Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Ledger data for fertilizers and pesticides comes from internal enterprise production management systems, sales ERP systems, and electronic reports from quality inspection departments. Production ledgers are updated per production batch. Sales ledgers are synced daily. Quality inspection reports are released after the corresponding production batch is completed. Most documents are structured spreadsheets or standardized JSON format. Each entry includes fixed fields such as batch number, production start time, raw material input volume, finished product output volume, quality inspection qualified items, sales flow region, and outbound quantity. Units include tons, kilograms, days, and others.
What constraints do these characteristics impose on the multi-turn dialogue and prompt link
Multiple data sources require multi-turn dialogue to first clarify the required ledger category, to avoid confusion between production, sales, and quality inspection data. A large number of fields require prompts to clearly define the extraction scope, to prevent the model from capturing non-target fields. Different update rhythms require confirming the statistical cycle granularity in dialogue, such as summarizing by batch or natural week. Structured document formats require multi-turn dialogue to guide users to provide standardized parameters like batch numbers or time ranges, to ensure the accuracy of summarization results.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Max Context Size | 8000–12000 characters | Fertilizer and pesticide ledgers contain multiple types of fields, sufficient historical dialogue must be retained to clarify ambiguous query requirements |
| Question Optimization | Enabled | Queries for ledger summarization often include vague expressions. Optimization can convert queries into precise field extraction and summarization instructions |
| Single Search Limit | Top 8 entries | Ledger data is grouped by batch. Extracting no more than 8 groups at a time avoids redundant results and aligns with standard query granularity |
| Single Request Data Limit | 1000 MB | Single-batch ledger data has a large volume. 1000 MB covers most conventional production and sales ledger retrieval needs, and can be adjusted based on actual testing |
| Timeout Threshold | 60 seconds | Retrieval and summarization of ledger data takes a certain amount of time. 60 seconds covers most conventional query scenarios, and can be adjusted based on actual testing |
| Variable Presets | Preset "Ledger Type", "Statistical Cycle", "Target Batch" | Preset high-frequency query parameters via the variables parameter to reduce repeated clarification steps in multi-turn dialogue |
Three common mistakes
- Symptom: The Human field is empty in the API call response preview. Cause: The role and content of the user's query were not correctly passed in the
messagesparameter, causing the conversation history to fail to be properly recognized. - Symptom: Different operators cannot view their own exclusive conversation history. Cause: User isolation permissions for conversation history were not configured, causing all sessions to share the same context pool.
- Symptom: Summarization results include redundant information from unrelated fields. Cause: The required ledger fields were not clearly defined in the prompt, causing the model to capture non-target data.
How to confirm configuration is complete
- Initiate a test query, input a vague requirement such as "Summarize the fertilizer ledgers for the first half of the year", check if the system automatically clarifies the ledger type and specific statistical cycle.
- Initiate a conversation using different accounts, confirm that only the session records initiated by the current user can be viewed.
- Pass the
chatIdparameter when calling the API, verify that the context is correctly retained when calling the same session multiple times. - Check that the summarization results only include the preset ledger fields, with no redundant non-target information.
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.