Source code for langchain_community.chat_models.konko

"""KonkoAI chat wrapper."""
from __future__ import annotations

import logging
import os
import warnings
from typing import (
    Any,
    Dict,
    Iterator,
    List,
    Optional,
    Set,
    Tuple,
    Union,
    cast,
)

import requests
from langchain_core.callbacks import (
    CallbackManagerForLLMRun,
)
from langchain_core.messages import AIMessageChunk, BaseMessage
from langchain_core.outputs import ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_community.adapters.openai import (
    convert_message_to_dict,
)
from langchain_community.chat_models.openai import (
    ChatOpenAI,
    _convert_delta_to_message_chunk,
    generate_from_stream,
)
from langchain_community.utils.openai import is_openai_v1

DEFAULT_API_BASE = "https://api.konko.ai/v1"
DEFAULT_MODEL = "meta-llama/Llama-2-13b-chat-hf"

logger = logging.getLogger(__name__)


[docs]class ChatKonko(ChatOpenAI): """`ChatKonko` Chat large language models API. To use, you should have the ``konko`` python package installed, and the environment variable ``KONKO_API_KEY`` and ``OPENAI_API_KEY`` set with your API key. Any parameters that are valid to be passed to the konko.create call can be passed in, even if not explicitly saved on this class. Example: .. code-block:: python from langchain_community.chat_models import ChatKonko llm = ChatKonko(model="meta-llama/Llama-2-13b-chat-hf") """ @property def lc_secrets(self) -> Dict[str, str]: return {"konko_api_key": "KONKO_API_KEY", "openai_api_key": "OPENAI_API_KEY"}
[docs] @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return False
client: Any = None #: :meta private: model: str = Field(default=DEFAULT_MODEL, alias="model") """Model name to use.""" temperature: float = 0.7 """What sampling temperature to use.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" openai_api_key: Optional[str] = None konko_api_key: Optional[str] = None max_retries: int = 6 """Maximum number of retries to make when generating.""" streaming: bool = False """Whether to stream the results or not.""" n: int = 1 """Number of chat completions to generate for each prompt.""" max_tokens: int = 20 """Maximum number of tokens to generate.""" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["konko_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "konko_api_key", "KONKO_API_KEY") ) try: import konko except ImportError: raise ImportError( "Could not import konko python package. " "Please install it with `pip install konko`." ) try: if is_openai_v1(): values["client"] = konko.chat.completions else: values["client"] = konko.ChatCompletion except AttributeError: raise ValueError( "`konko` has no `ChatCompletion` attribute, this is likely " "due to an old version of the konko package. Try upgrading it " "with `pip install --upgrade konko`." ) if not hasattr(konko, "_is_legacy_openai"): warnings.warn( "You are using an older version of the 'konko' package. " "Please consider upgrading to access new features." ) if values["n"] < 1: raise ValueError("n must be at least 1.") if values["n"] > 1 and values["streaming"]: raise ValueError("n must be 1 when streaming.") return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Konko API.""" return { "model": self.model, "max_tokens": self.max_tokens, "stream": self.streaming, "n": self.n, "temperature": self.temperature, **self.model_kwargs, }
[docs] @staticmethod def get_available_models( konko_api_key: Union[str, SecretStr, None] = None, openai_api_key: Union[str, SecretStr, None] = None, konko_api_base: str = DEFAULT_API_BASE, ) -> Set[str]: """Get available models from Konko API.""" # Try to retrieve the OpenAI API key if it's not passed as an argument if not openai_api_key: try: openai_api_key = convert_to_secret_str(os.environ["OPENAI_API_KEY"]) except KeyError: pass # It's okay if it's not set, we just won't use it elif isinstance(openai_api_key, str): openai_api_key = convert_to_secret_str(openai_api_key) # Try to retrieve the Konko API key if it's not passed as an argument if not konko_api_key: try: konko_api_key = convert_to_secret_str(os.environ["KONKO_API_KEY"]) except KeyError: raise ValueError( "Konko API key must be passed as keyword argument or " "set in environment variable KONKO_API_KEY." ) elif isinstance(konko_api_key, str): konko_api_key = convert_to_secret_str(konko_api_key) models_url = f"{konko_api_base}/models" headers = { "Authorization": f"Bearer {konko_api_key.get_secret_value()}", } if openai_api_key: headers["X-OpenAI-Api-Key"] = cast( SecretStr, openai_api_key ).get_secret_value() models_response = requests.get(models_url, headers=headers) if models_response.status_code != 200: raise ValueError( f"Error getting models from {models_url}: " f"{models_response.status_code}" ) return {model["id"] for model in models_response.json()["data"]}
[docs] def completion_with_retry( self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any ) -> Any: def _completion_with_retry(**kwargs: Any) -> Any: return self.client.create(**kwargs) return _completion_with_retry(**kwargs)
def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class = AIMessageChunk for chunk in self.completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] chunk = _convert_delta_to_message_chunk( choice["delta"], default_chunk_class ) finish_reason = choice.get("finish_reason") generation_info = ( dict(finish_reason=finish_reason) if finish_reason is not None else None ) default_chunk_class = chunk.__class__ cg_chunk = ChatGenerationChunk( message=chunk, generation_info=generation_info ) if run_manager: run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk) yield cg_chunk def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: should_stream = stream if stream is not None else self.streaming if should_stream: stream_iter = self._stream( messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} response = self.completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) def _create_message_dicts( self, messages: List[BaseMessage], stop: Optional[List[str]] ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: params = self._client_params if stop is not None: if "stop" in params: raise ValueError("`stop` found in both the input and default params.") params["stop"] = stop message_dicts = [convert_message_to_dict(m) for m in messages] return message_dicts, params @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**{"model_name": self.model}, **self._default_params} @property def _client_params(self) -> Dict[str, Any]: """Get the parameters used for the konko client.""" 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.""" return { "model": self.model, **super()._get_invocation_params(stop=stop), **self._default_params, **kwargs, } @property def _llm_type(self) -> str: """Return type of chat model.""" return "konko-chat"