What Data for This Category Looks Like
Data for internal office and employee services at power grid enterprises primarily comes from internal OA systems, operation and maintenance ticket platforms, employee training documents, power grid dispatch logs, and equipment ledgers. Update frequencies vary significantly: operation logs and real-time tickets are updated minute-by-minute, while employee handbooks and policy documents are updated quarterly. Documents include structured fields such as ticket numbers, tower IDs, and voltage levels, as well as unstructured content. Units often carry power-specific identifiers like kV, outage duration, and team IDs.
What Constraints These Characteristics Impose on Model Access and Configuration
Multi-source heterogeneous data structures require models to support both structured field extraction and non-long-form text parsing. Minute-level real-time operation data requires model call response latency to align with business processing windows. Power-specific fields such as voltage levels and tower IDs need dedicated prompts added to model configuration to avoid recognition errors. Long-form text power grid dispatch logs place higher demands on context window parameters, to prevent content truncation that reduces analysis accuracy.
All configuration instructions on this page apply to FastGPT v4.17.0.
How to Set Configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text internal documents such as power grid dispatch logs and operation reports, avoiding context truncation |
maxTokens | 2000–3000 token | Matches the reply length requirements of employee service conversations, preventing overly long single outputs that disrupt interaction |
quoteMaxToken | 1500 token | Limits the length of referenced internal document fragments, avoiding redundant content that interferes with model outputs |
temperature | 0.3–0.5 | Reduces randomness to ensure accurate replies for power-specific fields and ticket processes |
AIPROXY_API_ENDPOINT | Fill in the private model address based on deployment | Adapts to internal data security requirements of state-owned enterprises and public institutions, preventing sensitive business data leaks via external interfaces |
AIPROXY_API_TOKEN | Fill in the key managed under enterprise permissions | Follows internal data security specifications, restricting unauthorized model calls |
Three Common Misconfigurations
- Phenomenon: The workflow only calls a single model, and cannot perform step-by-step processing to output only the final result. Cause: The node output rules of the workflow are not configured correctly, and only the result display of the final conversation node is enabled.
- Phenomenon: The model runtime context exceeds the 10k limit set by the deployment, resulting in truncation or errors. Cause: The parameter priority between the deployment side and the FastGPT configuration side is confused, and the parameters set in FastGPT configuration are not followed.
- Phenomenon: The conversation has no streaming output, and only returns the complete result. Cause: Whether streaming transmission is enabled in FastGPT's model call configuration is not confirmed, or the model itself does not support streaming responses.
How to Verify Successful Configuration
- Submit test text containing power-specific fields such as 10kV line faults, and check whether the model output correctly identifies the fields and generates process-compliant replies.
- View the workflow operation log to confirm that multi-node model calls are triggered, and the final node results are displayed normally.
- Test long-text input such as a 1000-word operation report, and check that the context is not truncated and the reply meets expectations.
- Initiate a real-time conversation to verify whether the output format meets the configuration requirements, or returns the complete result.
The values provided here are common starting points, and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.