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"""Langchain Wrapper around Sambanova LLM APIs."""
import json
from typing import Any, Dict, Generator, Iterator, List, Optional, Union
import requests
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Extra
from langchain_core.utils import get_from_dict_or_env, pre_init
class SSEndpointHandler:
"""
SambaNova Systems Interface for SambaStudio model endpoints.
:param str host_url: Base URL of the DaaS API service
"""
def __init__(self, host_url: str, api_base_uri: str):
"""
Initialize the SSEndpointHandler.
:param str host_url: Base URL of the DaaS API service
:param str api_base_uri: Base URI of the DaaS API service
"""
self.host_url = host_url
self.api_base_uri = api_base_uri
self.http_session = requests.Session()
def _process_response(self, response: requests.Response) -> Dict:
"""
Processes the API response and returns the resulting dict.
All resulting dicts, regardless of success or failure, will contain the
`status_code` key with the API response status code.
If the API returned an error, the resulting dict will contain the key
`detail` with the error message.
If the API call was successful, the resulting dict will contain the key
`data` with the response data.
:param requests.Response response: the response object to process
:return: the response dict
:type: dict
"""
result: Dict[str, Any] = {}
try:
result = response.json()
except Exception as e:
result['detail'] = str(e)
if 'status_code' not in result:
result['status_code'] = response.status_code
return result
def _process_streaming_response(
self,
response: requests.Response,
) -> Generator[Dict, None, None]:
"""Process the streaming response"""
if 'api/predict/nlp' in self.api_base_uri:
try:
import sseclient
except ImportError:
raise ImportError(
'could not import sseclient library' 'Please install it with `pip install sseclient-py`.'
)
client = sseclient.SSEClient(response)
close_conn = False
for event in client.events():
if event.event == 'error_event':
close_conn = True
chunk = {
'event': event.event,
'data': event.data,
'status_code': response.status_code,
}
yield chunk
if close_conn:
client.close()
elif 'api/v2/predict/generic' in self.api_base_uri or 'api/predict/generic' in self.api_base_uri:
try:
for line in response.iter_lines():
chunk = json.loads(line)
if 'status_code' not in chunk:
chunk['status_code'] = response.status_code
yield chunk
except Exception as e:
raise RuntimeError(f'Error processing streaming response: {e}')
else:
raise ValueError(f'handling of endpoint uri: {self.api_base_uri} not implemented')
def _get_full_url(self, path: str) -> str:
"""
Return the full API URL for a given path.
:param str path: the sub-path
:returns: the full API URL for the sub-path
:type: str
"""
return f'{self.host_url}/{self.api_base_uri}/{path}'
def nlp_predict(
self,
project: str,
endpoint: str,
key: str,
input: Union[List[str], str],
params: Optional[str] = '',
stream: bool = False,
) -> Dict:
"""
NLP predict using inline input string.
:param str project: Project ID in which the endpoint exists
:param str endpoint: Endpoint ID
:param str key: API Key
:param str input_str: Input string
:param str params: Input params string
:returns: Prediction results
:type: dict
"""
if isinstance(input, str):
input = [input]
if 'api/predict/nlp' in self.api_base_uri:
if params:
data = {'inputs': input, 'params': json.loads(params)}
else:
data = {'inputs': input}
elif 'api/v2/predict/generic' in self.api_base_uri:
items = [{'id': f'item{i}', 'value': item} for i, item in enumerate(input)]
if params:
data = {'items': items, 'params': json.loads(params)}
else:
data = {'items': items}
elif 'api/predict/generic' in self.api_base_uri:
if params:
data = {'instances': input, 'params': json.loads(params)}
else:
data = {'instances': input}
else:
raise ValueError(f'handling of endpoint uri: {self.api_base_uri} not implemented')
response = self.http_session.post(
self._get_full_url(f'{project}/{endpoint}'),
headers={'key': key},
json=data,
)
return self._process_response(response)
def nlp_predict_stream(
self,
project: str,
endpoint: str,
key: str,
input: Union[List[str], str],
params: Optional[str] = '',
) -> Iterator[Dict]:
"""
NLP predict using inline input string.
