Natural Language Query for Project Data: Internal Policies and Data Q&A — HTTP Interfaces and External Systems

Natural Language Query for Project Data: Internal Policies and Data Q&A — HTTP Interfaces and External Systems

What This Category of Data Looks Like

This category of data originates from internal project management systems, financial ledger systems, and progress reporting systems. The update schedule is full synchronization every day at midnight, or incremental updates triggered on demand. The document structure of a single project data record includes unique identifier, project name, owning department, total budget, actual expenditure, current progress status, planned completion date, and responsible person account. For field units: total budget and actual expenditure are measured in yuan, and planned completion date and current date use the YYYY-MM-DD format.

What Constraints Do These Characteristics Impose on HTTP Interfaces and External Systems

Reference only; verify against the target FastGPT release and deployment documentation.

How to Configure

Configuration ItemRecommended SettingRationale
AuthorizationBearer example valueUsed for identity authentication, binds project data access permissions for users or departments, complies with internal data security requirements
variables{"project_scope": "internally specified department", "time_range": "last 30 days"}Limits query scope, avoids returning excessive project data beyond requirements, adapts to department permission rules in internal policies
responseDataOnly return project ID, name, total budget, planned completion dateMatches common query needs in internal Q&A scenarios, reduces data transmission volume, improves interface response speed
Timeout600 secondsAdapts to the volume of project data, avoids interface timeouts caused by excessive data volume
Retry Count2–3 timesAddresses internal network fluctuations or temporary unavailability of external systems, improves call stability
SSL Certificate VerificationEnable based on deployment environment, disable when no valid certificate existsAdapts to internal network security policies, avoids call failures caused by certificate issues

Three Common Configuration Mistakes

  • Scenario: The interface returns a 403 Forbidden status code, or prompts that access to project data is unauthorized. Cause: The Authorization parameter is not configured correctly, or the token is not bound to the access permission of the corresponding department, which does not comply with internal data control rules.
  • Scenario: The number of interface return results does not match expectations, or includes irrelevant project data. Cause: The query scope is not limited via the variables parameter, or the responseData field is not specified, resulting in redundant data returned beyond requirements.
  • Scenario: When connecting to external systems, a prompt appears that the message receiving address verification failed, and configuration cannot be completed. Cause: FastGPT is not deployed in an environment accessible via the public network, or SSL certificate verification rules are not configured according to docking requirements, which does not comply with the security verification requirements of external systems.

How to Confirm Configuration Is Complete

  • Initiate a limited-range HTTP request, and verify that the returned data fields match those specified in the responseData configuration.
  • Check whether the token carried in the Authorization parameter is bound to the department permission of the current test account, and confirm that there are no prompts related to unauthorized access.
  • Simulate network fluctuations in internal systems, call the interface multiple times, and confirm that the retry mechanism takes effect and there are no consecutive call failures.
  • When connecting to external systems, verify that the callback address can be normally accessed by the target system, and that the certificate verification rules meet docking requirements.

The values listed above are common starting points, and should be evaluated against specific samples in actual use.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.