Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
The internal office data for state-owned industrial funds mainly comes from investment research ledgers, project due diligence files, internal approval process records, fund operation reports, and daily employee office documents. Data update frequency varies by scenario: investment research ledgers are updated in real time as projects progress, approval processes trigger updates after submission, and operation reports are updated on a monthly or quarterly basis. The document structure mostly combines structured fields and unstructured text. Structured fields include project code, investment amount, cooperating entities, approval nodes, and others, with units including 100 million yuan, calendar days, person-times, and similar units.
What Constraints These Characteristics Impose on Multi-Turn Dialogue and Prompt Workflows
Confidential project data and frequently updated investment research information for state-owned industrial funds require that multi-turn dialogue must strictly isolate contexts across different sessions to prevent cross-project data leaks. Data sources with different update frequencies require that dialogue contexts be regularly cleaned of expired content to avoid generating responses using outdated project information. Professional industrial investment terminology and structured fields require that prompts be adapted to the investment research context, while distinguishing the processing logic between structured field queries and unstructured text interpretation to prevent matching errors.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the most recent 20 conversation records, with each piece of content not exceeding 1500 characters | Adapt to the single-paragraph length of industrial fund project documents, covering the complete process of a single office conversation |
variables | Bind two dedicated variables: project code and approval node status | Match the structured data fields of state-owned industrial funds to enable accurate retrieval of project data |
| Historical context cleanup threshold | Automatically clean up expired session data every 7 days | Match the monthly update cycle of operation reports, preventing cached outdated project information |
chatId | Isolate contexts by session ID | Ensure that dialogue data from different projects does not interfere with each other, complying with state-owned asset data security requirements |
| Prompt template length | Do not exceed 800 tokens | Adapt to the short-process query needs of daily employee office work, avoiding model response timeouts |
Three Common Misconfigurations
- Subsequent responses in multi-turn dialogue do not link to historical questions, making it impossible to expand replies based on previously broken-down sub-questions. This occurs because the context retention rules for
messagesare not configured correctly, and historical questions are not fully passed into the dialogue chain. - The model's thought process is displayed in conversation records, which does not meet the concise display requirements for internal office work. This occurs because the thought process output switch for proxy tools is not turned off, causing intermediate steps to be written into the dialogue context.
- Data confusion occurs across sessions for different projects, with approval information from different fund projects incorrectly associated. This occurs because
chatIdis not used to isolate contexts across different sessions, resulting in shared historical data between projects.
How to Verify Proper Configuration
- Initiate a test session with multiple rounds of questions to verify whether subsequent responses link to historical questions. Adjust the context retention rules based on actual session length.
- View conversation records to confirm that the model's thought process is not included. Check the output configuration of proxy tools to ensure it meets display requirements.
- Switch session identifiers for different projects to verify that dialogue data from different projects does not interfere with each other. Verify the isolation effect through the
chatIdconfiguration. - Initiate a query containing professional terminology to verify whether the model's response is adapted to the industrial investment context. Adjust the prompt template based on actual usage scenarios.
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 2026-09-28.