What data for this use case looks like
Data sources are official written transcripts and live audio transcription texts from Party building meetings at all levels. The update cycle is 1 to 3 days after a single meeting concludes to generate the corresponding document. Document structure includes fixed fields such as meeting theme, holding time, core speaking points, collective resolution matters, and follow-up work requirements. Individual document lengths vary widely, with the complete record of a single meeting as the minimum data unit. There is no unified fixed unit for document size.
What constraints these characteristics impose on model access and configuration
The data sources for Party building meeting spirit summarization include official transcripts and transcribed texts, with traits of long text span and fixed structure. The long text span requires model access to adapt to input content of varying lengths, to avoid truncating key information such as core resolutions and follow-up work requirements. The fixed document structure requires configurations to support targeted extraction per preset fields, to prevent irrelevant content from interfering with summarization generation. The update cycle of one document per single meeting does not require high-frequency incremental synchronization configuration; access adaptation can be completed via a single batch task.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meeting transcripts are usually thousands of characters long. This range can cover the core content of a single document and prevent key information from being truncated |
maxTokens | 2000–3000 token | The summary needs to cover the core points and resolutions of the meeting. This output length balances information completeness and readability |
temperature | 0.1–0.3 | Party building content must remain rigorous and accurate. This parameter range reduces the randomness of generated content |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the deployment service provider | Matches the network path for model calls, ensuring requests can be correctly sent to the target model |
AIPROXY_API_TOKEN | Fill in the exclusive key assigned | Completes identity verification for model calls, ensuring requests can be recognized by the target model |
toolChoice | none | Meeting spirit summarization only requires text generation, no need to call external tools for assistance |
Three common configuration errors
- The symptom is failed model access detection, with connection timeout or 401 status code returned. The cause is incorrect filling of
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameter values, leading to mismatched identity verification or network path. - The symptom is that workflow node calls return global context content instead of only the current task's context. The cause is failing to specify using only the current session context in node configuration, or enabling the global context synchronization switch.
- The symptom is that call requests cannot be distinguished when multiple models with identical names are added. The cause is failing to configure an independent
AIPROXY_API_TOKENfor each model, or not setting unique identifiers for different models on the platform.
How to verify successful configuration
- Submit a single test meeting transcript, and confirm whether the model's returned summary includes the meeting's core points and resolution matters.
- Check the model call log to verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameter values are correctly loaded, with no null values or incorrect characters. - Test the multi-model configuration scenario to confirm that call requests for different models can be correctly distinguished using the configured keys.
- Adjust the
temperatureparameter and submit a test, confirming that the rigor of the generated content changes as expected with the parameter adjustment.
The values provided here are common starting points, and should be evaluated 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.