القائمة
Use when configuring or choosing an LLM for a Cognigy agent — valid provider names (openAI, anthropic, azureOpenAI, google, mistral), model strings, connection types, and credential resolution.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when adding custom logic inside a Cognigy tool branch with manage_flow_nodes — supported node types, config schemas, placement rules, and the tool-first workflow.
Use when the user wants to add xApps to a Cognigy flow — interactive HTML / Adaptive Card mini-apps rendered to the user, with the submitted data flowing back into the conversation. Covers the xApp flow nodes, the Init Session rule, and accessing xApp data.
Add a new MCP tool (or extend an existing one) in the NiCE Cognigy Plugin's MCP server
Use when the user wants to build, create, or set up a new Cognigy AI Agent from scratch — covers listing projects, ensuring an LLM exists, creating the agent, testing it, and refining persona/job fields.
Use when the user wants to add knowledge, RAG, or a knowledge store/source to a Cognigy agent — covers embedding vs Knowledge Search models, Knowledge AI settings, ingesting sources, and attaching knowledge as a tool.
Use when exporting, importing, uploading, inspecting, or downloading Cognigy package zip files — including reusing an LLM plus its connection across projects.
| name | llm-providers |
| description | Use when configuring or choosing an LLM for a Cognigy agent — valid provider names (openAI, anthropic, azureOpenAI, google, mistral), model strings, connection types, and credential resolution. |
| provider | modelType examples | Connection type | Notes |
|---|---|---|---|
| openAI | gpt-4o, gpt-4o-mini, gpt-4.1, gpt-4.1-mini | OpenAIProvider | Requires apiKey (sk-...) |
| anthropic | claude-sonnet-4-0, claude-opus-4-0, claude-3-opus-20240229 | AnthropicProvider | Requires apiKey |
| azureOpenAI | gpt-4o (deployment name) | AzureOpenAIProviderV2 | Requires apiKey, may need connectionId with deployment config |
| gemini-2.0-flash, gemini-1.5-pro | GoogleVertexAIProvider | Requires apiKey | |
| mistral | mistral-small-2503, mistral-medium-latest | MistralProvider | Requires apiKey |
setup_llm can create different kinds of Cognigy LLM resources. The important distinction is the modelType:
gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet-4-0, gemini-2.0-flash, mistral-small-2503.text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002, luminous-embedding-128, amazon.titan-embed-text-v2:0, Pharia-1-Embedding-4608, gemini-embedding-001, custom-embedding-model.Chat/completion models are not embedding models. gpt-4o-mini is a chat model, not a valid embedding-model choice for knowledge-store indexing.
When using list_resources { resourceType: "llm_model", projectId }, inspect the returned modelType and select the model by its exact role. Do not infer that all LLMs are interchangeable.
After creating the model, setup_llm automatically tests the connection by sending a minimal probe to the provider. This catches invalid API keys, wrong model types, and misconfigured providers before they can break downstream flows.
connectionTest.isCredentialsValid: true and the provider's confirmation message.If the connection test cannot run in your environment (e.g., air-gapped setup, unsupported custom model provider), you can pass dangerouslySkipConnectionTest: true to skip validation. This is a last resort — it may leave a non-functional model reference that silently breaks agent conversations and knowledge stores. Always prefer fixing the root cause instead. Do not use this to work around a missing, invalid, or cross-project connection.