mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
1cbab0ebda
Description: you don't need to pass a version for Replicate official
models. That was broken on LangChain until now!
You can now run:
```
llm = Replicate(
model="meta/meta-llama-3-8b-instruct",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
llm(prompt)
```
I've updated the replicate.ipynb to reflect that.
twitter: @charliebholtz
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
232 lines
8.2 KiB
Python
232 lines
8.2 KiB
Python
from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.outputs import GenerationChunk
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from langchain_core.pydantic_v1 import Extra, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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if TYPE_CHECKING:
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from replicate.prediction import Prediction
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logger = logging.getLogger(__name__)
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class Replicate(LLM):
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"""Replicate models.
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To use, you should have the ``replicate`` python package installed,
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and the environment variable ``REPLICATE_API_TOKEN`` set with your API token.
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You can find your token here: https://replicate.com/account
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The model param is required, but any other model parameters can also
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be passed in with the format model_kwargs={model_param: value, ...}
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Example:
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.. code-block:: python
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from langchain_community.llms import Replicate
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replicate = Replicate(
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model=(
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"stability-ai/stable-diffusion: "
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"27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
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),
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model_kwargs={"image_dimensions": "512x512"}
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)
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"""
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model: str
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model_kwargs: Dict[str, Any] = Field(default_factory=dict, alias="input")
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replicate_api_token: Optional[str] = None
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prompt_key: Optional[str] = None
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version_obj: Any = Field(default=None, exclude=True)
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"""Optionally pass in the model version object during initialization to avoid
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having to make an extra API call to retrieve it during streaming. NOTE: not
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serializable, is excluded from serialization.
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"""
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streaming: bool = False
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"""Whether to stream the results."""
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stop: List[str] = Field(default_factory=list)
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"""Stop sequences to early-terminate generation."""
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class Config:
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"""Configuration for this pydantic config."""
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allow_population_by_field_name = True
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extra = Extra.forbid
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"replicate_api_token": "REPLICATE_API_TOKEN"}
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@classmethod
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def is_lc_serializable(cls) -> bool:
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return True
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "llms", "replicate"]
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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input = values.pop("input", {})
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if input:
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logger.warning(
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"Init param `input` is deprecated, please use `model_kwargs` instead."
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)
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extra = {**values.pop("model_kwargs", {}), **input}
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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replicate_api_token = get_from_dict_or_env(
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values, "replicate_api_token", "REPLICATE_API_TOKEN"
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)
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values["replicate_api_token"] = replicate_api_token
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return values
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"model": self.model,
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"model_kwargs": self.model_kwargs,
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of model."""
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return "replicate"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call to replicate endpoint."""
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if self.streaming:
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completion: Optional[str] = None
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for chunk in self._stream(
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prompt, stop=stop, run_manager=run_manager, **kwargs
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):
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if completion is None:
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completion = chunk.text
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else:
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completion += chunk.text
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else:
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prediction = self._create_prediction(prompt, **kwargs)
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prediction.wait()
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if prediction.status == "failed":
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raise RuntimeError(prediction.error)
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if isinstance(prediction.output, str):
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completion = prediction.output
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else:
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completion = "".join(prediction.output)
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assert completion is not None
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stop_conditions = stop or self.stop
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for s in stop_conditions:
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if s in completion:
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completion = completion[: completion.find(s)]
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return completion
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def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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prediction = self._create_prediction(prompt, **kwargs)
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stop_conditions = stop or self.stop
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stop_condition_reached = False
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current_completion: str = ""
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for output in prediction.output_iterator():
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current_completion += output
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# test for stop conditions, if specified
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for s in stop_conditions:
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if s in current_completion:
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prediction.cancel()
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stop_condition_reached = True
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# Potentially some tokens that should still be yielded before ending
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# stream.
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stop_index = max(output.find(s), 0)
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output = output[:stop_index]
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if not output:
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break
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if output:
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if run_manager:
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run_manager.on_llm_new_token(
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output,
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verbose=self.verbose,
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)
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yield GenerationChunk(text=output)
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if stop_condition_reached:
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break
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def _create_prediction(self, prompt: str, **kwargs: Any) -> Prediction:
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try:
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import replicate as replicate_python
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except ImportError:
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raise ImportError(
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"Could not import replicate python package. "
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"Please install it with `pip install replicate`."
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)
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# get the model and version
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if self.version_obj is None:
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if ":" in self.model:
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model_str, version_str = self.model.split(":")
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model = replicate_python.models.get(model_str)
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self.version_obj = model.versions.get(version_str)
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else:
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model = replicate_python.models.get(self.model)
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self.version_obj = model.latest_version
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if self.prompt_key is None:
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# sort through the openapi schema to get the name of the first input
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input_properties = sorted(
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self.version_obj.openapi_schema["components"]["schemas"]["Input"][
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"properties"
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].items(),
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key=lambda item: item[1].get("x-order", 0),
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)
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self.prompt_key = input_properties[0][0]
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input_: Dict = {
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self.prompt_key: prompt,
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**self.model_kwargs,
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**kwargs,
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}
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# if it's an official model
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if ":" not in self.model:
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return replicate_python.models.predictions.create(self.model, input=input_)
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else:
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return replicate_python.predictions.create(
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version=self.version_obj, input=input_
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)
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