What this category of data looks like
Market data is mostly sourced from official exchange APIs or compliant market data service provider interfaces. Update frequency varies by asset type: individual stocks receive tick-by-tick or second-level pushes, while index data is updated at minute-level intervals. Each data entry is a structured document containing fields such as asset code, asset name, latest transaction price, price change percentage, trading volume, trading amount, and data generation timestamp. Price units are Renminbi yuan. Trading volume is measured in shares or trading lots. Trading amount is measured in Renminbi yuan. Timestamps use the ISO 8601 format.
What constraints do these characteristics impose on model access and configuration
The structured nature of market data requires model access to support precise matching and extraction of structured fields, to avoid errors from unstructured parsing. The high-frequency update feature requires setting a short cache expiration time in the configuration to prevent returning outdated market data. The complex multi-field structure requires configuration options that support specifying a recall field range, to avoid irrelevant fields interfering with retrieval result accuracy. Additionally, the large volume of tick-by-tick updated data for individual stocks requires limiting the number of returned entries per retrieval, to prevent context overload that impacts model generation performance.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recall_count | Top 3-8 entries | Individual market data entries have concise information. Too many recalled entries will cause context redundancy, while too few will fail to cover valid retrieval needs |
similarity_threshold | 0.75-0.90 | Market data fields are precise. A threshold that is too high will filter valid matching results, while a threshold that is too low will introduce irrelevant interference |
maxContext | 800-1200 characters | Individual market data entries are short. The total context length must be controlled when combining multiple recalled entries, to adapt to the input limits of most large language models |
PARSE_FIELD_WHITELIST | ["asset code","latest transaction price","price change percentage","timestamp"] | Limit the scope of retrieval fields to avoid irrelevant fields interfering with matching accuracy, and focus on core market information that users care about |
CACHE_EXPIRE_SECONDS | 60-300 seconds | Match the update frequency of market data, balancing real-time performance and the cost of interface calls |
embedding_model_type | Text-matching specialized embedding models | Market data consists of structured fields, requiring precise semantic matching capabilities to adapt to field-level retrieval needs |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to conduct tests on your own samples before finalizing the configuration.
Three common mistakes
- Issue: Retrieval returns abnormal similarity values (such as reaching 10000+). Cause: No suitable embedding model was selected for structured market data, or the input field range for embedding was not configured correctly.
- Issue: Custom models deployed on a local area network cannot be called normally, returning a 401 authentication failure prompt. Cause: The
CHAT_API_KEYparameter was not filled in correctly, or the interface access whitelist for the machine where the model is located was not configured. - Issue: Market analysis content generated by the model cannot be output to the dialog box after secondary processing via the workflow module. Cause: The output port of the AI chat module was not connected to subsequent processing nodes, or the dialog box output binding for the workflow was not enabled.
How to confirm the configuration is complete
- Enter keywords for a single market asset, and check whether the retrieved results include the configured whitelist fields.
- View system logs to confirm that the response time of model calls meets the configured timeout settings, with no frequent timeout errors.
- Adjust the similarity threshold and observe whether the matching accuracy of retrieval results changes as expected.
- Trigger a high-frequency market data retrieval and confirm that the update time of the returned results matches the update rhythm of the actual data source.
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.