the latest mold trial and modification records on demand. Mold history records include multiple rounds of modification entries. Long context processing must adapt to accumulated project data volume to avoid exceeding model context limits. It must also support custom field combinations for different projects.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Mold project data includes accumulated content from multiple documents. This range covers the complete ledger data for a single project |
maxTokens | 2000–3000 token | Generating production ledger reports requires outputting structured summary content. This range balances output length and response speed |
quoteMaxToken | 1500 token | Core document fragments such as mold trial reports and mold modification notices need to be referenced. This value retains key referenced content |
temperature | 0.3–0.5 | Ledger summaries must maintain data accuracy. A lower temperature reduces the generation of irrelevant content |
AIPROXY_API_ENDPOINT | Proxy address deployed on the enterprise intranet | Mold enterprise data must be transferred within the intranet to avoid external leakage. Set based on actual testing |
AIPROXY_API_TOKEN | Exclusive key bound to the enterprise proxy | Ensures interface call permissions. Only authorized personnel can configure this. Set based on actual testing |
Three common mistakes
- Phenomenon: The model returns the
context length exceedederror, and logs show the context limit has been exceeded. Cause: ThemaxContextparameter was not adjusted based on the accumulated document volume of the mold project, and the general scenario default value was used. - Phenomenon: All configuration items are lost after a Docker container restarts, and the application cannot call the model normally. Cause: The configuration file was not mounted to the host machine's persistent directory, and the container data volume was not correctly bound.
- Phenomenon: The DeepSeek series models return an invalid response, and the ledger report cannot be generated. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwere not correctly configured, or the proxy did not adapt to the target model's interface format.
How to confirm the configuration is complete
- View the model access configuration page in the FastGPT v4.17.0 backend, and confirm that the values of parameters such as
maxContextandmaxTokensmatch the preset values. - Upload a single mold trial report, trigger the ledger summary task, and check if the output includes core fields such as mold trial times and defect types.
- Restart the Docker container, revisit the configuration page, and confirm that all parameters have not been cleared. Run an interface call test to verify a normal response is returned.
- Call the proxy interface, initiate a test request using
AIPROXY_API_TOKEN, and confirm the return status code is 200 and contains valid data.
The values provided here are common starting points and should be evaluated against the target deployment's own sample data.