from __future__ import annotations
from typing import Any, Dict, Iterator, List, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import LanguageModelInput
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.utils import get_from_dict_or_env, pre_init
from pydantic import BaseModel, SecretStr
from langchain_community.llms import BaseLLM
from langchain_community.utilities.vertexai import create_retry_decorator
[docs]
def completion_with_retry(
llm: GooglePalm,
prompt: LanguageModelInput,
is_gemini: bool = False,
stream: bool = False,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = create_retry_decorator(
llm, max_retries=llm.max_retries, run_manager=run_manager
)
@retry_decorator
def _completion_with_retry(
prompt: LanguageModelInput, is_gemini: bool, stream: bool, **kwargs: Any
) -> Any:
generation_config = kwargs.get("generation_config", {})
if is_gemini:
return llm.client.generate_content(
contents=prompt, stream=stream, generation_config=generation_config
)
return llm.client.generate_text(prompt=prompt, **kwargs)
return _completion_with_retry(
prompt=prompt, is_gemini=is_gemini, stream=stream, **kwargs
)
def _is_gemini_model(model_name: str) -> bool:
return "gemini" in model_name
def _strip_erroneous_leading_spaces(text: str) -> str:
"""Strip erroneous leading spaces from text.
The PaLM API will sometimes erroneously return a single leading space in all
lines > 1. This function strips that space.
"""
has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:])
if has_leading_space:
return text.replace("\n ", "\n")
else:
return text
[docs]
@deprecated("0.0.12", alternative_import="langchain_google_genai.GoogleGenerativeAI")
class GooglePalm(BaseLLM, BaseModel):
"""
DEPRECATED: Use `langchain_google_genai.GoogleGenerativeAI` instead.
Google PaLM models.
"""
client: Any #: :meta private:
google_api_key: Optional[SecretStr]
model_name: str = "models/text-bison-001"
"""Model name to use."""
temperature: float = 0.7
"""Run inference with this temperature. Must be in the closed interval
[0.0, 1.0]."""
top_p: Optional[float] = None
"""Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
top_k: Optional[int] = None
"""Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive."""
max_output_tokens: Optional[int] = None
"""Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64."""
n: int = 1
"""Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated."""
max_retries: int = 6
"""The maximum number of retries to make when generating."""
@property
def is_gemini(self) -> bool:
"""Returns whether a model is belongs to a Gemini family or not."""
return _is_gemini_model(self.model_name)
@property
def lc_secrets(self) -> Dict[str, str]:
return {"google_api_key": "GOOGLE_API_KEY"}
@classmethod
def is_lc_serializable(self) -> bool:
return True
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "llms", "google_palm"]
[docs]
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate api key, python package exists."""
google_api_key = get_from_dict_or_env(
values, "google_api_key", "GOOGLE_API_KEY"
)
model_name = values["model_name"]
try:
import google.generativeai as genai
if isinstance(google_api_key, SecretStr):
google_api_key = google_api_key.get_secret_value()
genai.configure(api_key=google_api_key)
if _is_gemini_model(model_name):
values["client"] = genai.GenerativeModel(model_name=model_name)
else:
values["client"] = genai
except ImportError:
raise ImportError(
"Could not import google-generativeai python package. "
"Please install it with `pip install google-generativeai`."
)
if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
raise ValueError("temperature must be in the range [0.0, 1.0]")
if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
raise ValueError("top_p must be in the range [0.0, 1.0]")
if values["top_k"] is not None and values["top_k"] <= 0:
raise ValueError("top_k must be positive")
if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0:
raise ValueError("max_output_tokens must be greater than zero")
return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations: List[List[Generation]] = []
generation_config = {
"stop_sequences": stop,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"max_output_tokens": self.max_output_tokens,
"candidate_count": self.n,
}
for prompt in prompts:
if self.is_gemini:
res = completion_with_retry(
self,
prompt=prompt,
stream=False,
is_gemini=True,
run_manager=run_manager,
generation_config=generation_config,
)
candidates = [
"".join([p.text for p in c.content.parts]) for c in res.candidates
]
generations.append([Generation(text=c) for c in candidates])
else:
res = completion_with_retry(
self,
model=self.model_name,
prompt=prompt,
stream=False,
is_gemini=False,
run_manager=run_manager,
**generation_config,
)
prompt_generations = []
for candidate in res.candidates:
raw_text = candidate["output"]
stripped_text = _strip_erroneous_leading_spaces(raw_text)
prompt_generations.append(Generation(text=stripped_text))
generations.append(prompt_generations)
return LLMResult(generations=generations)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
generation_config = kwargs.get("generation_config", {})
if stop:
generation_config["stop_sequences"] = stop
for stream_resp in completion_with_retry(
self,
prompt,
stream=True,
is_gemini=True,
run_manager=run_manager,
generation_config=generation_config,
**kwargs,
):
chunk = GenerationChunk(text=stream_resp.text)
if run_manager:
run_manager.on_llm_new_token(
stream_resp.text,
chunk=chunk,
verbose=self.verbose,
)
yield chunk
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "google_palm"
[docs]
def get_num_tokens(self, text: str) -> int:
"""Get the number of tokens present in the text.
Useful for checking if an input will fit in a model's context window.
Args:
text: The string input to tokenize.
Returns:
The integer number of tokens in the text.
"""
if self.is_gemini:
raise ValueError("Counting tokens is not yet supported!")
result = self.client.count_text_tokens(model=self.model_name, prompt=text)
return result["token_count"]
