What the data for this category looks like
Data sources include supporting lists exported from OEM supply chain management systems, supplier production ledgers, public reports from national automotive testing institutions, and archived information from customs trade data platforms. Update cadence varies by data type:
- Supporting lists are synchronized quarterly
- Production ledgers are updated with each delivery batch
- Testing reports are updated in real time with each inspection batch
Document structure primarily uses structured tables. Fields include OE part number, supplier unified social credit identifier, production batch, inbound weight, delivery lead time, inspection item name, inspection result value, compliance standard number, and more. Field units include pieces, kilograms, days, items, and others.
Constraints on Multi-turn Dialogue and Prompt Engineering
The multi-source, multi-batch nature of auto parts due diligence data requires clear differentiation of production batches and supplier entities in multi-turn dialogue. This avoids context confusion. The large number of structured fields and varying units require prompts to predefine field extraction priorities and unit verification rules. This prevents incorrect matching of output results. Differing update cadences across data sources require multi-turn dialogue to guide users to specify their target data type. This ensures the called knowledge base matches the latest update cadence. The uniqueness requirement for OE part numbers requires prompts to include entity matching verification logic. This ensures part numbers referenced in dialogue exactly match the query target.
Configuration Settings
These configurations follow FastGPT V4.9.1 rules.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Auto parts due diligence documents often include multi-batch associated data. Sufficient retained context is needed for multi-turn dialogue to avoid information loss |
RAG_RECALL_TOP_N | 8–12 entries | Auto parts data has many closely related fields. Sufficient associated information must be recalled to cover query requirements |
PROMPT_COMPRESSION_ENABLE | Enabled | Multi-turn dialogue accumulates long context. Compression reduces token consumption and improves response speed |
CHAT_HISTORY_SCOPE | Isolated by user | Due diligence queries from different users are independent. Chat history data must be isolated to avoid confusion |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Auto parts due diligence documents include multiple sub-tables. Parsing takes longer, so the timeout period must be extended to avoid interruptions |
SIMILARITY_THRESHOLD | 0.75–0.85 | OE part numbers are unique. A high matching threshold is required to ensure recalled data accurately matches the query target |
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 targeted analysis. It is recommended to test on relevant internal samples before finalizing configuration settings.
Three Common Misconfigurations
- Phenomenon: The Human field is empty in the response record preview when calling the dialogue interface via API. Cause: The user identification parameter was not correctly carried. The dialogue context is not bound to the specified user, so user input cannot be recorded correctly.
- Phenomenon: Different users can view each other's due diligence dialogue history. Users cannot isolate their own query records. Cause: The user-isolated chat history configuration was not enabled. All users' dialogue data is stored uniformly.
- Phenomenon: Multi-turn dialogue output includes irrelevant batch auto parts data. Entity matching errors occur. Cause: The prompt does not include entity verification rules for OE part numbers and production batches. Context association becomes chaotic.
How to Verify Successful Configuration
- Initiate a test dialogue. Input a due diligence query for a specific OE part number. Confirm the Human field in the response record preview correctly records the input content.
- Create multiple test users. Each user initiates a due diligence query. Confirm that each user can only view their own historical dialogue records.
- Upload an auto parts due diligence document with multiple sub-tables. Confirm that the parsing task does not time out or interrupt.
- Enter a cross-batch query request. Confirm that the output only includes associated data for the specified OE part number and production batch.
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.