What the Data for This Category Looks Like
Dedicated equipment data primarily comes from local runtime logs of financial trading terminals, exchange market data push APIs, and synchronized data from internal enterprise risk management systems. The update schedule is full batch updates for all device runtime and revenue data for the current day after daily market close. Some dedicated devices with real-time monitoring push minute-level incremental status data.
Each single-device daily report includes a fixed set of fields: unique device identifier, trading day, daily transaction execution records, total daily revenue, cumulative total revenue, and runtime status identifier. Field units are as follows: number of transaction records is measured in transactions, revenue amount is in Chinese yuan, and runtime status is an enumerated value. Documents are grouped by device ID, with each group containing detailed entries for the day’s trading nodes.
Constraints on Multi-turn Dialogue and Prompt Engineering
Dedicated equipment data is grouped by device ID and sourced from multiple data channels. Multi-turn dialogue must support filtering retrieved knowledge base content using the unique device identifier to prevent cross-device data confusion.
Data updates fall into two categories: full daily post-close updates and minute-level incremental updates. Prompts must explicitly specify the data time range and data source type to avoid calling outdated or mismatched information.
Documents include enumerated runtime status fields. Prompts must predefine standard meanings for these status enumerations to ensure consistent interpretation of statuses during dialogue. Additionally, single-device daily reports contain detailed transaction entries. The context retrieval limit for multi-turn dialogue must match the average length of a single document to avoid loading redundant content and causing response delays.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The average length of a single-device daily report document is 1000-3000 characters. Multi-turn dialogue must retain 3-4 rounds of context to avoid content truncation |
recallTopK | Top 6–10 entries | Dedicated equipment data is grouped by device ID. Documents related to a single device will not exceed 10 at maximum. An overly large value increases response latency |
SIMILARITY_THRESHOLD | 0.75–0.85 | Device IDs are unique identifiers for dedicated equipment. A high similarity threshold is required to ensure retrieval of documents matching the target device and avoid mixing irrelevant data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Batch daily report documents for multiple devices contain numerous detailed entries, requiring sufficient time to complete parsing |
maxConversationRounds | 5–8 rounds | Yield rate analysis for dedicated equipment typically requires 3-5 rounds of context association. Excessive rounds lead to redundant context |
promptTemplate | Configured as "Answer based on dedicated equipment data in {context}, specify target device ID and time range, distinguish between full post-close data and real-time incremental data" | Explicitly specify data filtering rules and data source types to adapt to the grouped data characteristics of dedicated equipment |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Long dialogue response latency. Cause: The
recallTopKconfiguration is set too high, retrieving too many irrelevant dedicated equipment documents, which increases the time spent on context processing and model inference. - Symptom:
Unexpected end of JSON inputerror appears during dialogue. Cause: Null values or format abnormalities exist in the detailed data fields of dedicated equipment, leading to truncation when the model parses returned structured results. This issue is more pronounced when using thechatglm2model, which has stricter format validation. - Symptom: Each dialogue must be re-initiated, and continuous sessions cannot be maintained. Cause: The
persistConversationconfiguration item is not enabled, so dialogue context is not persistently stored, and historical information cannot be retained across dialogues.
How to Verify Proper Configuration
- Initiate a targeted query using a single device ID. Verify that retrieved knowledge base content only includes daily report data for the corresponding device, with no cross-device redundant information. Adjust related configurations until this requirement is met.
- Initiate a multi-turn query that includes a time range. Verify that the model can correctly distinguish between full post-close data and real-time incremental data. Adjust the prompt template to ensure explicit specification of data types.
- Initiate a dialogue with more than 5 consecutive rounds. Verify that context is correctly retained and the dialogue process is not interrupted. Adjust conversation rounds and context storage configurations as needed.
- Upload batch daily report documents for multiple devices. Verify that no parsing timeout errors occur. Adjust the file parsing timeout configuration to ensure successful parsing.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.