:param str project: Project ID in which the endpoint exists
:param str endpoint: Endpoint ID
:param str key: API Key
:param str input_str: Input string
:param str params: Input params string
:returns: Prediction results
:type: dict
"""
if 'api/predict/nlp' in self.api_base_uri:
if isinstance(input, str):
input = [input]
if params:
data = {'inputs': input, 'params': json.loads(params)}
else:
data = {'inputs': input}
elif 'api/v2/predict/generic' in self.api_base_uri:
if isinstance(input, str):
input = [input]
items = [{'id': f'item{i}', 'value': item} for i, item in enumerate(input)]
if params:
data = {'items': items, 'params': json.loads(params)}
else:
data = {'items': items}
elif 'api/predict/generic' in self.api_base_uri:
if isinstance(input, list):
input = input[0]
if params:
data = {'instance': input, 'params': json.loads(params)}
else:
data = {'instance': input}
else:
raise ValueError(f'handling of endpoint uri: {self.api_base_uri} not implemented')
# Streaming output
response = self.http_session.post(
self._get_full_url(f'stream/{project}/{endpoint}'),
headers={'key': key},
json=data,
stream=True,
)
for chunk in self._process_streaming_response(response):
yield chunk
class SambaStudio(LLM):
"""
SambaStudio large language models.
To use, you should have the environment variables
``SAMBASTUDIO_BASE_URL`` set with your SambaStudio environment URL.
``SAMBASTUDIO_BASE_URI`` set with your SambaStudio api base URI.
``SAMBASTUDIO_PROJECT_ID`` set with your SambaStudio project ID.
``SAMBASTUDIO_ENDPOINT_ID`` set with your SambaStudio endpoint ID.
``SAMBASTUDIO_API_KEY`` set with your SambaStudio endpoint API key.
https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite
read extra documentation in https://docs.sambanova.ai/sambastudio/latest/index.html
Example:
.. code-block:: python
from langchain_community.llms.sambanova import SambaStudio
SambaStudio(
sambastudio_base_url="your-SambaStudio-environment-URL",
sambastudio_base_uri="your-SambaStudio-base-URI",
sambastudio_project_id="your-SambaStudio-project-ID",
sambastudio_endpoint_id="your-SambaStudio-endpoint-ID",
sambastudio_api_key="your-SambaStudio-endpoint-API-key,
streaming=False
model_kwargs={
"do_sample": False,
"max_tokens_to_generate": 1000,
"temperature": 0.7,
"top_p": 1.0,
"repetition_penalty": 1,
"top_k": 50,
#"process_prompt": False,
#"select_expert": "Meta-Llama-3-8B-Instruct"
},
)
"""
sambastudio_base_url: str = ''
"""Base url to use"""
sambastudio_base_uri: str = ''
"""endpoint base uri"""
sambastudio_project_id: str = ''
"""Project id on sambastudio for model"""
sambastudio_endpoint_id: str = ''
"""endpoint id on sambastudio for model"""
sambastudio_api_key: str = ''
"""sambastudio api key"""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
streaming: Optional[bool] = False
"""Streaming flag to get streamed response."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{'model_kwargs': self.model_kwargs}}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return 'Sambastudio LLM'
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values['sambastudio_base_url'] = get_from_dict_or_env(values, 'sambastudio_base_url', 'SAMBASTUDIO_BASE_URL')
values['sambastudio_base_uri'] = get_from_dict_or_env(
values,
'sambastudio_base_uri',
'SAMBASTUDIO_BASE_URI',
default='api/predict/generic',
)
values['sambastudio_project_id'] = get_from_dict_or_env(
values, 'sambastudio_project_id', 'SAMBASTUDIO_PROJECT_ID'
)
values['sambastudio_endpoint_id'] = get_from_dict_or_env(
values, 'sambastudio_endpoint_id', 'SAMBASTUDIO_ENDPOINT_ID'
)
values['sambastudio_api_key'] = get_from_dict_or_env(values, 'sambastudio_api_key', 'SAMBASTUDIO_API_KEY')
return values
def _get_tuning_params(self, stop: Optional[List[str]]) -> str:
"""
Get the tuning parameters to use when calling the LLM.
