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228 | class MyMagicAI(LLM):
"""MyMagicAI LLM.
Examples:
`pip install llama-index-llms-mymagic`
```python
from llama_index.llms.mistralai import MistralAI
llm = MistralAI(model="mistral7b", api_key="<api_key>")
resp = llm.complete("Paul Graham is ")
print(resp)
```
"""
base_url_template: str = "https://{model}.mymagic.ai"
api_key: str = None
model: str = Field(default="mistral7b", description="The MyMagicAI model to use.")
max_tokens: int = Field(
default=10, description="The maximum number of tokens to generate."
)
question = Field(default="", description="The user question.")
storage_provider: str = Field(
default="gcs", description="The storage provider to use."
)
bucket_name: str = Field(
default="your-bucket-name",
description="The bucket name where the data is stored.",
)
session: str = Field(
default="test-session",
description="The session to use. This is a subfolder in the bucket where your data is located.",
)
role_arn: Optional[str] = Field(
None, description="ARN for role assumption in AWS S3."
)
system_prompt: str = Field(
default="Answer the question based only on the given content. Do not give explanations or examples. Do not continue generating more text after the answer.",
description="The system prompt to use.",
)
question_data: Dict[str, Any] = Field(
default_factory=dict, description="The data to send to the MyMagicAI API."
)
region: Optional[str] = Field(
"eu-west-2", description="The region the bucket is in. Only used for AWS S3."
)
return_output: Optional[bool] = Field(
False, description="Whether MyMagic API should return the output json"
)
input_json_file: Optional[str] = None
structured_output: Optional[Dict[str, Any]] = Field(
None, description="User-defined structure for the response output"
)
def __init__(
self,
api_key: str,
storage_provider: str,
bucket_name: str,
session: str,
system_prompt: Optional[str],
role_arn: Optional[str] = None,
region: Optional[str] = None,
return_output: Optional[bool] = False,
input_json_file: Optional[str] = None,
structured_output: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.return_output = return_output
self.question_data = {
"storage_provider": storage_provider,
"bucket_name": bucket_name,
"personal_access_token": api_key,
"session": session,
"max_tokens": self.max_tokens,
"role_arn": role_arn,
"system_prompt": system_prompt,
"region": region,
"return_output": return_output,
"input_json_file": input_json_file,
"structured_output": structured_output,
}
@classmethod
def class_name(cls) -> str:
return "MyMagicAI"
def _construct_url(self, model: str) -> str:
"""Constructs the API endpoint URL based on the specified model."""
return self.base_url_template.format(model=model)
async def _submit_question(self, question_data: Dict[str, Any]) -> Dict[str, Any]:
timeout_config = httpx.Timeout(600.0, connect=60.0)
async with httpx.AsyncClient(timeout=timeout_config) as client:
url = f"{self._construct_url(self.model)}/submit_question"
resp = await client.post(url, json=question_data)
resp.raise_for_status()
return resp.json()
def _submit_question_sync(self, question_data: Dict[str, Any]) -> Dict[str, Any]:
"""Submits a question to the model synchronously."""
url = f"{self._construct_url(self.model)}/submit_question"
resp = requests.post(url, json=question_data)
resp.raise_for_status()
return resp.json()
def _get_result_sync(self, task_id: str) -> Dict[str, Any]:
"""Polls for the result of a task synchronously."""
url = f"{self._construct_url(self.model)}/get_result/{task_id}"
response = requests.get(url)
response.raise_for_status()
return response.json()
async def _get_result(self, task_id: str) -> Dict[str, Any]:
async with httpx.AsyncClient() as client:
resp = await client.get(
f"{self._construct_url(self.model)}/get_result/{task_id}"
)
resp.raise_for_status()
return resp.json()
async def acomplete(
self,
question: str,
model: Optional[str] = None,
max_tokens: Optional[int] = None,
poll_interval: float = 1.0,
) -> CompletionResponse:
self.question_data["question"] = question
self.model = self.question_data["model"] = model or self.model
self.max_tokens = self.question_data["max_tokens"] = (
max_tokens or self.max_tokens
)
task_response = await self._submit_question(self.question_data)
if self.return_output:
return task_response
task_id = task_response.get("task_id")
while True:
result = await self._get_result(task_id)
if result["status"] != "PENDING":
return result
await asyncio.sleep(poll_interval)
def complete(
self,
question: str,
model: Optional[str] = None,
max_tokens: Optional[int] = None,
poll_interval: float = 1.0,
) -> CompletionResponse:
self.question_data["question"] = question
self.model = self.question_data["model"] = model or self.model
self.max_tokens = self.question_data["max_tokens"] = (
max_tokens or self.max_tokens
)
task_response = self._submit_question_sync(self.question_data)
if self.return_output:
return task_response
task_id = task_response.get("task_id")
while True:
result = self._get_result_sync(task_id)
if result["status"] != "PENDING":
return CompletionResponse(
text=result.get("message", ""),
additional_kwargs={"status": result["status"]},
)
time.sleep(poll_interval)
def stream_complete(self, question: str) -> CompletionResponseGen:
raise NotImplementedError(
"MyMagicAI does not currently support streaming completion."
)
async def achat(self, question: str) -> ChatResponse:
raise NotImplementedError("MyMagicAI does not currently support chat.")
def chat(self, question: str) -> ChatResponse:
raise NotImplementedError("MyMagicAI does not currently support chat.")
async def astream_complete(self, question: str) -> CompletionResponseAsyncGen:
raise NotImplementedError("MyMagicAI does not currently support streaming.")
async def astream_chat(self, question: str) -> ChatResponseAsyncGen:
raise NotImplementedError("MyMagicAI does not currently support streaming.")
def chat(self, question: str) -> ChatResponse:
raise NotImplementedError("MyMagicAI does not currently support chat.")
def stream_chat(self, question: str) -> ChatResponseGen:
raise NotImplementedError("MyMagicAI does not currently support chat.")
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
num_output=self.max_tokens,
model_name=self.model,
is_chat_model=False,
)
|