pydantic model llama_index.llms.litellm.LiteLLM

Show JSON schema
   "title": "LiteLLM",
   "description": "LLM interface.",
   "type": "object",
   "properties": {
      "callback_manager": {
         "title": "Callback Manager"
      "model": {
         "title": "Model",
         "description": "The LiteLLM model to use. For complete list of providers",
         "default": "gpt-3.5-turbo",
         "type": "string"
      "temperature": {
         "title": "Temperature",
         "description": "The temperature to use during generation.",
         "default": 0.1,
         "gte": 0.0,
         "lte": 1.0,
         "type": "number"
      "max_tokens": {
         "title": "Max Tokens",
         "description": "The maximum number of tokens to generate.",
         "exclusiveMinimum": 0,
         "type": "integer"
      "additional_kwargs": {
         "title": "Additional Kwargs",
         "description": "Additional kwargs for the LLM API.",
         "type": "object"
      "max_retries": {
         "title": "Max Retries",
         "description": "The maximum number of API retries.",
         "default": 10,
         "type": "integer"
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "litellm_llm"

  • arbitrary_types_allowed: bool = True

  • _validate_callback_manager » callback_manager

field additional_kwargs: Dict[str, Any] [Optional]

Additional kwargs for the LLM API.

field max_retries: int = 10

The maximum number of API retries.

field max_tokens: Optional[int] = None

The maximum number of tokens to generate.

  • exclusiveMinimum = 0

field model: str = 'gpt-3.5-turbo'

The LiteLLM model to use. For complete list of providers

field temperature: float = 0.1

The temperature to use during generation.

async achat(messages: Sequence[ChatMessage], **kwargs: Any) Any

Async chat endpoint for LLM.

async acomplete(*args: Any, **kwargs: Any) Any

Async completion endpoint for LLM.

async astream_chat(messages: Sequence[ChatMessage], **kwargs: Any) Any

Async streaming chat endpoint for LLM.

async astream_complete(*args: Any, **kwargs: Any) Any

Async streaming completion endpoint for LLM.

chat(messages: Sequence[ChatMessage], **kwargs: Any) Any

Chat endpoint for LLM.

classmethod class_name() str

Get the class name, used as a unique ID in serialization.

This provides a key that makes serialization robust against actual class name changes.

complete(*args: Any, **kwargs: Any) Any

Completion endpoint for LLM.

stream_chat(messages: Sequence[ChatMessage], **kwargs: Any) Any

Streaming chat endpoint for LLM.

stream_complete(*args: Any, **kwargs: Any) Any

Streaming completion endpoint for LLM.

property metadata: LLMMetadata

LLM metadata.