What the data for this category looks like
Basic chemicals declaration materials and file review data comes from three main sources: internal enterprise declaration documents, archived files from regulatory authorities, and third-party test reports. Project declaration batches trigger data updates. Daily archived files update with project archiving batches. Individual document lengths vary widely. Some documents are only hundreds of characters long, such as approval forms. Others run to tens of thousands of characters, like process safety evaluation reports. Available fields include project number, raw and auxiliary material names, usage (units: tons per year, cubic meters per hour), process parameters (units: degrees Celsius, megapascals), and approval node status.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The wide variation in individual document length requires adjustments to multi-turn dialogue context windows for both short and long texts. This prevents long documents from being truncated and losing key process parameters. Fields with specific units require prompt engineering to explicitly instruct the AI to match units from the declaration materials exactly. This avoids unit conversion errors. Data updates tied to project batches require multi-turn dialogue to bind the current project context via chatId. This ensures review logic for different declaration projects stays isolated. The fixed format of archived files requires prompt engineering to preset fixed field extraction rules. This lets reviewers accurately locate target information during audits.
Configuration Settings
This configuration uses FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 token | Adapts to the wide length range of basic chemicals declaration materials, covering core review text |
| Single Search Limit | Top 8 entries | Matches typical attachment counts for individual declaration materials, covering primary review sources |
| Question Optimization | Enabled | Optimizes question wording for basic chemical professional process terms, improving retrieval accuracy |
chatId | Bind current project ID | Restricts multi-turn dialogue context to the current declaration project, preventing cross-project information confusion |
variables | Preset raw and auxiliary material and process parameter field variables | Lets users quickly call extracted declaration fields, cutting down repeated input |
messages | Split dialogue turns by review node | Aligns with the multi-node file review workflow, clearly communicating review requirements for each turn |
Three Common Configuration Mistakes
- Phenomenon: AI review results do not cite referenced declaration material fields or page numbers. Referenced data formats also fail to meet business requirements. Cause: Prompt engineering does not explicitly define required reference source formats and fields.
- Phenomenon: The second dialogue turn cannot access output from the first turn. Context is lost. Cause: The current dialogue context is not bound via
chatId, or the previous round’smessagesare not passed as a parameter to the dialogue API. - Phenomenon: After uploading multimodal materials such as process flow diagrams, the AI cannot identify process parameters in the images. Review results do not incorporate image content. Cause: Multimodal dialogue configuration is not enabled, or prompt engineering does not require analysis to combine image content.
How to Verify Correct Configuration
- Initiate a query about raw and auxiliary material usage. Confirm that the AI returns units matching those in the declaration materials. Adjust prompt engineering rules until units match.
- Run two consecutive dialogue turns. Confirm that the second turn can access output from the first turn. Verify
chatIdandmessagesparameters are configured correctly. - Call the API to start a dialogue. Confirm that the response uses streaming format. Adjust API configuration until it meets business latency requirements.
- Upload multimodal materials such as process flow diagrams. Confirm that the AI extracts relevant parameters using image content. Check that multimodal dialogue configuration is enabled.
The values provided here are common starting points, and should be evaluated against samples relevant to the specific deployment.
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.