Data Profile for This Category
Data for this category originates from purchase orders, product drawings and specifications, supplier quality certificates, data collected by inspection equipment, historical quality ledgers, and supplier portal interaction information. Daily processed documents include inspection specifications, sampling plans, incoming material inspection reports, supplier audit reports, exception contact sheets, 8D correction reports, and concession acceptance approval forms. Data update frequency varies by business scenario. New inspection and report data is generated for each incoming batch. Overall supplier quality evaluations are completed monthly or quarterly. Special audits and exception reviews are carried out irregularly. Document fields include batch number, supplier number, inspection items, qualified judgment, rectification requirements, and more. Some documents include numeric inspection parameters and unit identifiers.
Constraints for Deployment and Upgrade
Data for this category comes from multiple dispersed sources with fluctuating update frequencies. New data is generated for each incoming batch. This requires configuring stable multi-source data access channels during deployment, to avoid delaying inspection processes due to data synchronization delays. Mixed processing of multiple document types requires retaining parsing compatibility with different document formats during upgrades. This prevents new parsing modules from failing to recognize unique document structures such as inspection specifications and sampling plans. Data includes strongly associated fields such as batch number and supplier number. Unified field mapping rules must be configured during deployment to ensure consistency across data from different sources. For intranet deployment scenarios, local storage and model call configurations must be planned in advance. This avoids data upload and model inference failures due to network restrictions.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:yourpass@localhost:27017/fastgpt?authSource=admin | Compatible with FastGPT v4.17.0, persists multi-batch incoming material data and supplier quality ledgers, supports fast queries by batch number |
REDIS_URL | redis://:yourpass@localhost:6379/0 | Caches frequently called sampling plan and supplier qualification data, reduces database concurrent pressure |
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 (intranet) or public cloud storage endpoint | Stores document attachments such as incoming material inspection reports and 8D correction reports, supports upload and reading of large-size drawing files |
| Chunk length | 800–1200 characters | Adapts to semantic extraction of long texts such as inspection specifications and exception contact sheets, avoids chunk breaks or context overflow |
| Single recall limit | Top 10 entries | Matches inspection items and historical quality data for a single incoming batch, controls retrieval scope to ensure response speed |
| File parsing timeout | 300 seconds | Processes incoming reports with complex drawings and numeric parameters, avoids task interruption due to parsing timeout |
Three Common Errors
- Symptom: After upgrading the version, the embedding model fails to correctly generate document vectors, and the console returns model call failure logs. Cause: Embedding model configuration parameters were not updated synchronously, leading to incompatibility between the deployment package and model version.
- Symptom: In a docker-deployed instance, after uploading an incoming material report, no corresponding data appears in the vector database, and the file parsing module logs show split-related exceptions. Cause: The temporary directory of the file parsing container was not mounted correctly, leading to temporary file write failures during chunk processing.
- Symptom: After completing a FastGPT version upgrade in a network-free environment, locally stored supplier quality certificate data cannot be read. Cause: Offline dependency packages were not downloaded in advance during the upgrade, leading to storage driver installation failures and inability to access the local file system.
How to Verify Configurations
- Run the built-in database connectivity test tool to verify whether the
MONGODB_URIandREDIS_URLconfigurations take effect, and confirm there are no connection timeout or permission errors. - Upload a standard incoming material inspection report, check the running logs of the file parsing module, and confirm that the document content is correctly parsed and chunk data is generated.
- Initiate a simulated technical document cross-review request, check that the number of returned retrieval results matches the configured recall rules, and no abnormal filtering occurs.
- In a local deployment scenario, call the configured large model interface, confirm that the model response is normal, and there are no network or permission-related errors.
The values provided are common starting points and should be measured against the reader's own samples.