Settings

Settings Guide

Manage project-level settings in Cognigy via the manage_settings tool.

Voice Preview Settings

Configure a speech provider so voice endpoints (WebRTC) can synthesize and recognize speech.

Quick Start

{
  "operation": "set_voice_preview",
  "projectId": "<24-char hex>",
  "provider": "microsoft"
}

This auto-detects an existing speech connection for the provider. If none is found, you'll get instructions to upload a package containing one.

With explicit connection

{
  "operation": "set_voice_preview",
  "projectId": "<24-char hex>",
  "provider": "microsoft",
  "connectionId": "<connection referenceId>"
}

Supported Providers

No speech connection found?

Speech connections are typically installed via Cognigy packages. To add one:

1. manage_packages { operation: "upload_and_inspect", projectId, filePath: "<path to package.zip>" }
2. manage_packages { operation: "import", projectId, packageId }
3. Retry manage_settings { operation: "set_voice_preview", projectId, provider }

Typical Full Workflow

1. create_ai_agent → get projectId
2. setup_llm → configure LLM
3. manage_settings { operation: "set_voice_preview", projectId, provider: "microsoft" } → configure speech
4. manage_voice_gateway { projectId, flowId } → create voice endpoint with WebRTC

Knowledge AI Settings

Configure the project-level settings used by Knowledge Search and document parsing.

This is separate from the embedding model used by manage_knowledge to build the knowledge-store index. Do not confuse these settings:

• Embedding model: required for the knowledge store itself
• Knowledge Search model: configured here via knowledgeSearchModelId
• answerExtractionModelId is also supported by the tool, but it is usually not needed for normal AI-agent knowledge-store setups.
• knowledgeSearchModelId must reference an llm_model from the same project
• The accepted model type for knowledgeSearchModelId is instance-dependent

Quick Start

{
  "operation": "set_knowledge_ai",
  "projectId": "<24-char hex>",
  "knowledgeSearchModelId": "<llm referenceId>",
  "contentParser": "default"
}

If you provide knowledgeSearchModelId or answerExtractionModelId, the tool automatically enables generative AI settings for the project.

With Azure Content Parser

{
  "operation": "set_knowledge_ai",
  "projectId": "<24-char hex>",
  "contentParser": "azure",
  "azureDIConnectionId": "<connection referenceId>"
}

Fields

Important Notes

• Knowledge AI model IDs must come from the SAME project. Use list_resources { resourceType: "llm_model", projectId } to find them.
• For Knowledge Search, prefer list_resources { resourceType: "llm_model", projectId, useCase: "knowledgeSearch" } so the candidate set matches the Settings UI dropdown.
• knowledgeSearchModelId is not the embedding-model field used by manage_knowledge
• Keep the AI Agent model and knowledgeSearchModelId separate. The response model you want for the agent is not a reason to test that same model for Knowledge Search.
• Treat model names in guides as examples only. The use-case-filtered LLM list and API validation are the source of truth.
• In normal AI-agent knowledge flows, set these settings before manage_knowledge { operation: "create_store", ... }
• If reusing another project's setup, import the exact source-project Knowledge Search model into the target project before the first set_knowledge_ai attempt
• If multiple required imported models share one connection, transfer that connection once together with all of those models instead of importing it again later
• If the exact source-project Knowledge Search model is still missing from the target project, stop and import it before trying a different model here

Typical Knowledge Workflow

1. Ensure the target project has an embedding model for the knowledge store, and if reusing another project, bring over the exact source-project Knowledge Search model before guessing with another candidate
2. manage_settings { operation: "set_knowledge_ai", projectId, knowledgeSearchModelId, contentParser }
3. manage_knowledge { operation: "create_store", projectId, name }
4. create_tool { toolType: "knowledge", ... } or create_ai_agent { knowledgeStoreReferenceId }