Gradient Model Adapter

pydantic model llama_index.llms.gradient.GradientModelAdapterLLM

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{
   "title": "GradientModelAdapterLLM",
   "description": "Simple abstract base class for custom LLMs.\n\nSubclasses must implement the `__init__`, `_complete`,\n    `_stream_complete`, and `metadata` methods.",
   "type": "object",
   "properties": {
      "callback_manager": {
         "title": "Callback Manager"
      },
      "system_prompt": {
         "title": "System Prompt",
         "description": "System prompt for LLM calls.",
         "type": "string"
      },
      "messages_to_prompt": {
         "title": "Messages To Prompt"
      },
      "completion_to_prompt": {
         "title": "Completion To Prompt"
      },
      "output_parser": {
         "title": "Output Parser"
      },
      "pydantic_program_mode": {
         "default": "default",
         "allOf": [
            {
               "$ref": "#/definitions/PydanticProgramMode"
            }
         ]
      },
      "query_wrapper_prompt": {
         "title": "Query Wrapper Prompt"
      },
      "max_tokens": {
         "title": "Max Tokens",
         "description": "The number of tokens to generate.",
         "default": 256,
         "exclusiveMinimum": 0,
         "exclusiveMaximum": 512,
         "type": "integer"
      },
      "access_token": {
         "title": "Access Token",
         "description": "The Gradient access token to use.",
         "type": "string"
      },
      "host": {
         "title": "Host",
         "description": "The url of the Gradient service to access.",
         "type": "string"
      },
      "workspace_id": {
         "title": "Workspace Id",
         "description": "The Gradient workspace id to use.",
         "type": "string"
      },
      "is_chat_model": {
         "title": "Is Chat Model",
         "description": "Whether the model is a chat model.",
         "default": false,
         "type": "boolean"
      },
      "model_adapter_id": {
         "title": "Model Adapter Id",
         "description": "The id of the model adapter to use.",
         "type": "string"
      },
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "custom_llm"
      }
   },
   "required": [
      "model_adapter_id"
   ],
   "definitions": {
      "PydanticProgramMode": {
         "title": "PydanticProgramMode",
         "description": "Pydantic program mode.",
         "enum": [
            "default",
            "openai",
            "llm",
            "guidance",
            "lm-format-enforcer"
         ],
         "type": "string"
      }
   }
}

Config

• arbitrary_types_allowed: bool = True

Fields

• access_token (Optional[str])

• callback_manager (llama_index.callbacks.base.CallbackManager)

• completion_to_prompt (llama_index.llms.llm.CompletionToPromptType)

• host (Optional[str])

• is_chat_model (bool)

• max_tokens (Optional[int])

• messages_to_prompt (llama_index.llms.llm.MessagesToPromptType)

• model_adapter_id (str)

• output_parser (Optional[llama_index.types.BaseOutputParser])

• pydantic_program_mode (llama_index.types.PydanticProgramMode)

• query_wrapper_prompt (Optional[llama_index.prompts.base.BasePromptTemplate])

• system_prompt (Optional[str])

• workspace_id (Optional[str])

Validators

• _validate_callback_manager » callback_manager

• set_completion_to_prompt » completion_to_prompt

• set_messages_to_prompt » messages_to_prompt

field access_token: Optional[str] = None

The Gradient access token to use.

field host: Optional[str] = None

The url of the Gradient service to access.

field is_chat_model: bool = False

Whether the model is a chat model.

field max_tokens: Optional[int] = 256

The number of tokens to generate.

Constraints

• exclusiveMinimum = 0

• exclusiveMaximum = 512

field model_adapter_id: str [Required]

The id of the model adapter to use.

field workspace_id: Optional[str] = None

The Gradient workspace id to use.

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

Async completion endpoint for LLM.

close() → None

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

Completion endpoint for LLM.

stream_complete(prompt: str, formatted: bool = False, **kwargs: Any) → Generator[CompletionResponse, None, None]

Streaming completion endpoint for LLM.

property metadata: LLMMetadata

LLM metadata.