What this category of data looks like
Auto service investment research data sources include publicly available supply chain data from original equipment manufacturers, data from auto aftermarket parts trading platforms, work order records archived by repair shops, and circulation reports from industry monitoring institutions. Update cadence is tiered: parts prices are synchronized weekly, fault code data is updated quarterly, and work order data is archived daily. Document structure falls into two categories. Structured fields include parts SKU, compatible vehicle VIN, and maintenance man-hours, with units of yuan and hours. Unstructured content covers maintenance cases and technical bulletins. Some documents include cross-vehicle compatibility association information.
What Constraints Do These Characteristics Impose on Database and Operations
The large number of structured fields and cross-vehicle compatibility associations require the database to support multi-dimensional joint indexing and efficient join operations. The tiered update cadence requires configuring differentiated incremental synchronization tasks to avoid resource preemption. The wide range of lengths for unstructured documents requires the vector database to support variable-length text splitting. The high-frequency work order write demand requires configuring a read-write separated cluster architecture to ensure concurrent stability for queries and writes. Multi-source data access scenarios require a unified ETL process to handle format differences and prevent data import exceptions.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_CONNECTION_STRING | mongodb://admin:password@host:27017/fastgpt?authSource=admin | Complies with official MongoDB connection format, and adapts to the metadata storage dependency built into FastGPT |
QDRANT_COLLECTION_SHARD_NUM | 2-4 shards | The vector data volume for auto service investment research is medium. This shard count adapts to single-node storage capacity and reduces metadata overhead |
UPLOAD_FILE_MAX_SIZE | 1024 MB | Auto service maintenance manuals and fault code documents are usually large in size. Allowing large file uploads prevents parsing truncation |
VECTOR_SEGMENT_LENGTH | 800-1200 characters | Auto service documents contain a large number of technical terms and long sentences. This segment length preserves contextual associations and avoids semantic breaks |
DB_SYNC_INTERVAL | 3600 seconds | Adapts to the weekly update cadence of auto service parts prices. Hourly synchronization balances data timeliness and resource usage |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large maintenance manuals takes a long time. This prevents the parsing process from being interrupted by a timeout during execution |
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. Testing should be conducted with local samples before finalizing settings.
Three Common Errors
- Symptom: A
Connection refusederror is returned when testing the database connection. Server logs showfailed to establish TCP connection to host:port. Cause: The database ports required for auto service investment research (such as Qdrant's 6333 and Clickhouse's 8123) have not been added to the server security group whitelist, or the host address and port in the connection string are configured incorrectly. - Symptom: The uploaded auto repair manual PDF does not generate a knowledge base index, and no matching results appear after retrieval. Cause:
UPLOAD_FILE_MAX_SIZEwas incorrectly set to a value smaller than the actual file size, causing the file to be automatically discarded after upload without triggering the parsing process. - Symptom: After connecting a custom Qdrant vector database, the recall results are always empty. Retrieval logs show
empty vector result. Cause: Professional fields related to auto service (such as VIN codes and fault codes) were not correctly mapped to the storage fields of the vector database, causing the input query to not match the stored vectors.
How to Confirm Configuration Is Correct
- The FastGPT database configuration page is accessed. The database connection parameters corresponding to auto service investment research are entered, and the test connection button is clicked. A connection successful prompt is confirmed to display on the page.
- A standard auto maintenance document is uploaded. After the parsing task completes, a preset fault code keyword is entered into the knowledge base search bar, and non-empty search results are confirmed.
- The server storage management interface is logged into. The storage path of the uploaded files is checked, and the stored file size is confirmed to match the original file, with no abnormal truncation occurring.
- The FastGPT-provided API debugging tool is used. Query text related to auto service investment research is passed in, and returned search results are confirmed to include matching professional content.
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.