What data for this category includes
Data for this category originates from design documents, standard instrument and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. Daily generated documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version logs. Data is updated for each device following every calibration. Data fields cover device range, error calculation values, calibration dates, metrology certificate numbers, and more. Most units are standard physical quantity units, including pascals, degrees Celsius, percentages, and similar units.
Constraints imposed by these characteristics during model access and configuration
The high-frequency update nature of this category’s data requires model access configurations to support incremental synchronization and rapid index updates. The mixed structure of multiple document types, including numerical curves and certificate text, requires configurations to adapt to chunking and parsing logic for unstructured and semi-structured data. The status of calibration records as core deliverables requires configurations to strictly control the traceability accuracy and reference scope of recalled documents. Data sources include local calibration software and laboratory systems, requiring configurations to support connection parameter settings for locally deployed models, while adapting to format differences across various data sources.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category’s documents include long texts such as error curves and calibration records, requiring sufficient context to support cross-review logic |
quoteMaxToken | 2000–3000 token | Core deliverable documents such as calibration certificates and original records require full reference to avoid truncation of critical traceability information |
temperature | 0.1–0.3 | Technical document cross-review requires rigorous output, reducing random deviation |
AIPROXY_API_ENDPOINT | Fill in according to the locally deployed model path, for example http://localhost:11434/api | Adapt to the docking requirements of locally deployed large models, matching common local deployment solutions for this category |
AIPROXY_API_TOKEN | Leave blank or fill in the local model authentication token | Most locally deployed large models do not require additional authentication tokens, avoiding connection failures caused by configuration errors |
maxTokens | 4000–6000 token | Control the output length of single-round review results, adapting to the context window limits of large models |
Three Common Configuration Errors
- Symptom: Reordered results are not displayed during online recall testing, even though Rerank is enabled in configuration items and indexing is enabled. Cause: The reranking model is not associated in the application’s model access configuration, or the docking parameters for the reranking model are not filled correctly.
- Symptom: Context is not sent to the large model when
maxContextis set above 3000. Cause: The set value exceeds the default context window limit of FastGPT v4.17.0, or the maximum context actually supported by the configured large model is smaller than the set value. - Symptom: Request Time errors occur frequently during conversations. Cause: The timeout parameters set for the locally deployed large model or FastGPT service are too short, failing to adapt to the processing time required after recalling a large number of documents for this category.
How to Confirm Configurations Are Correctly Set
- Navigate to the model access configuration page in FastGPT, verify that the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches the actual parameters of the locally deployed model. - Initiate a test request for technical document cross-review, check whether the returned results reference core documents for this category such as calibration certificates and error curves.
- Check the application’s context preview module, confirm that the number of recalled documents matches the setting logic of
maxContext. - Simulate multi-round review conversations, observe whether request timeout errors occur, and adjust relevant parameters to adapt to actual processing time.
The values provided are common starting points and should be evaluated against relevant samples.