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385 | class Maritalk(LLM):
"""Maritalk LLM.
Examples:
`pip install llama-index-llms-maritalk`
```python
from llama_index.core.llms import ChatMessage
from llama_index.llms.maritalk import Maritalk
# To customize your API key, do this
# otherwise it will lookup MARITALK_API_KEY from your env variable
# llm = Maritalk(api_key="<your_maritalk_api_key>")
llm = Maritalk()
# Call chat with a list of messages
messages = [
ChatMessage(
role="system",
content="You are an assistant specialized in suggesting pet names. Given the animal, you must suggest 4 names.",
),
ChatMessage(role="user", content="I have a dog."),
]
response = llm.chat(messages)
print(response)
```
"""
api_key: str = Field(
default=None,
description="Your MariTalk API key.",
)
model: str = Field(
default="sabia-2-medium",
description="Chose one of the available models:\n"
"- `sabia-2-medium`\n"
"- `sabia-2-small`\n"
"- `maritalk-2024-01-08`",
)
temperature: float = Field(
default=0.7,
gt=0.0,
lt=1.0,
description="Run inference with this temperature. Must be in the"
"closed interval [0.0, 1.0].",
)
max_tokens: int = Field(
default=512,
gt=0,
description="The maximum number of tokens to" "generate in the reply.",
)
do_sample: bool = Field(
default=True,
description="Whether or not to use sampling; use `True` to enable.",
)
top_p: float = Field(
default=0.95,
gt=0.0,
lt=1.0,
description="Nucleus sampling parameter controlling the size of"
" the probability mass considered for sampling.",
)
_endpoint: str = PrivateAttr("https://chat.maritaca.ai/api/chat/inference")
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
# If an API key is not provided during instantiation,
# fall back to the MARITALK_API_KEY environment variable
self.api_key = self.api_key or os.getenv("MARITALK_API_KEY")
if not self.api_key:
raise ValueError(
"An API key must be provided or set in the "
"'MARITALK_API_KEY' environment variable."
)
@classmethod
def class_name(cls) -> str:
return "Maritalk"
def parse_messages_for_model(
self, messages: Sequence[ChatMessage]
) -> List[Dict[str, Union[str, List[Union[str, Dict[Any, Any]]]]]]:
"""
Parses messages from LlamaIndex's format to the format expected by
the MariTalk API.
Parameters:
messages (Sequence[ChatMessage]): A list of messages in LlamaIndex
format to be parsed.
Returns:
A list of messages formatted for the MariTalk API.
"""
formatted_messages = []
for message in messages:
if message.role.value == MessageRole.USER:
role = "user"
elif message.role.value == MessageRole.ASSISTANT:
role = "assistant"
elif message.role.value == MessageRole.SYSTEM:
role = "system"
formatted_messages.append({"role": role, "content": message.content})
return formatted_messages
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
model_name="maritalk",
context_window=self.max_tokens,
is_chat_model=True,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
# Prepare the data payload for the Maritalk API
formatted_messages = self.parse_messages_for_model(messages)
data = {
"model": self.model,
"messages": formatted_messages,
"do_sample": self.do_sample,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
**kwargs,
}
headers = {"authorization": f"Key {self.api_key}"}
response = requests.post(self._endpoint, json=data, headers=headers)
if response.ok:
answer = response.json().get("answer", "No answer found")
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=answer),
raw=response.json(),
)
else:
raise MaritalkHTTPError(response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
try:
import httpx
# Prepare the data payload for the Maritalk API
formatted_messages = self.parse_messages_for_model(messages)
data = {
"model": self.model,
"messages": formatted_messages,
"do_sample": self.do_sample,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
**kwargs,
}
headers = {"authorization": f"Key {self.api_key}"}
async with httpx.AsyncClient() as client:
response = await client.post(
self._endpoint, json=data, headers=headers, timeout=None
)
if response.status_code == 200:
answer = response.json().get("answer", "No answer found")
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=answer),
raw=response.json(),
)
else:
raise MaritalkHTTPError(response)
except ImportError:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`."
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
acomplete_fn = achat_to_completion_decorator(self.achat)
return await acomplete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
# Prepare the data payload for the Maritalk API
formatted_messages = self.parse_messages_for_model(messages)
data = {
"model": self.model,
"messages": formatted_messages,
"do_sample": self.do_sample,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"stream": True,
**kwargs,
}
headers = {"authorization": f"Key {self.api_key}"}
def gen() -> ChatResponseGen:
response = requests.post(
self._endpoint, json=data, headers=headers, stream=True
)
if response.ok:
content = ""
for line in response.iter_lines():
if line.startswith(b"data: "):
response_data = line.replace(b"data: ", b"").decode("utf-8")
if response_data:
parsed_data = json.loads(response_data)
if "text" in parsed_data:
content_delta = parsed_data["text"]
content += content_delta
yield ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=content
),
delta=content_delta,
raw=parsed_data,
)
else:
raise MaritalkHTTPError(response)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
try:
import httpx
# Prepare the data payload for the Maritalk API
formatted_messages = self.parse_messages_for_model(messages)
data = {
"model": self.model,
"messages": formatted_messages,
"do_sample": self.do_sample,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"stream": True,
**kwargs,
}
headers = {"authorization": f"Key {self.api_key}"}
async def gen() -> ChatResponseAsyncGen:
async with httpx.AsyncClient() as client:
async with client.stream(
"POST",
self._endpoint,
data=json.dumps(data),
headers=headers,
timeout=None,
) as response:
if response.status_code == 200:
content = ""
async for line in response.aiter_lines():
if line.startswith("data: "):
response_data = line.replace("data: ", "")
if response_data:
parsed_data = json.loads(response_data)
if "text" in parsed_data:
content_delta = parsed_data["text"]
content += content_delta
yield ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
content=content,
),
delta=content_delta,
raw=parsed_data,
)
else:
raise MaritalkHTTPError(response)
return gen()
except ImportError:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`."
)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, **kwargs)
|
