What the data for this category looks like
Core data for this category comes from project document libraries, configuration lists, meeting minutes, and test reports produced during each project phase. Daily processed documents include completion documents, configuration manuals, training records, and acceptance certificates. Project phase changes or project completion trigger data updates. No fixed update cycle exists. Document structures include both structured fields and unstructured content. Some acceptance certificates and training records exist as scanned files or stamped images. Common fields include project number, version number, delivery date, and stamp status. No unified mandatory unit requirements apply. Adaptations must align with the format of individual project documents.
What constraints do these characteristics place on model integration and configuration?
The multi-source, decentralized data nature of project document handover and archiving requires model integration to support multi-format data processing. This covers both text and image documents. Long completion documents and configuration manuals require configuration to cover sufficient context length. This prevents truncation of key information. Data that updates per project phase requires model configuration to flexibly adapt to document formats and field rules for different phases. Analysis results for this category directly tie to project acceptance and final payment settlement. Model outputs must be rigorous. Irrelevant or deviant content must be avoided.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Project documents are mostly long text. Sufficient context must cover complete delivery documents to avoid key information truncation |
vision | Enabled | Daily processed acceptance certificates and training records often include scanned files or stamped images. Multimodal recognition capability is required |
temperature | 0.2–0.4 | Test cases and defect analysis need rigorous output. Irrelevant or deviant content must be avoided |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions paths. Do not use single-model service addresses such as VLLM/Ollama/Xinference | This variable is a required environment variable for the App/Admin server. It must match the listening address configured for the AI Proxy server |
toolChoice | auto | Tools must automatically call based on tasks to complete checks of document completeness and version consistency |
quoteMaxToken | 1000–1500 token | Sufficient original text fragments must be retained for defect traceability and delivery evidence verification |
Three common configuration errors
- The symptom is missing key document fragments in model-generated analysis results. Troubleshoot by checking if the
maxContextparameter is unconfigured or set too small. This causes long document context truncation. - The symptom is failed parsing of scanned acceptance certificates. Troubleshoot by checking if the
visionconfiguration item is disabled. Multimodal image recognition capability is not activated. - The symptom is model call failures or incompatibility errors. Troubleshoot by checking if required
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables for the App/Admin server are missing.AIPROXY_API_ENDPOINTis the root address of the AI Proxy service.AIPROXY_API_TOKENis the AI Proxy administrator token. It must match the AI Proxy server configuration and cannot be empty. Alternatively, theAIPROXY_API_ENDPOINTaddress does not match the deployed AI Proxy service environment. Missing these configurations will cause App/Admin service startup verification failure.
How to verify successful configuration
- Upload a single complete completion document. Confirm the model correctly extracts core fields such as project number and version number.
- Upload a scanned acceptance certificate with a stamp. Confirm the model recognizes stamp information in the image and completes validity verification.
- Set
toolChoiceto auto. Submit a defect analysis task. Confirm the model automatically calls tools to complete document version consistency checks. - View model call logs for FastGPT v4.17.0. Confirm no prompts for context truncation or parameter configuration errors appear.
The values provided 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 2026-10-19.