LlamaIndex connection¶

The llamaindex connection type configures access to LLM and embedding providers for LlamaIndex. It backs LlamaIndexHook (see Using LlamaIndex directly: LlamaIndexHook for hook usage and installation instructions).

Default Connection IDs¶

The LlamaIndexHook uses llamaindex_default by default.

Configuring the Connection¶

Embedding Model (Extra field): Default LlamaIndex embedding model name (e.g. text-embedding-3-small). This field appears as a dedicated input in the connection form (via conn-fields) and stores its value in extra["embed_model"].
LLM Model (Extra field): Default LlamaIndex LLM model name (e.g. gpt-5). This field appears as a dedicated input in the connection form (via conn-fields) and stores its value in extra["llm_model"].
API Key (Password field): The API key for your LLM/embedding provider, passed as api_key= to the LlamaIndex model constructor.
Host (optional): Optional base URL, passed as api_base= (for example, to point at an OpenAI-compatible proxy that serves official OpenAI model names).

The schema, port, and login fields are hidden in the connection form; they are not used by this connection type.

OpenAI models only¶

get_llm() and get_embedding_model() return LlamaIndex’s OpenAI and OpenAIEmbedding classes whatever host points at, and both classes check the model name against LlamaIndex’s own OpenAI model lists. Local or self-hosted servers (Ollama, vLLM and similar) are therefore not usable through this connection type unless they answer to an official OpenAI model name. For other vendors and for local models, build the LlamaIndex class in a @task and pass it to the operator’s embed_model= / llm= parameter; Using LlamaIndex directly: LlamaIndexHook explains the check and shows the pattern.

Model resolution order¶

Both get_embedding_model() and get_llm() resolve the model identifier from, in order:

The embed_model / llm_model constructor argument on LlamaIndexHook.
extra["embed_model"] / extra["llm_model"] on the connection.

If neither is set, the hook raises a ValueError when the model is needed.

Examples¶

OpenAI (embeddings and LLM)

{
    "conn_type": "llamaindex",
    "password": "sk-...",
    "extra": "{\"embed_model\": \"text-embedding-3-small\", \"llm_model\": \"gpt-5\"}"
}

LLM only (embeddings unset)

{
    "conn_type": "llamaindex",
    "password": "sk-...",
    "extra": "{\"llm_model\": \"gpt-5\"}"
}