Source code for langchain_community.chat_models.javelin_ai_gateway

import logging
from typing import Any, Dict, List, Mapping, Optional, cast

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    ChatMessage,
    FunctionMessage,
    HumanMessage,
    SystemMessage,
)
from langchain_core.outputs import (
    ChatGeneration,
    ChatResult,
)
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, SecretStr

logger = logging.getLogger(__name__)


# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"  [call-arg]
[docs]class ChatParams(BaseModel, extra=Extra.allow): """Parameters for the `Javelin AI Gateway` LLM.""" temperature: float = 0.0 stop: Optional[List[str]] = None max_tokens: Optional[int] = None
[docs]class ChatJavelinAIGateway(BaseChatModel): """`Javelin AI Gateway` chat models API. To use, you should have the ``javelin_sdk`` python package installed. For more information, see https://docs.getjavelin.io Example: .. code-block:: python from langchain_community.chat_models import ChatJavelinAIGateway chat = ChatJavelinAIGateway( gateway_uri="<javelin-ai-gateway-uri>", route="<javelin-ai-gateway-chat-route>", params={ "temperature": 0.1 } ) """ route: str """The route to use for the Javelin AI Gateway API.""" gateway_uri: Optional[str] = None """The URI for the Javelin AI Gateway API.""" params: Optional[ChatParams] = None """Parameters for the Javelin AI Gateway LLM.""" client: Any """javelin client.""" javelin_api_key: Optional[SecretStr] = Field(None, alias="api_key") """The API key for the Javelin AI Gateway.""" class Config: """Configuration for this pydantic object.""" allow_population_by_field_name = True def __init__(self, **kwargs: Any): try: from javelin_sdk import ( JavelinClient, UnauthorizedError, ) except ImportError: raise ImportError( "Could not import javelin_sdk python package. " "Please install it with `pip install javelin_sdk`." ) super().__init__(**kwargs) if self.gateway_uri: try: self.client = JavelinClient( base_url=self.gateway_uri, api_key=cast(SecretStr, self.javelin_api_key).get_secret_value(), ) except UnauthorizedError as e: raise ValueError("Javelin: Incorrect API Key.") from e @property def _default_params(self) -> Dict[str, Any]: params: Dict[str, Any] = { "gateway_uri": self.gateway_uri, "javelin_api_key": cast(SecretStr, self.javelin_api_key).get_secret_value(), "route": self.route, **(self.params.dict() if self.params else {}), } return params def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: message_dicts = [ ChatJavelinAIGateway._convert_message_to_dict(message) for message in messages ] data: Dict[str, Any] = { "messages": message_dicts, **(self.params.dict() if self.params else {}), } resp = self.client.query_route(self.route, query_body=data) return ChatJavelinAIGateway._create_chat_result(resp.dict()) async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: message_dicts = [ ChatJavelinAIGateway._convert_message_to_dict(message) for message in messages ] data: Dict[str, Any] = { "messages": message_dicts, **(self.params.dict() if self.params else {}), } resp = await self.client.aquery_route(self.route, query_body=data) return ChatJavelinAIGateway._create_chat_result(resp.dict()) @property def _identifying_params(self) -> Dict[str, Any]: return self._default_params def _get_invocation_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> Dict[str, Any]: """Get the parameters used to invoke the model FOR THE CALLBACKS.""" return { **self._default_params, **super()._get_invocation_params(stop=stop, **kwargs), } @property def _llm_type(self) -> str: """Return type of chat model.""" return "javelin-ai-gateway-chat" @staticmethod def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: role = _dict["role"] content = _dict["content"] if role == "user": return HumanMessage(content=content) elif role == "assistant": return AIMessage(content=content) elif role == "system": return SystemMessage(content=content) else: return ChatMessage(content=content, role=role) @staticmethod def _raise_functions_not_supported() -> None: raise ValueError( "Function messages are not supported by the Javelin AI Gateway. Please" " create a feature request at https://docs.getjavelin.io" ) @staticmethod def _convert_message_to_dict(message: BaseMessage) -> dict: if isinstance(message, ChatMessage): message_dict = {"role": message.role, "content": message.content} elif isinstance(message, HumanMessage): message_dict = {"role": "user", "content": message.content} elif isinstance(message, AIMessage): message_dict = {"role": "assistant", "content": message.content} elif isinstance(message, SystemMessage): message_dict = {"role": "system", "content": message.content} elif isinstance(message, FunctionMessage): raise ValueError( "Function messages are not supported by the Javelin AI Gateway. Please" " create a feature request at https://docs.getjavelin.io" ) else: raise ValueError(f"Got unknown message type: {message}") if "function_call" in message.additional_kwargs: ChatJavelinAIGateway._raise_functions_not_supported() if message.additional_kwargs: logger.warning( "Additional message arguments are unsupported by Javelin AI Gateway " " and will be ignored: %s", message.additional_kwargs, ) return message_dict @staticmethod def _create_chat_result(response: Mapping[str, Any]) -> ChatResult: generations = [] for candidate in response["llm_response"]["choices"]: message = ChatJavelinAIGateway._convert_dict_to_message( candidate["message"] ) message_metadata = candidate.get("metadata", {}) gen = ChatGeneration( message=message, generation_info=dict(message_metadata), ) generations.append(gen) response_metadata = response.get("metadata", {}) return ChatResult(generations=generations, llm_output=response_metadata)