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334 | class RunGptLLM(LLM):
"""RunGPT LLM.
The opengpt of Jina AI models.
Examples:
`pip install llama-index-llms-rungpt`
```python
from llama_index.llms.rungpt import RunGptLLM
llm = RunGptLLM(model="rungpt", endpoint="0.0.0.0:51002")
response = llm.complete("What public transportation might be available in a city?")
print(str(response))
```
"""
model: Optional[str] = Field(
default=DEFAULT_RUNGPT_MODEL, description="The rungpt model to use."
)
endpoint: str = Field(description="The endpoint of serving address.")
temperature: float = Field(
default=DEFAULT_RUNGPT_TEMP,
description="The temperature to use for sampling.",
gte=0.0,
lte=1.0,
)
max_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="Max tokens model generates.",
gt=0,
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Replicate API."
)
base_url: str = Field(
description="The address of your target model served by rungpt."
)
def __init__(
self,
model: Optional[str] = DEFAULT_RUNGPT_MODEL,
endpoint: str = "0.0.0.0:51002",
temperature: float = DEFAULT_RUNGPT_TEMP,
max_tokens: Optional[int] = DEFAULT_NUM_OUTPUTS,
context_window: int = DEFAULT_CONTEXT_WINDOW,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
):
if endpoint.startswith("http://"):
base_url = endpoint
else:
base_url = "http://" + endpoint
super().__init__(
model=model,
endpoint=endpoint,
temperature=temperature,
max_tokens=max_tokens,
context_window=context_window,
additional_kwargs=additional_kwargs or {},
callback_manager=callback_manager or CallbackManager([]),
base_url=base_url,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "RunGptLLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_tokens,
model_name=self._model,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
try:
import requests
except ImportError:
raise ImportError(
"Could not import requests library."
"Please install requests with `pip install requests`"
)
response_gpt = requests.post(
self.base_url + "/generate",
json=self._request_pack("complete", prompt, **kwargs),
stream=False,
).json()
return CompletionResponse(
text=response_gpt["choices"][0]["text"],
additional_kwargs=response_gpt["usage"],
raw=response_gpt,
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
try:
import requests
except ImportError:
raise ImportError(
"Could not import requests library."
"Please install requests with `pip install requests`"
)
response_gpt = requests.post(
self.base_url + "/generate_stream",
json=self._request_pack("complete", prompt, **kwargs),
stream=True,
)
try:
import sseclient
except ImportError:
raise ImportError(
"Could not import sseclient-py library."
"Please install requests with `pip install sseclient-py`"
)
client = sseclient.SSEClient(response_gpt)
response_iter = client.events()
def gen() -> CompletionResponseGen:
text = ""
for item in response_iter:
item_dict = dict(item.data)
delta = item_dict["choices"][0]["text"]
additional_kwargs = item_dict["usage"]
text = text + self._space_handler(delta)
yield CompletionResponse(
text=text,
delta=delta,
raw=item_dict,
additional_kwargs=additional_kwargs,
)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
message_list = self._message_wrapper(messages)
try:
import requests
except ImportError:
raise ImportError(
"Could not import requests library."
"Please install requests with `pip install requests`"
)
response_gpt = requests.post(
self.base_url + "/chat",
json=self._request_pack("chat", message_list, **kwargs),
stream=False,
).json()
chat_message, _ = self._message_unpacker(response_gpt)
return ChatResponse(message=chat_message, raw=response_gpt)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
message_list = self._message_wrapper(messages)
try:
import requests
except ImportError:
raise ImportError(
"Could not import requests library."
"Please install requests with `pip install requests`"
)
response_gpt = requests.post(
self.base_url + "/chat_stream",
json=self._request_pack("chat", message_list, **kwargs),
stream=True,
)
try:
import sseclient
except ImportError:
raise ImportError(
"Could not import sseclient-py library."
"Please install requests with `pip install sseclient-py`"
)
client = sseclient.SSEClient(response_gpt)
chat_iter = client.events()
def gen() -> ChatResponseGen:
content = ""
for item in chat_iter:
item_dict = dict(item.data)
chat_message, delta = self._message_unpacker(item_dict)
content = content + self._space_handler(delta)
chat_message.content = content
yield ChatResponse(message=chat_message, raw=item_dict, delta=delta)
return gen()
@llm_chat_callback()
async def achat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
return self.chat(messages, **kwargs)
@llm_chat_callback()
async def astream_chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponseAsyncGen:
async def gen() -> ChatResponseAsyncGen:
for message in self.stream_chat(messages, **kwargs):
yield message
# NOTE: convert generator to async generator
return gen()
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return self.complete(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
async def gen() -> CompletionResponseAsyncGen:
for message in self.stream_complete(prompt, **kwargs):
yield message
return gen()
def _message_wrapper(self, messages: Sequence[ChatMessage]) -> List[Dict[str, Any]]:
message_list = []
for message in messages:
role = message.role.value
content = message.content
message_list.append({"role": role, "content": content})
return message_list
def _message_unpacker(
self, response_gpt: Dict[str, Any]
) -> Tuple[ChatMessage, str]:
message = response_gpt["choices"][0]["message"]
additional_kwargs = response_gpt["usage"]
role = message["role"]
content = message["content"]
key = MessageRole.SYSTEM
for r in MessageRole:
if r.value == role:
key = r
chat_message = ChatMessage(
role=key, content=content, additional_kwargs=additional_kwargs
)
return chat_message, content
def _request_pack(
self, mode: str, prompt: Union[str, List[Dict[str, Any]]], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if mode == "complete":
return {
"prompt": prompt,
"max_tokens": kwargs.pop("max_tokens", self.max_tokens),
"temperature": kwargs.pop("temperature", self.temperature),
"top_k": kwargs.pop("top_k", 50),
"top_p": kwargs.pop("top_p", 0.95),
"repetition_penalty": kwargs.pop("repetition_penalty", 1.2),
"do_sample": kwargs.pop("do_sample", False),
"echo": kwargs.pop("echo", True),
"n": kwargs.pop("n", 1),
"stop": kwargs.pop("stop", "."),
}
elif mode == "chat":
return {
"messages": prompt,
"max_tokens": kwargs.pop("max_tokens", self.max_tokens),
"temperature": kwargs.pop("temperature", self.temperature),
"top_k": kwargs.pop("top_k", 50),
"top_p": kwargs.pop("top_p", 0.95),
"repetition_penalty": kwargs.pop("repetition_penalty", 1.2),
"do_sample": kwargs.pop("do_sample", False),
"echo": kwargs.pop("echo", True),
"n": kwargs.pop("n", 1),
"stop": kwargs.pop("stop", "."),
}
return None
def _space_handler(self, word: str) -> str:
if word.isalnum():
return " " + word
return word
|