What this category of data looks like
This category of data originates from regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification lists, equipment isolation lists, and supervision platforms. Daily documents include risk identification records, hot work permits, confined space work permits, hoisting work permits, environmental monitoring records, hazardous waste ledgers, accident rectification reports, and more. Fields cover work type, gas concentration (ppm), qualification validity period (days), hazardous waste weight (tons), and other metrics. Data updates are triggered by each work order, occur in real time, or are updated regularly per monitoring requirements.
Constraints on Vector Models and Indexes
Data sources are scattered, and the data includes structured, semi-structured, and unstructured text. Vector models must support semantic encoding of multi-format text to avoid losing key information due to format differences. Data updates happen frequently, including real-time monitoring data and temporary work documents. Indexes must support low-latency writes and high-frequency shard expansion. Document fields are numerous and strongly correlated—for example, work permits are directly tied to guardian qualifications and monitoring data. Retrieval must accurately match cross-document related semantics, while strictly controlling false recall rates.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of single documents such as work permits and monitoring records, preventing cross-chunk breaks in key logic for risk identification and work permits |
| Overlap Length | 100–150 characters | Retains cross-chunk work boundaries and related information, stopping key compliance clauses from being split |
| Similarity Threshold | Calibrated via actual testing | Meets strict verification requirements for EHS documents, avoiding misjudgments of compliance or missed risk points |
| Single Search Limit | Top 10 results | Covers all related documents needed for pre-work risk identification, while controlling retrieval overhead |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Embedding Model Connection Timeout | 60 seconds | Adapts to large model inference delays in VLLM deployments, preventing frequent timeout interruptions of the retrieval process |
Three Common Misconfigurations
- A PostgreSQL-related configuration error occurs when deploying the vector database, and the service fails to start. The cause is directly using a generic Milvus Compose file without removing redundant component configurations for the lightweight storage needs of EHS scenarios.
- A connection timeout error occurs when connecting to the Qwen3-Embedding-8B model deployed via VLLM. The cause is not setting a reasonable request timeout parameter in the FastGPT v4.17.0 model configuration, failing to adapt to large model inference delays.
- The total disk usage of the local FastGPT knowledge base cannot be viewed, and storage proportion cannot be confirmed. The cause is not distinguishing the storage paths of original files, split document chunks, and embedded vectors, and not enabling corresponding statistical items in the system monitoring module.
How to Confirm Proper Configuration
- Upload a hot work permit document, check if the number of split chunks matches the chunk length configuration, and confirm no key information is broken.
- Initiate a retrieval request related to risk identification, check if the number of returned results matches the single search limit configuration.
- View the embedded vector storage path in the system monitoring module, confirm it matches the path of split documents in the knowledge base.
- Test the connection to the deployed embedding model, check if the returned vector dimension matches the preset dimension of the model.
The values provided here are common starting points and should be evaluated against the reader's own samples.