What the Data for This Category Looks Like
The core data involved in this category comes from the State-owned Assets Supervision and Administration Commission investment supervision system, enterprise ERP and project management systems, industry market data, audit and financial statements, with a quarterly update frequency. Daily produced documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset evaluation reports, and post-investment evaluation reports. Most documents are formal official documents with multiple chapters and structured tables, with fields covering project investment amount, approval time, post-investment revenue, risk level, etc., and units including ten thousand yuan, percentage, quarterly/annual cycle, etc.
What Constraints These Characteristics Impose on the "Multi-turn Dialogue and Prompt Engineering" Link
Multiple heterogeneous data sources require multi-turn dialogue to gradually align fields across different systems and avoid data conflicts. The quarterly update frequency requires prompts to clearly define time cycles to prevent cross-cycle data from being included. Complex document structures require multi-round breakdowns to specify chapters, ensuring output content meets the requirements of investment decision-making committees or regulatory bodies. Compliance fields related to state-owned assets require prompts to strictly restrict output formats to avoid potential liability risks.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context | Previous 10–15 dialogue history entries | Matches the quarterly data cycle range, avoids redundant history interfering with retrieval |
messages processing logic | Concatenate context by session dimension | Follows the session binding rules of the messages parameter, ensures coherent dialogue context |
variables preset values | Bind quarterly and project ID variables | Relies on the variables parameter to enable dynamic field injection, adapts to parallel multi-project scenarios |
chatId binding rules | Isolate data by session ID | Ensures dialogue contexts for different projects do not interfere with each other, meets state-owned assets data security requirements |
| Single recall limit | Previous 8–12 document fragments | Matches the number of chapters in a single feasibility study report, avoids retrieving irrelevant data |
| Context filtering threshold | Calibrated through actual testing | Adapts to the field relevance of data from state-owned assets supervision systems, reduces false recall probability |
Three Common Mistakes
- Phenomenon: After initiating a dialogue, the approval data of the corresponding project cannot be retrieved, and an empty result is returned. Cause: The project ID is not bound via the
variablesparameter, causing the retrieval scope to cover all data and fail to accurately match the target project. - Phenomenon: The prompt cannot extract project data for the current year or current month, and the output result includes cross-cycle content. Cause: The time cycle field of the data is not clearly defined in the prompt, or the time context of the current session is not passed via the
messagesparameter, causing the model to fail to anchor the target cycle. - Phenomenon: After setting the workflow context count to 0, redundant history still interferes with output. Cause: The transmission logic of the context count parameter is misunderstood. This parameter actually controls the number of chat history entries passed to the API. Setting it to 0 only prevents explicit history from being passed, but session default context may still be loaded in some scenarios.
How to Confirm Proper Configuration
- Initiate a test dialogue, enter the specified project ID and time cycle, and check whether the retrieval results only cover data for the target project and corresponding cycle.
- View workflow logs, check whether the
messagesparameter passed to the API includes the expected number of dialogue history entries, and verify whether the context configuration is effective. - Adjust the time restriction field in the prompt, observe whether the model output can accurately extract project fields for the current year or current month, and verify the rationality of the prompt configuration.
- Call the API to access the application, check whether the returned results are consistent with the results of the in-platform test dialogue, and verify the correctness of the API configuration.
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.