What this category of data looks like
Data related to basic chemical matter consultation and response comes from multiple sources: internal enterprise production process specifications, Material Safety Data Sheets (MSDS), equipment maintenance operation manuals, chemical compliance guidelines released by government regulatory authorities, and past matter consultation ticket records, among others. Update frequency varies: compliance guidelines are updated quarterly per regulatory requirements, process specifications are adjusted with production technological upgrades, MSDS are updated synchronously with material formula changes, and ticket records are stored in real time. Document structure primarily consists of structured parameter documents, which include clear chapter numbers, material names, operation parameters, and units such as MPa, ℃, kg/h. Unstructured content also exists, including operation logs and consultation dialogue history.
What constraints these characteristics impose on model access and configuration
Structured parameter documents for basic chemicals require the model to accurately match fields and units during retrieval. Therefore, configuration must ensure the indexing link retains the original format and unit information of fields. Long paragraphs of process descriptions and compliance clauses require sufficient context windows. Adjust the values of relevant context parameters accordingly. Data sensitivity is high, so a locally deployed model service must be used. Configure proxy endpoints and authentication information to point to local resources. Unstructured ticket history requires stable session context. Set a reasonable session expiration time to avoid context loss that negatively impacts the consultation experience.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Basic chemical documents often contain long paragraphs of process descriptions and compliance clauses. Sufficient context must be retained to avoid truncation of key parameters and processes |
maxTokens | 4000–6000 token | Responses to chemical matter consultations must cover complete content such as material parameters and operation steps to avoid missing key information |
quoteMaxToken | 1500–2000 token | Specific clauses from MSDS and process specifications must be accurately referenced to ensure the compliance and accuracy of responses |
toolChoice | auto | Chemical consultations may involve different scenarios such as parameter query and compliance verification. Automatic tool selection allows flexible adaptation to requirements |
temperature | 0.1–0.3 | Chemical scenarios require precise responses for parameters and processes. A lower temperature value reduces randomness and improves response accuracy |
AIPROXY_API_ENDPOINT | http://locally deployed ollama address:11434/v1 | Basic chemical data has high sensitivity. Local deployment prevents sensitive data leaks |
Three common configuration errors
- Issue: When sending consecutive consultation requests with short intervals via the FastGPT API, subsequent requests must wait for the previous generation to complete. Cause: Session concurrency isolation is not configured. The default session context is bound to a single-threaded processing logic, which cannot respond to multiple requests simultaneously.
- Issue: After private Docker deployment, calling the local Ollama service returns an error. Cause:
AIPROXY_API_ENDPOINTis not correctly configured as the access address of the local Ollama service, or the access permission for the corresponding port is not enabled. - Issue: Retrieval results for chemical compliance documents have low matching accuracy with query requirements. Cause: Content chunks are not split according to the structured chapters of chemical documents, leading to mixed binding of parameters and context within chunks, which causes the matching logic to fail.
How to confirm the configuration is correct
- Conduct a connectivity test for the local Ollama service, and check whether the
AIPROXY_API_ENDPOINTconfiguration can normally return the model list. - Send a test consultation containing chemical parameters, and verify whether the document fragments referenced in the response are consistent with the content and units of the source data.
- Initiate two consecutive consultation requests with a short interval, and confirm that both requests can be responded to simultaneously without queuing.
- Adjust the
temperatureparameter and conduct a test, and confirm that the rigor of the response meets the requirements of the chemical matter consultation scenario.
The values provided here are common starting points, and should be evaluated against the user’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.