Data Characteristics for This Category
Cold chain logistics R&D document data originates from temperature control equipment logs, transport records, drug or biological product characteristic reports, SOP (Standard Operating Procedure) files, and compliance audit reports. Data updates frequently. Sensor data, such as temperature and humidity, generates continuously during transport and storage. Document structures vary, including structured data tables, semi-structured report texts, and unstructured images and charts. Fields and units are industry-specific. For example, temperature units are often Celsius or Fahrenheit, humidity is a percentage, timestamps are precise to the second, and specific biological product batch numbers and serial numbers are present. Files may also contain multilingual descriptions and specialized terminology.
Constraints Imposed by These Characteristics on "Database and Operations"
High-frequency sensor data updates require databases with high write throughput and efficient time-series data processing mechanisms to prevent data accumulation and query delays. Document diversity necessitates multi-modal data storage support. This includes relational databases for structured data, document databases for semi-structured reports, and vector databases for semantic retrieval of unstructured text. Identification and standardization of specialized fields and units are critical. The pre-processing stage requires unit conversion and entity recognition to ensure accuracy in subsequent knowledge base construction. Multilingual content and specialized terminology demand more advanced pre-training and fine-tuning for vector models, impacting embedding quality and retrieval recall. Compliance requirements mandate complete and immutable audit logs, imposing strict rules on database security and backup strategies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_VERSION | 6.0 or higher | Supports richer aggregation pipeline operations and document query optimizations |
MILVUS_REPLICAS | 3 | Improves vector database availability and query concurrency |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large R&D reports and multimedia attachments |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient time for structured parsing of complex documents |
Chunk size | 800 characters | Balances context completeness with vector embedding efficiency, reducing truncation risk |
Similarity threshold | 0.75 | Ensures recall while reducing interference from irrelevant results |
Three Common Mistakes
- MongoDB startup fails with a version incompatibility error: This usually occurs because the host CPU does not support AVX instruction sets, which container images default to. Use a MongoDB image compatible with lower-version CPUs.
- Files upload but remain unresponsive or fail to parse for an extended period:
PARSE_FILE_TIMEOUT_SECONDSmay be set too low. This causes the system to time out when processing large or complex documents, preventing successful parsing. - Knowledge base retrieval results are poor and lack relevance: This often happens when specialized terminology and abbreviations specific to cold chain logistics are not incorporated into vocabulary expansion or model fine-tuning. This leads to vector embeddings that do not accurately capture semantic information.
How to Verify Correct Configuration
- Upload a typical cold chain transport report containing key fields like temperature and humidity. Check if the parsed data accurately identifies and extracts all relevant fields and values.
- Upload a file exceeding the
UPLOAD_FILE_MAX_SIZElimit via the FastGPT interface. Confirm the system correctly indicates the file is too large and rejects the upload. - Perform a knowledge base retrieval using specialized terminology. Compare the returned results with the expected relevant documents. Evaluate if the similarity scores meet the expected threshold.
- Check MongoDB and Milvus container logs for continuous error messages or connection interruption warnings. Ensure database services are running stably.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.