What the data for this category looks like
Auto parts research report data comes from securities firm industry reports, public auto parts industry databases, and public documents from original equipment manufacturer (OEM) supporting supply chains. Update cycles cover multiple periods: monthly supporting data updates dynamically with supply chain changes, quarterly in-depth analysis reports are released quarterly, and annual industry white papers are updated annually. Document structure includes part numbers, supporting vehicle lists, cost breakdowns, supply chain tiers, and compliance certification requirements. Field units include pieces, yuan, units, tons, and similar units.
Constraints on multi-turn dialogue and prompt engineering
The multi-cycle update and multi-dimensional field characteristics of auto parts research reports impose clear constraints on multi-turn dialogue and prompt setup. Multi-cycle data requires prompts to explicitly specify the report release time range to avoid mixing data across periods. The multi-part, multi-vehicle field structure requires multi-turn dialogue to gradually guide users to clarify their target part number and supporting vehicle. Prompts must include built-in part matching rules. Complex fields such as supply chain tiers and compliance certifications require prompts to preset tier guidance logic to prevent search results from deviating from user needs. Additionally, the long document structure requires conversation context to retain confirmed filter conditions to reduce repeated questions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Auto parts research report documents are lengthy, so context must retain filter conditions such as part numbers and time ranges across multi-turn dialogue to avoid context overflow |
recall_top_k | Top 8–12 results | Auto parts research reports have multiple relevant result dimensions, so enough candidate documents must be recalled to cover requirements for different fields |
similarity_threshold | 0.75–0.85 | High precision is required for part number matching to avoid retrieving content from unrelated categories |
system_prompt_template | Please first confirm the target part number and supporting time range, then retrieve the corresponding research report content | Guide users to clarify filter conditions, adapting to the gradual follow-up logic of multi-turn dialogue |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Auto parts research report documents have large content volume, so parsing requires longer processing time |
max_turns | 8–12 turns | Multi-turn dialogue requires gradually narrowing filter conditions, avoiding context logic confusion from excessive turns |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: After upgrading to version 4.9.0, refreshing the conversation page causes conversation records to be lost, and newly created conversations are displayed. Cause: The
conversation_history_storageparameter is not configured correctly, and a non-persistent memory storage method is used, causing records to be lost after restart or refresh. - Symptom: The AI prompts that no file has been uploaded, but document interpretation rules have been configured in the system prompt. Cause: The target knowledge base is not associated with the current conversation context, or the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, causing retrieval to trigger before document parsing is complete. - Symptom: The same part number is repeatedly asked during multi-turn dialogue. Cause: The retention logic for confirmed parameters is not preset in
system_prompt, causing the conversation context to not reuse previous filter conditions.
How to Confirm Configurations Are Correct
- Run a test conversation with a clear part number and time range. Refresh the page, and check whether conversation records are retained to confirm that the
conversation_history_storageconfiguration is effective. - Upload a single auto parts research report document, run a document interpretation query, and check whether the AI can correctly extract field information from the document to confirm that the
system_prompt_templateand knowledge base association configurations are effective. - Run multi-turn follow-up questions to gradually narrow the filter range, and check whether the AI can reuse previous filter conditions to confirm that the
maxContextand conversation context logic configurations are effective. - Adjust the
similarity_thresholdparameter, run a query with a vague part number, and check whether the relevance of retrieval results meets expectations to confirm that the threshold configuration is reasonable.
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.