Args:
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of the stop substrings.
Returns:
The tuning parameters as a JSON string.
"""
_model_kwargs = self.model_kwargs or {}
_kwarg_stop_sequences = _model_kwargs.get('stop_sequences', [])
_stop_sequences = stop or _kwarg_stop_sequences
# if not _kwarg_stop_sequences:
# _model_kwargs["stop_sequences"] = ",".join(
# f'"{x}"' for x in _stop_sequences
# )
if 'api/v2/predict/generic' in self.sambastudio_base_uri:
tuning_params_dict = _model_kwargs
else:
tuning_params_dict = {k: {'type': type(v).__name__, 'value': str(v)} for k, v in (_model_kwargs.items())}
# _model_kwargs["stop_sequences"] = _kwarg_stop_sequences
tuning_params = json.dumps(tuning_params_dict)
return tuning_params
def _handle_nlp_predict(self, sdk: SSEndpointHandler, prompt: Union[List[str], str], tuning_params: str) -> str:
"""
Perform an NLP prediction using the SambaStudio endpoint handler.
Args:
sdk: The SSEndpointHandler to use for the prediction.
prompt: The prompt to use for the prediction.
tuning_params: The tuning parameters to use for the prediction.
Returns:
The prediction result.
Raises:
ValueError: If the prediction fails.
"""
response = sdk.nlp_predict(
self.sambastudio_project_id,
self.sambastudio_endpoint_id,
self.sambastudio_api_key,
prompt,
tuning_params,
)
if response['status_code'] != 200:
optional_detail = response.get('detail')
if optional_detail:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{response['status_code']}.\n Details: {optional_detail}"
)
else:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
f"{response['status_code']}.\n response {response}"
)
if 'api/predict/nlp' in self.sambastudio_base_uri:
return response['data'][0]['completion']
elif 'api/v2/predict/generic' in self.sambastudio_base_uri:
return response['items'][0]['value']['completion']
elif 'api/predict/generic' in self.sambastudio_base_uri:
return response['predictions'][0]['completion']
else:
raise ValueError(f'handling of endpoint uri: {self.sambastudio_base_uri} not implemented')
def _handle_completion_requests(self, prompt: Union[List[str], str], stop: Optional[List[str]]) -> str:
"""
Perform a prediction using the SambaStudio endpoint handler.
Args:
prompt: The prompt to use for the prediction.
stop: stop sequences.
Returns:
The prediction result.
Raises:
ValueError: If the prediction fails.
"""
ss_endpoint = SSEndpointHandler(self.sambastudio_base_url, self.sambastudio_base_uri)
tuning_params = self._get_tuning_params(stop)
return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params)
def _handle_nlp_predict_stream(
self, sdk: SSEndpointHandler, prompt: Union[List[str], str], tuning_params: str
) -> Iterator[GenerationChunk]:
"""
Perform a streaming request to the LLM.
Args:
sdk: The SVEndpointHandler to use for the prediction.
prompt: The prompt to use for the prediction.
tuning_params: The tuning parameters to use for the prediction.
Returns:
An iterator of GenerationChunks.
"""
for chunk in sdk.nlp_predict_stream(
self.sambastudio_project_id,
self.sambastudio_endpoint_id,
self.sambastudio_api_key,
prompt,
tuning_params,
):
if chunk['status_code'] != 200:
error = chunk.get('error')
if error:
optional_code = error.get('code')
optional_details = error.get('details')
optional_message = error.get('message')
raise ValueError(
f"Sambanova /complete call failed with status code "
f"{chunk['status_code']}.\n"
f"Message: {optional_message}\n"
f"Details: {optional_details}\n"
f"Code: {optional_code}\n"
)
else:
raise RuntimeError(
f"Sambanova /complete call failed with status code " f"{chunk['status_code']}." f"{chunk}."
