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235 | class Gemini(CustomLLM):
"""
Gemini LLM.
Examples:
`pip install llama-index-llms-gemini`
```python
from llama_index.llms.gemini import Gemini
llm = Gemini(model="models/gemini-ultra", api_key="YOUR_API_KEY")
resp = llm.complete("Write a poem about a magic backpack")
print(resp)
```
"""
model: str = Field(default=GEMINI_MODELS[0], description="The Gemini model to use.")
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use during generation.",
ge=0.0,
le=1.0,
)
max_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The number of tokens to generate.",
gt=0,
)
generate_kwargs: dict = Field(
default_factory=dict, description="Kwargs for generation."
)
_model: "genai.GenerativeModel" = PrivateAttr()
_model_meta: "genai.types.Model" = PrivateAttr()
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = GEMINI_MODELS[0],
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = None,
generation_config: Optional["genai.types.GenerationConfigDict"] = None,
safety_settings: "genai.types.SafetySettingOptions" = None,
callback_manager: Optional[CallbackManager] = None,
api_base: Optional[str] = None,
transport: Optional[str] = None,
model_name: Optional[str] = None,
default_headers: Optional[Dict[str, str]] = None,
**generate_kwargs: Any,
):
"""Creates a new Gemini model interface."""
try:
import google.generativeai as genai
except ImportError:
raise ValueError(
"Gemini is not installed. Please install it with "
"`pip install 'google-generativeai>=0.3.0'`."
)
if model_name is not None:
warnings.warn(
"model_name is deprecated, please use model instead",
DeprecationWarning,
)
model = model_name
# API keys are optional. The API can be authorised via OAuth (detected
# environmentally) or by the GOOGLE_API_KEY environment variable.
config_params: Dict[str, Any] = {
"api_key": api_key or os.getenv("GOOGLE_API_KEY"),
}
if api_base:
config_params["client_options"] = {"api_endpoint": api_base}
if transport:
config_params["transport"] = transport
if default_headers:
default_metadata: Sequence[Dict[str, str]] = []
for key, value in default_headers.items():
default_metadata.append((key, value))
# `default_metadata` contains (key, value) pairs that will be sent with every request.
# When using `transport="rest"`, these will be sent as HTTP headers.
config_params["default_metadata"] = default_metadata
# transport: A string, one of: [`rest`, `grpc`, `grpc_asyncio`].
genai.configure(**config_params)
base_gen_config = generation_config if generation_config else {}
# Explicitly passed args take precedence over the generation_config.
final_gen_config = {"temperature": temperature, **base_gen_config}
model_meta = genai.get_model(model)
genai_model = genai.GenerativeModel(
model_name=model,
generation_config=final_gen_config,
safety_settings=safety_settings,
)
supported_methods = model_meta.supported_generation_methods
if "generateContent" not in supported_methods:
raise ValueError(
f"Model {model} does not support content generation, only "
f"{supported_methods}."
)
if not max_tokens:
max_tokens = model_meta.output_token_limit
else:
max_tokens = min(max_tokens, model_meta.output_token_limit)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
generate_kwargs=generate_kwargs,
callback_manager=callback_manager,
)
self._model_meta = model_meta
self._model = genai_model
@classmethod
def class_name(cls) -> str:
return "Gemini_LLM"
@property
def metadata(self) -> LLMMetadata:
total_tokens = self._model_meta.input_token_limit + self.max_tokens
return LLMMetadata(
context_window=total_tokens,
num_output=self.max_tokens,
model_name=self.model,
is_chat_model=True,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
result = self._model.generate_content(prompt, **kwargs)
return completion_from_gemini_response(result)
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
it = self._model.generate_content(prompt, stream=True, **kwargs)
yield from map(completion_from_gemini_response, it)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
merged_messages = merge_neighboring_same_role_messages(messages)
*history, next_msg = map(chat_message_to_gemini, merged_messages)
chat = self._model.start_chat(history=history)
response = chat.send_message(next_msg)
return chat_from_gemini_response(response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
merged_messages = merge_neighboring_same_role_messages(messages)
*history, next_msg = map(chat_message_to_gemini, merged_messages)
chat = self._model.start_chat(history=history)
response = chat.send_message(next_msg, stream=True)
def gen() -> ChatResponseGen:
content = ""
for r in response:
top_candidate = r.candidates[0]
content_delta = top_candidate.content.parts[0].text
role = ROLES_FROM_GEMINI[top_candidate.content.role]
raw = {
**(type(top_candidate).to_dict(top_candidate)),
**(
type(response.prompt_feedback).to_dict(response.prompt_feedback)
),
}
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=raw,
)
return gen()
|
