What the Data for This Category Looks Like
Marketing content data for general equipment comes primarily from official product manuals of partner manufacturers working with financial institutions, working condition adaptation case libraries, compliance certification documents, and supporting marketing materials for leasing. Data updates are triggered by new product launches, adjustments to compliance standards, or customer feedback, with no fixed cycle. Each single update covers full-dimensional information for a single model. Documents combine structured tables and long-form text. Core fields include equipment model, rated power (unit: kW), maximum rotational speed (unit: r/min), applicable working conditions, and warranty period (unit: month). Some documents include transcribed content from scanned copies of third-party test reports.
What Constraints These Characteristics Impose on Deployment and Upgrade
General equipment marketing content includes structured parameters and supporting financial plan information. The deployment phase requires configuring unified field parsing rules to avoid risk control matching deviations caused by unit conversion or parameter splitting. Data updates have no fixed cycle, and each single update covers full-dimensional information for a single model. The upgrade phase needs to support incremental synchronization mechanisms to reduce server load caused by full replacement and avoid affecting solution matching for existing customers. Documents include structured tables and transcribed scanned content, so compatible file parsing parameters are required to ensure complete extraction of long text and table fields, and ensure the accuracy of leasing plan information. The presence of multi-industry adaptation tags requires supporting custom tag mapping rules during deployment to adapt to lead generation screening needs for different enterprise customers in financial scenarios.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | General equipment documents include long text and structured tables, with long parsing durations, to avoid interrupting the parsing process due to timeout |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some scanned copies of compliance certification documents have large file sizes, allowing large file uploads to cover complete test content |
maxContext | 8000–12000 characters | The text of general equipment technical parameters and leasing plans is lengthy, requiring sufficient context to ensure retrieval of related content |
| Similarity Threshold | 0.75–0.85 | The accuracy requirements for equipment parameters and leasing plans are high, requiring filtering of irrelevant content with low matching degrees |
| Number of Retrieved Entries | Top 8 entries | Single-model information for general equipment marketing content is scattered, requiring retrieving enough entries to cover selection, working condition, and leasing plan dimensions |
CHUNK_SIZE | 800 characters | There are many equipment parameter fields with close associations, requiring retaining field associations during chunking to avoid parameter splitting breaks |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Empty content returned when calling the ollama model, with the interface showing empty model responses. Cause: The ollama service port was not mapped to the container network during deployment, or the ollama address configured in FastGPT does not include the default port 11434.
- Long blank loading time when accessing the deployment address, with no error messages. Cause: No firewall rule for port 3000 was configured under the Ubuntu system, or the configuration file directory was not correctly mounted when starting the Docker container.
- The ollama model cannot be called normally after local deployment restart. Cause: The Docker container and ollama service were not run in the correct startup order after restart, causing FastGPT to fail to connect to the local model service.
How to Confirm Successful Configuration
- Upload a general equipment product manual document, check if the parsed fields include complete information such as equipment model and power unit, to confirm that the parsing rules are effective.
- Configure a test model API key, initiate a small test call, to confirm that the model can normally return response content.
- Check the server firewall and Docker port mapping rules, try accessing the deployment address via intranet or public network, to confirm that the page loads normally.
- Initiate an incremental update test, upload an equipment document for a new model, confirm that the existing knowledge base content is not overwritten and the new content can be normally retrieved.
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.