)
if 'api/predict/nlp' in self.sambastudio_base_uri:
text = json.loads(chunk['data'])['stream_token']
elif 'api/v2/predict/generic' in self.sambastudio_base_uri:
text = chunk['result']['items'][0]['value']['stream_token']
elif 'api/predict/generic' in self.sambastudio_base_uri:
if len(chunk['result']['responses']) > 0:
text = chunk['result']['responses'][0]['stream_token']
else:
text = ''
else:
raise ValueError(f'handling of endpoint uri: {self.sambastudio_base_uri}' f'not implemented')
generated_chunk = GenerationChunk(text=text)
yield generated_chunk
def _stream(
self,
prompt: Union[List[str], str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Call out to Sambanova's complete endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
"""
ss_endpoint = SSEndpointHandler(self.sambastudio_base_url, self.sambastudio_base_uri)
tuning_params = self._get_tuning_params(stop)
try:
if self.streaming:
for chunk in self._handle_nlp_predict_stream(ss_endpoint, prompt, tuning_params):
if run_manager:
run_manager.on_llm_new_token(chunk.text)
yield chunk
else:
return
except Exception as e:
# Handle any errors raised by the inference endpoint
raise ValueError(f'Error raised by the inference endpoint: {e}') from e
def _handle_stream_request(
self,
prompt: Union[List[str], str],
stop: Optional[List[str]],
run_manager: Optional[CallbackManagerForLLMRun],
kwargs: Dict[str, Any],
) -> str:
"""
Perform a streaming request to the LLM.
Args:
prompt: The prompt to generate from.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of the stop substrings.
run_manager: Callback manager for the run.
**kwargs: Additional keyword arguments. directly passed
to the sambastudio model in API call.
Returns:
The model output as a string.
"""
completion = ''
for chunk in self._stream(prompt=prompt, stop=stop, run_manager=run_manager, **kwargs):
completion += chunk.text
return completion
def _call(
self,
prompt: Union[List[str], str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Sambanova's complete endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
"""
if stop is not None:
raise Exception('stop not implemented')
try:
if self.streaming:
return self._handle_stream_request(prompt, stop, run_manager, kwargs)
return self._handle_completion_requests(prompt, stop)
except Exception as e:
# Handle any errors raised by the inference endpoint
raise ValueError(f'Error raised by the inference endpoint: {e}') from e
class SambaNovaCloud(LLM):
"""
SambaNova Cloud large language models.
To use, you should have the environment variables
``SAMBANOVA_URL`` set with your SambaNova Cloud URL.
``SAMBANOVA_API_KEY`` set with your SambaNova Cloud API Key.
http://cloud.sambanova.ai/
Example:
.. code-block:: python
SambaNovaCloud(
sambanova_url = SambaNova cloud endpoint URL,
sambanova_api_key = set with your SambaNova cloud API key,
max_tokens = mas number of tokens to generate
stop_tokens = list of stop tokens
model = model name
)
"""
sambanova_url: str = ''
"""SambaNova Cloud Url"""
sambanova_api_key: str = ''
"""SambaNova Cloud api key"""
max_tokens: int = 1024
"""max tokens to generate"""
stop_tokens: list = ['<|eot_id|>']
"""Stop tokens"""
model: str = 'llama3-8b'
"""LLM model expert to use"""
temperature: float = 0.0
"""model temperature"""
top_p: float = 0.0
"""model top p"""
top_k: int = 1
"""model top k"""
stream_api: bool = True
"""use stream api"""
stream_options: dict = {'include_usage': True}
"""stream options, include usage to get generation metrics"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
'model': self.model,
'max_tokens': self.max_tokens,
'stop': self.stop_tokens,
'temperature': self.temperature,
'top_p': self.top_p,
'top_k': self.top_k,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return 'SambaNova Cloud'
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values['sambanova_url'] = get_from_dict_or_env(
values, 'sambanova_url', 'SAMBANOVA_URL', default='https://api.sambanova.ai/v1/chat/completions'
)
values['sambanova_api_key'] = get_from_dict_or_env(values, 'sambanova_api_key', 'SAMBANOVA_API_KEY')
return values
def _handle_nlp_predict_stream(
self,
prompt: Union[List[str], str],
stop: List[str],
) -> Iterator[GenerationChunk]:
"""
Perform a streaming request to the LLM.
