In-Terminal Natural Language Retrieval for Market Data: Multi-Turn Dialogue and Prompt Engineering

Market data is sourced from exchange public market data APIs and compliant third-party market data aggregation services. Update frequency varies by

What Data for This Category Looks Like

Market data is sourced from exchange public market data APIs and compliant third-party market data aggregation services. Update frequency varies by asset type: equities use per-minute snapshots or real-time tick-by-tick pushes, while indices and futures use real-time updates. Single data documents follow a standardized structure, including fields such as asset code, asset name, latest transaction price, price change amount, price change percentage, daily trading volume, daily trading turnover, daily highest and lowest prices, and data generation timestamp. Price units are yuan per share or the pricing unit of the corresponding asset. Trading volume units match the trading unit of the corresponding asset. Timestamps use the ISO 8601 format.

Constraints on Multi-Turn Dialogue and Prompt Engineering

The real-time update nature of market data requires that latest data be prioritized during multi-turn dialogue, avoiding reliance on expired context caches. The large number of fields and detailed pricing units require prompts to explicitly specify target fields to call, preventing vague output results. Multi-turn dialogue must associate the currently discussed asset code and name to avoid cross-asset confusion. The data generation timestamp field can be used to verify context data timeliness; filtering requirements for outdated data must be included in prompts.

Configuration Settings

Configuration ItemRecommended ValueRationale
maxContextPrevious 3-5 entriesMarket data has strong timeliness; excessive old context will introduce expired data. 3-5 entries retain asset association information while avoiding redundancy
contextWindowTimeout600 secondsMarket data updates frequently; context within 10 minutes retains valid asset association information, and expired data is automatically cleaned up beyond this threshold
similarityThreshold0.75-0.85Used to match the relevance between user questions and market data fields. This range filters low-relevance field information and prevents incorrect field calls
rerankTopNTop 2-3 entriesReranks recalled market data fields, prioritizing fields most relevant to the current conversation to improve response accuracy
systemPromptCustom content that always includes "Prioritize using the latest market data, and clearly specify the corresponding asset code and data generation time when calling fields"Clarifies the core constraints of the prompt, preventing responses that rely on expired data or confuse assets

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 individual analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

  • The number of context entries displayed in conversation details is far lower than the set maxContext value, and responses fail to associate asset information from historical conversations. This occurs when context persistence configuration is not enabled, or when contextWindowTimeout is set too short, causing old context to be cleaned up prematurely.
  • Prompts fail to correctly identify asset codes and field names separated by spaces in user questions, such as recognizing "600000 price change percentage" as a single incorrect keyword. This happens when the prompt does not include explicit requirements for identifying space-separated parameters, or when corresponding word segmentation optimization configuration is not enabled.
  • When a conversation node is executed cyclically in a workflow, the process cannot be terminated based on the judgment result of the previous round. This occurs when trigger rules for conditional branch nodes are not configured, or when the judgment result is not correctly bound to the process's termination/continue logic.

How to Verify Correct Configuration

  • Initiate a single-turn conversation that includes an asset code and field name, verify that the response accurately calls the latest data for the corresponding field.
  • Initiate a conversation with multiple asset switches, verify that the context retained in conversation details includes the currently discussed asset information.
  • Adjust the value of similarityThreshold, verify that the recalled field information meets the expected relevance requirements.
  • Initiate a multi-turn conversation via the API channel, verify that the returned results associate asset and field information from historical conversations.

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.