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"""KonkoAI chat wrapper."""
from __future__ import annotations
import logging
import os
from typing import (
Any,
Dict,
Iterator,
List,
Mapping,
Optional,
Set,
Tuple,
Union,
)
import requests
from langchain_core.messages import AIMessageChunk, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import Field, root_validator
from langchain.adapters.openai import convert_dict_to_message, convert_message_to_dict
from langchain.callbacks.manager import (
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel, _generate_from_stream
from langchain.chat_models.openai import _convert_delta_to_message_chunk
from langchain.utils import get_from_dict_or_env
DEFAULT_API_BASE = "https://api.konko.ai/v1"
DEFAULT_MODEL = "meta-llama/Llama-2-13b-chat-hf"
logger = logging.getLogger(__name__)
class ChatKonko(BaseChatModel):
"""`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.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"}
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
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
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to Konko completion API."""
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"] = get_from_dict_or_env(
values, "konko_api_key", "KONKO_API_KEY"
)
try:
import konko
except ImportError:
raise ValueError(
"Could not import konko python package. "
"Please install it with `pip install konko`."
)
try:
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 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,
"request_timeout": self.request_timeout,
"max_tokens": self.max_tokens,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
**self.model_kwargs,
}
@staticmethod
def get_available_models(
konko_api_key: Optional[str] = None,
openai_api_key: Optional[str] = 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 = os.environ["OPENAI_API_KEY"]
except KeyError:
pass # It's okay if it's not set, we just won't use it
# Try to retrieve the Konko API key if it's not passed as an argument
if not konko_api_key:
try:
konko_api_key = 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."
)
models_url = f"{konko_api_base}/models"
headers = {
"Authorization": f"Bearer {konko_api_key}",
}
if openai_api_key:
headers["X-OpenAI-Api-Key"] = openai_api_key
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"]}
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 _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
# Happens in streaming
continue
token_usage = output["token_usage"]
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {"token_usage": overall_token_usage, "model_name": self.model}
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__
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=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
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for res in response["choices"]:
message = convert_dict_to_message(res["message"])
gen = ChatGeneration(
message=message,
generation_info=dict(finish_reason=res.get("finish_reason")),
)
generations.append(gen)
token_usage = response.get("usage", {})
llm_output = {"token_usage": token_usage, "model_name": self.model}
return ChatResult(generations=generations, llm_output=llm_output)
@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"
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