What the data for this category looks like
The data for automated equipment investment research primarily comes from official technical manuals of equipment manufacturers, industry compliance documents, equipment operation and maintenance logs, and financial scenario adaptation case documents. Update cycles are irregular: updates align with new manual releases from manufacturers, maintenance logs are archived daily, and adaptation cases are added as needed. Most documents use structured formats, with modules such as parameter tables, installation steps, fault codes, and compliance requirements. Core fields include rated power (kW), operating temperature (℃), interface specifications (mm), and compliance certification numbers. Units and field definitions strictly follow industry standards.
What constraints these characteristics impose on deployment and upgrade
The long, structured nature of automated equipment investment research data requires adjustments to file parsing and upload configurations during deployment, to avoid parsing failures caused by timeouts or capacity limits. The standardized requirements for structured fields require specifying dedicated field extraction rules during knowledge base configuration, to ensure parameter-related information is accurately identified and stored. Irregular update cycles and real-time maintenance logs require upgrade phases to support incremental synchronization and real-time data stream connections, to avoid resource waste from full retransmissions. Additionally, compliance requirements in financial scenarios require that document parsing and storage in the knowledge base meet data security specifications, so corresponding permission verification rules must be configured during deployment.
How to Configure Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Automated equipment technical manuals typically contain multi-page structured parameters, leading to long parsing times |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single files such as equipment manuals and maintenance log packages are usually large in size |
maxContext | 8000–12000 characters | Context information covering multiple sets of equipment parameters is required to ensure completeness of investment research queries |
Recall count | Top 8 results | Investment research scenarios require parameter comparisons across multiple models of the same product category |
Similarity threshold | 0.75–0.85 | Precise differentiation of parameter differences between different models of the same product category |
PARSE_STRUCTURED_FIELDS | Enabled | Automated equipment documents contain standardized parameter fields, so structured extraction must be enabled to improve recall accuracy |
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 individual analysis, and testing on local samples before finalizing settings is recommended.
Three Common Configuration Errors
- Symptom: Accessing the FastGPT service via public IP returns a
502 Bad Gatewayerror. Cause: TheEXPOSE_PUBLIC_PORTparameter is not configured, or the service listening address is only bound to127.0.0.1. - Symptom: Core parameter fields such as rated power are not correctly extracted after parsing automated equipment manuals. Cause: The
PARSE_STRUCTURED_FIELDSconfiguration is not enabled, or matching rules for structured fields are not specified. - Symptom: Locally deployed models cannot be called normally, returning a
404 Not Founderror. Cause: The port of the model deployment machine is not mapped to the public network, or the model API address configured in FastGPT does not point to the actual deployed machine IP.
How to Verify Configurations Are Correct
- Upload a typical automated equipment technical manual. Check if parsed structured fields include preset parameters such as rated power and operating temperature, to confirm the field extraction configuration is effective.
- Initiate a query for equipment parameter comparison. Check if the number of returned results matches the preset configuration, to confirm the recall rule is effective.
- Access the FastGPT frontend page via an external machine. Test whether public network connection is normal, to confirm the port and listening configuration is effective.
- View the vector database update logs. Confirm that newly added equipment documents have been correctly vectorized and stored, to confirm the incremental synchronization configuration is effective.
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.