import os
from typing import Any, Dict, List, Optional, cast
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import (
BaseChatModel,
)
from langchain_core.language_models.llms import BaseLLM
from langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import (
ChatGeneration,
ChatResult,
Generation,
LLMResult,
)
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_google_vertexai._base import _BaseVertexAIModelGarden
from langchain_google_vertexai._utils import enforce_stop_tokens
from langchain_google_vertexai.model_garden import VertexAIModelGarden
USER_CHAT_TEMPLATE = "<start_of_turn>user\n{prompt}<end_of_turn>\n"
MODEL_CHAT_TEMPLATE = "<start_of_turn>model\n{prompt}<end_of_turn>\n"
[docs]def gemma_messages_to_prompt(history: List[BaseMessage]) -> str:
"""Converts a list of messages to a chat prompt for Gemma."""
messages: List[str] = []
if len(messages) == 1:
content = cast(str, history[0].content)
if isinstance(history[0], SystemMessage):
raise ValueError("Gemma currently doesn't support system message!")
return content
for message in history:
content = cast(str, message.content)
if isinstance(message, SystemMessage):
raise ValueError("Gemma currently doesn't support system message!")
elif isinstance(message, AIMessage):
messages.append(MODEL_CHAT_TEMPLATE.format(prompt=content))
elif isinstance(message, HumanMessage):
messages.append(USER_CHAT_TEMPLATE.format(prompt=content))
else:
raise ValueError(f"Unexpected message with type {type(message)}")
messages.append("<start_of_turn>model\n")
return "".join(messages)
def _parse_gemma_chat_response(response: str) -> str:
"""Removes chat history from the response."""
pattern = "<start_of_turn>model\n"
pos = response.rfind(pattern)
if pos == -1:
return response
text = response[(pos + len(pattern)) :]
pos = text.find("<start_of_turn>user\n")
if pos > 0:
return text[:pos]
return text
class _GemmaBase(BaseModel):
max_tokens: Optional[int] = None
"""The maximum number of tokens to generate."""
temperature: Optional[float] = None
"""The temperature to use for sampling."""
top_p: Optional[float] = None
"""The top-p value to use for sampling."""
top_k: Optional[int] = None
"""The top-k value to use for sampling."""
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling gemma."""
params = {
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
}
return {k: v for k, v in params.items()}
def _get_params(self, **kwargs) -> Dict[str, Any]:
params = {k: kwargs.get(k, v) for k, v in self._default_params.items()}
return {k: v for k, v in params.items() if v is not None}
[docs]class GemmaVertexAIModelGarden(VertexAIModelGarden):
allowed_model_args: Optional[List[str]] = [
"temperature",
"top_p",
"top_k",
"max_tokens",
]
@property
def _llm_type(self) -> str:
return "gemma_vertexai_model_garden"
# Needed so that mypy doesn't flag missing aliased init args.
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
[docs]class GemmaChatVertexAIModelGarden(_GemmaBase, _BaseVertexAIModelGarden, BaseChatModel):
allowed_model_args: Optional[List[str]] = [
"temperature",
"top_p",
"top_k",
"max_tokens",
"max_length",
]
parse_response: bool = False
"""Whether to post-process the chat response and clean repeations """
"""or multi-turn statements."""
def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
"""Needed for mypy typing to recognize model_name as a valid arg."""
if model_name:
kwargs["model_name"] = model_name
super().__init__(**kwargs)
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
@property
def _llm_type(self) -> str:
return "gemma_vertexai_model_garden"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling gemma."""
# support both Gemma 1B and 2B
params = super()._default_params
params["max_length"] = self.max_tokens
return params
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
request = self._get_params(**kwargs)
request["prompt"] = gemma_messages_to_prompt(messages)
output = self.client.predict(endpoint=self.endpoint_path, instances=[request])
text = output.predictions[0]
if self.parse_response or kwargs.get("parse_response"):
text = _parse_gemma_chat_response(text)
if stop:
text = enforce_stop_tokens(text, stop)
generations = [
ChatGeneration(
message=AIMessage(content=text),
)
]
return ChatResult(generations=generations)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Top Level call"""
request = self._get_params(**kwargs)
request["prompt"] = gemma_messages_to_prompt(messages)
output = await self.async_client.predict(
endpoint=self.endpoint_path, instances=[request]
)
text = output.predictions[0]
if self.parse_response or kwargs.get("parse_response"):
text = _parse_gemma_chat_response(text)
if stop:
text = enforce_stop_tokens(text, stop)
generations = [
ChatGeneration(
message=AIMessage(content=text),
)
]
return ChatResult(generations=generations)
class _GemmaLocalKaggleBase(_GemmaBase):
"""Local gemma model loaded from Kaggle."""
client: Any = None #: :meta private:
keras_backend: str = "jax"
model_name: str = Field(default="gemma_2b_en", alias="model")
"""Gemma model name."""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
"""Needed for mypy typing to recognize model_name as a valid arg."""
