What the data for this use case looks like
Party building learning data for coal transportation and sales originates from internal enterprise party building management systems, official documents issued by superior party committees, and branch study records. Update schedule follows a fixed cadence: monthly new special study materials are added, and annual party building assessment standards are updated quarterly. Most documents are formatted PDFs or structured Word files. Included fields are document number, issuing entity, effective date, core study items, and assessment criteria. Units of measure for these fields are copies, entries, and items.
Constraints for model access and configuration
Party building learning data for coal transportation and sales includes both structured record fields and unstructured study text. Separate processing logic for the two data types must be clearly defined when connecting a model, to avoid field confusion that reduces Q&A accuracy. Monthly updated materials require the vector database sync cycle to match the update schedule. This prevents data lag or redundant consumption of storage resources. The multi-field document structure requires limiting the target range for context extraction during configuration. This prevents irrelevant business fields from being mistakenly included in the context for party building Q&A. Policy requirements for party building content strictly limit the data source boundary for model calls. This prevents generation of content beyond the provided materials.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single party building study materials for coal transportation and sales are usually several thousand characters long. Sufficient context must be accommodated to avoid truncating core study points |
maxTokens | 2000–3000 token | Answers to party building Q&A must cover clear policy basis and study requirements. Excessively long output may deviate from core topics |
quoteMaxToken | 1500 token | Sufficient original reference fragments must be retained to ensure the policy rigor and authority of answers, while avoiding reference content occupying too much output quota |
temperature | 0.1–0.3 | Party building content must remain rigorous. A lower temperature value reduces the chance of the model generating irrelevant or policy-offending content |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Ensures model call requests are correctly delivered to the target service, avoiding request routing errors |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Used for identity verification, ensuring the security and legality of model calls |
Three Common Configuration Mistakes
- Symptom: Model calls return a 401 status code or an unauthorized prompt. Cause: Incorrect
AIPROXY_API_TOKENconfiguration, missing correct key, or insufficient key permissions. - Symptom: Answers include content beyond the scope of party building materials. Cause:
maxContextvalue is too small to cover enough associated study materials, ortemperaturevalue is too high, causing the model to generate irrelevant content. - Symptom: Retrieved background knowledge includes non-party building coal transportation and sales business data. Cause: The field range for context extraction is not limited, resulting in structured business data being mistakenly included in the context for party building Q&A.
How to Verify Successful Configuration
- Send a test question such as "Please state the core requirements for annual party building study in coal transportation and sales enterprises". Check if returned results only include content from the configured data sources, with no out-of-scope statements.
- View model call logs to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters in interface requests match the deployed service configuration, with no formatting errors. - Adjust the
temperatureparameter, then observe changes in answer rigor to confirm the parameter is effective. - Trigger a vector database sync task, then check sync logs to confirm that files matching the update cycle are correctly loaded, with no omissions or duplicates.
The values given 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.