Args:
prompt: The prompt to use for the prediction.
stop: list of stop tokens
Returns:
An iterator of GenerationChunks.
"""
try:
import sseclient
except ImportError:
raise ImportError('could not import sseclient library' 'Please install it with `pip install sseclient-py`.')
try:
formatted_prompt = json.loads(prompt)
except:
formatted_prompt = [{'role': 'user', 'content': prompt}]
http_session = requests.Session()
if not stop:
stop = self.stop_tokens
data = {
'messages': formatted_prompt,
'max_tokens': self.max_tokens,
'stop': stop,
'model': self.model,
'temperature': self.temperature,
'top_p': self.top_p,
'top_k': self.top_k,
'stream': self.stream_api,
'stream_options': self.stream_options,
}
# Streaming output
response = http_session.post(
self.sambanova_url,
headers={'Authorization': f'Bearer {self.sambanova_api_key}', 'Content-Type': 'application/json'},
json=data,
stream=True,
)
client = sseclient.SSEClient(response)
close_conn = False
if response.status_code != 200:
raise RuntimeError(
f'Sambanova /complete call failed with status code ' f'{response.status_code}.' f'{response.text}.'
)
for event in client.events():
if event.event == 'error_event':
close_conn = True
chunk = {
'event': event.event,
'data': event.data,
'status_code': response.status_code,
}
if chunk.get('error'):
raise RuntimeError(
f"Sambanova /complete call failed with status code " f"{chunk['status_code']}." f"{chunk}."
)
try:
# check if the response is a final event in that case event data response is '[DONE]'
if chunk['data'] != '[DONE]':
data = json.loads(chunk['data'])
if data.get('error'):
raise RuntimeError(
f"Sambanova /complete call failed with status code " f"{chunk['status_code']}." f"{chunk}."
)
# check if the response is a final response with usage stats (not includes content)
if data.get('usage') is None:
# check is not "end of text" response
if data['choices'][0]['finish_reason'] is None:
text = data['choices'][0]['delta']['content']
generated_chunk = GenerationChunk(text=text)
yield generated_chunk
except Exception as e:
raise Exception(f'Error getting content chunk raw streamed response: {chunk}')
def _stream(
self,
prompt: Union[List[str], str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Call out to Sambanova's complete endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
"""
try:
for chunk in self._handle_nlp_predict_stream(prompt, stop):
if run_manager:
run_manager.on_llm_new_token(chunk.text)
yield chunk
except Exception as e:
# Handle any errors raised by the inference endpoint
raise ValueError(f'Error raised by the inference endpoint: {e}') from e
def _handle_stream_request(
self,
prompt: Union[List[str], str],
stop: Optional[List[str]],
run_manager: Optional[CallbackManagerForLLMRun],
kwargs: Dict[str, Any],
) -> str:
"""
Perform a streaming request to the LLM.
Args:
prompt: The prompt to generate from.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of the stop substrings.
run_manager: Callback manager for the run.
**kwargs: Additional keyword arguments. directly passed
to the Sambanova Cloud model in API call.
Returns:
The model output as a string.
"""
completion = ''
for chunk in self._stream(prompt=prompt, stop=stop, run_manager=run_manager, **kwargs):
completion += chunk.text
return completion
def _call(
self,
prompt: Union[List[str], str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Sambanova's complete endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
"""
try:
return self._handle_stream_request(prompt, stop, run_manager, kwargs)
except Exception as e:
# Handle any errors raised by the inference endpoint
raise ValueError(f'Error raised by the inference endpoint: {e}') from e
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