What the data for this category looks like
Auto service investment research data sources include publicly disclosed manufacturer documents, public industry association reports, offline store operation ledgers, parts supply chain quotation systems, and official new vehicle launch announcements.
Data update frequencies vary: manufacturer financial reports are updated quarterly, monthly industry data is updated weekly, new vehicle announcements are released in real time alongside launches, and supply chain quotations are updated daily.
Three document structure types are supported: structured tables, which contain fields such as sales volume, revenue, and cost; semi-structured meeting minutes, which contain interview highlights and market judgment content; and unstructured logs, which contain work order records and customer feedback content. Field units use basic units such as yuan and days, with no custom format requirements.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Structured documents account for a high proportion of the total data. In multi-turn dialogue, extraction rules for structured fields and unstructured content must be clearly distinguished. Prompts must specify the corresponding format for fields to avoid confusion across fields.
Significant differences exist in data update frequencies. Real-time new vehicle announcements and quarterly financial report data require different recall time threshold configurations. Multi-turn dialogue contexts must be associated with data update time tags to ensure response timeliness matches the data.
Multiple document types are covered. Prompts must specify separate processing logic. For example, table data must strictly match fields and units, meeting minutes must extract core judgments, and logs must be associated with specific service nodes. Additionally, field unit standardization requirements are high. Prompts must clearly specify unified conversion rules to avoid unit confusion.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Auto service investment research data includes multiple financial reports and industry reports. Longer contexts need to retain sufficient historical dialogue and retrieved content to avoid truncation of critical information |
RECALL_TOP_N | Top 10 entries | Auto service investment research data has many fields and close associations. Too many retrieved entries will increase context load, while too few will miss key related data. 10 entries can cover information needs for most investment research scenarios |
SIMILARITY_THRESHOLD | 0.75–0.85 | Fields in auto service investment research data have high similarity. A threshold that is too low will introduce irrelevant data, while a threshold that is too high will miss related segmented category data. This range balances retrieval precision and coverage |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Financial reports and industry report documents for auto services are usually lengthy and take longer to parse. 120 seconds can cover parsing needs for most large-volume documents |
PROMPT_TEMPLATE | Specify processing rules by data type, retain time and brand tags in context | Auto service investment research data includes multiple document types such as structured tables, semi-structured meeting minutes, and unstructured logs. Processing logic for different documents must be clearly defined. Time and brand tags in context can avoid ambiguity in multi-turn dialogue |
HISTORY_IGNORE_PLUGIN | Enable and specify plugin identifiers | Some scenarios call designated reply plugins. Rules to ignore plugin call records must be configured to avoid historical content interfering with current dialogue |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on in-house samples before finalizing settings.
Three Common Misconfigurations
- Symptom: No streaming output during dialogue, only full generated results are returned. Cause: The
STREAM_OUTPUTconfiguration item is not enabled, or the selected model does not support the streaming output protocol. - Symptom: Extra spaces appear in model output, or letters are forcibly converted to uppercase. Cause: The prompt does not clearly specify output format specifications, retrieved documents contain non-standardized text fragments, and the prompt does not require unified format processing.
- Symptom: The file parsing tool cannot be triggered when calling the model. Cause: No tool call trigger instruction is configured in the prompt, or the
TOOL_CALL_ENABLEconfiguration item is not enabled, preventing the model from recognizing tool call requirements.
How to Confirm Configurations Are Correct
- View the knowledge base parsing logs to confirm that all target documents have been parsed, with no parsing timeout or failure records.
- Initiate a multi-turn dialogue to verify that context brand and time tags are correctly retained, and output content complies with the format rules specified in the prompt.
- Test the tool call function to confirm that the model can trigger the file parsing tool as required by the prompt and return correct parsing results.
- Adjust relevant retrieval configuration items to verify that the number and relevance of retrieval results meet expectations, with no excessive irrelevant content or missing critical data.
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.