Source code for langchain_community.chat_models.dappier

from typing import Any, Dict, List, Optional, Union

from aiohttp import ClientSession
from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import (
    BaseChatModel,
)
from langchain_core.messages import (
    AIMessage,
    BaseMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.pydantic_v1 import Extra, Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_community.utilities.requests import Requests


def _format_dappier_messages(
    messages: List[BaseMessage],
) -> List[Dict[str, Union[str, List[Union[str, Dict[Any, Any]]]]]]:
    formatted_messages = []

    for message in messages:
        if message.type == "human":
            formatted_messages.append({"role": "user", "content": message.content})
        elif message.type == "system":
            formatted_messages.append({"role": "system", "content": message.content})

    return formatted_messages


[docs]class ChatDappierAI(BaseChatModel): """`Dappier` chat large language models. `Dappier` is a platform enabling access to diverse, real-time data models. Enhance your AI applications with Dappier's pre-trained, LLM-ready data models and ensure accurate, current responses with reduced inaccuracies. To use one of our Dappier AI Data Models, you will need an API key. Please visit Dappier Platform (https://platform.dappier.com/) to log in and create an API key in your profile. Example: .. code-block:: python from langchain_community.chat_models import ChatDappierAI from langchain_core.messages import HumanMessage # Initialize `ChatDappierAI` with the desired configuration chat = ChatDappierAI( dappier_endpoint="https://api.dappier.com/app/datamodel/dm_01hpsxyfm2fwdt2zet9cg6fdxt", dappier_api_key="<YOUR_KEY>") # Create a list of messages to interact with the model messages = [HumanMessage(content="hello")] # Invoke the model with the provided messages chat.invoke(messages) you can find more details here : https://docs.dappier.com/introduction""" dappier_endpoint: str = "https://api.dappier.com/app/datamodelconversation" dappier_model: str = "dm_01hpsxyfm2fwdt2zet9cg6fdxt" dappier_api_key: Optional[SecretStr] = Field(None, description="Dappier API Token") class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key exists in environment.""" values["dappier_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "dappier_api_key", "DAPPIER_API_KEY") ) return values
[docs] @staticmethod def get_user_agent() -> str: from langchain_community import __version__ return f"langchain/{__version__}"
@property def _llm_type(self) -> str: """Return type of chat model.""" return "dappier-realtimesearch-chat" @property def _api_key(self) -> str: if self.dappier_api_key: return self.dappier_api_key.get_secret_value() return "" def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: url = f"{self.dappier_endpoint}" headers = { "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } user_query = _format_dappier_messages(messages=messages) payload: Dict[str, Any] = { "model": self.dappier_model, "conversation": user_query, } request = Requests(headers=headers) response = request.post(url=url, data=payload) response.raise_for_status() data = response.json() message_response = data["message"] return ChatResult( generations=[ChatGeneration(message=AIMessage(content=message_response))] ) async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: url = f"{self.dappier_endpoint}" headers = { "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } user_query = _format_dappier_messages(messages=messages) payload: Dict[str, Any] = { "model": self.dappier_model, "conversation": user_query, } async with ClientSession() as session: async with session.post(url, json=payload, headers=headers) as response: response.raise_for_status() data = await response.json() message_response = data["message"] return ChatResult( generations=[ ChatGeneration(message=AIMessage(content=message_response)) ] )