if model_name:
kwargs["model_name"] = model_name
super().__init__(**kwargs)
@root_validator(pre=False, skip_on_failure=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that llama-cpp-python library is installed."""
try:
os.environ["KERAS_BACKEND"] = values["keras_backend"]
from keras_nlp.models import GemmaCausalLM # type: ignore
except ImportError:
raise ImportError(
"Could not import GemmaCausalLM library. "
"Please install the GemmaCausalLM library to "
"use this model: pip install keras-nlp keras>=3 kaggle"
)
values["client"] = GemmaCausalLM.from_preset(values["model_name"])
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling gemma."""
params = {"max_length": self.max_tokens}
return {k: v for k, v in params.items() if v is not None}
def _get_params(self, **kwargs) -> Dict[str, Any]:
mapping = {"max_tokens": "max_length"}
params = {mapping[k]: v for k, v in kwargs.items() if k in mapping}
return {**self._default_params, **params}
[docs]class GemmaLocalKaggle(_GemmaLocalKaggleBase, BaseLLM): # type: ignore
"""Local gemma chat model loaded from Kaggle."""
def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
"""Only needed for typing."""
if model_name:
kwargs["model_name"] = model_name
super().__init__(**kwargs)
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
params = self._get_params(**kwargs)
results = self.client.generate(prompts, **params)
results = [results] if isinstance(results, str) else results
if stop:
results = [enforce_stop_tokens(text, stop) for text in results]
return LLMResult(generations=[[Generation(text=result)] for result in results])
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "gemma_local_kaggle"
[docs]class GemmaChatLocalKaggle(_GemmaLocalKaggleBase, BaseChatModel): # type: ignore
parse_response: bool = False
"""Whether to post-process the chat response and clean repeations """
"""or multi-turn statements."""
def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
"""Needed for mypy typing to recognize model_name as a valid arg."""
if model_name:
kwargs["model_name"] = model_name
super().__init__(**kwargs)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
params = self._get_params(**kwargs)
prompt = gemma_messages_to_prompt(messages)
text = self.client.generate(prompt, **params)
if self.parse_response or kwargs.get("parse_response"):
text = _parse_gemma_chat_response(text)
if stop:
text = enforce_stop_tokens(text, stop)
generation = ChatGeneration(message=AIMessage(content=text))
return ChatResult(generations=[generation])
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "gemma_local_chat_kaggle"
class _GemmaLocalHFBase(_GemmaBase):
"""Local gemma model loaded from HuggingFace."""
tokenizer: Any = None #: :meta private:
client: Any = None #: :meta private:
hf_access_token: str
cache_dir: Optional[str] = None
model_name: str = Field(default="google/gemma-2b", alias="model")
"""Gemma model name."""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that llama-cpp-python library is installed."""
try:
from transformers import AutoTokenizer, GemmaForCausalLM # type: ignore
except ImportError:
raise ImportError(
"Could not import GemmaForCausalLM library. "
"Please install the GemmaForCausalLM library to "
"use this model: pip install transformers>=4.38.1"
)
values["tokenizer"] = AutoTokenizer.from_pretrained(
values["model_name"], token=values["hf_access_token"]
)
values["client"] = GemmaForCausalLM.from_pretrained(
values["model_name"],
token=values["hf_access_token"],
cache_dir=values["cache_dir"],
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling gemma."""
params = {"max_length": self.max_tokens}
return {k: v for k, v in params.items() if v is not None}
def _get_params(self, **kwargs) -> Dict[str, Any]:
mapping = {"max_tokens": "max_length"}
params = {mapping[k]: v for k, v in kwargs.items() if k in mapping}
return {**self._default_params, **params}
def _run(self, prompt: str, **kwargs: Any) -> str:
inputs = self.tokenizer(prompt, return_tensors="pt")
params = self._get_params(**kwargs)
generate_ids = self.client.generate(inputs.input_ids, **params)
return self.tokenizer.batch_decode(
generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
[docs]class GemmaLocalHF(_GemmaLocalHFBase, BaseLLM): # type: ignore
"""Local gemma model loaded from HuggingFace."""
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
results = [self._run(prompt, **kwargs) for prompt in prompts]
if stop:
results = [enforce_stop_tokens(text, stop) for text in results]
return LLMResult(generations=[[Generation(text=text)] for text in results])
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "gemma_local_hf"
[docs]class GemmaChatLocalHF(_GemmaLocalHFBase, BaseChatModel): # type: ignore
parse_response: bool = False
"""Whether to post-process the chat response and clean repeations """
"""or multi-turn statements."""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
prompt = gemma_messages_to_prompt(messages)
text = self._run(prompt, **kwargs)
if self.parse_response or kwargs.get("parse_response"):
text = _parse_gemma_chat_response(text)
if stop:
text = enforce_stop_tokens(text, stop)
generation = ChatGeneration(message=AIMessage(content=text))
return ChatResult(generations=[generation])
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "gemma_local_chat_hf"
