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import logging
import os
import sys
from typing import Optional, Dict
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.llms import LLM
from langchain_core.language_models.chat_models import BaseChatModel
current_dir = os.path.dirname(os.path.abspath(__file__))
utils_dir = os.path.abspath(os.path.join(current_dir, '..'))
repo_dir = os.path.abspath(os.path.join(utils_dir, '..'))
sys.path.append(utils_dir)
sys.path.append(repo_dir)
from utils.model_wrappers.langchain_embeddings import SambaStudioEmbeddings
from utils.model_wrappers.langchain_llms import SambaStudio
from utils.model_wrappers.langchain_llms import SambaNovaCloud
from utils.model_wrappers.langchain_chat_models import ChatSambaNovaCloud
EMBEDDING_MODEL = 'intfloat/e5-large-v2'
NORMALIZE_EMBEDDINGS = True
# Configure the logger
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] - %(message)s',
handlers=[
logging.StreamHandler(),
],
)
logger = logging.getLogger(__name__)
class APIGateway:
@staticmethod
def load_embedding_model(
type: str = 'cpu',
batch_size: Optional[int] = None,
coe: bool = False,
select_expert: Optional[str] = None,
sambastudio_embeddings_base_url: Optional[str] = None,
sambastudio_embeddings_base_uri: Optional[str] = None,
sambastudio_embeddings_project_id: Optional[str] = None,
sambastudio_embeddings_endpoint_id: Optional[str] = None,
sambastudio_embeddings_api_key: Optional[str] = None,
) -> Embeddings:
"""Loads a langchain embedding model given a type and parameters
Args:
type (str): wether to use sambastudio embedding model or in local cpu model
batch_size (int, optional): batch size for sambastudio model. Defaults to None.
coe (bool, optional): whether to use coe model. Defaults to False. only for sambastudio models
select_expert (str, optional): expert model to be used when coe selected. Defaults to None.
only for sambastudio models.
sambastudio_embeddings_base_url (str, optional): base url for sambastudio model. Defaults to None.
sambastudio_embeddings_base_uri (str, optional): endpoint base uri for sambastudio model. Defaults to None.
sambastudio_embeddings_project_id (str, optional): project id for sambastudio model. Defaults to None.
sambastudio_embeddings_endpoint_id (str, optional): endpoint id for sambastudio model. Defaults to None.
sambastudio_embeddings_api_key (str, optional): api key for sambastudio model. Defaults to None.
Returns:
langchain embedding model
"""
if type == 'sambastudio':
envs = {
'sambastudio_embeddings_base_url': sambastudio_embeddings_base_url,
'sambastudio_embeddings_base_uri': sambastudio_embeddings_base_uri,
'sambastudio_embeddings_project_id': sambastudio_embeddings_project_id,
'sambastudio_embeddings_endpoint_id': sambastudio_embeddings_endpoint_id,
'sambastudio_embeddings_api_key': sambastudio_embeddings_api_key,
}
envs = {k: v for k, v in envs.items() if v is not None}
if coe:
if batch_size is None:
batch_size = 1
embeddings = SambaStudioEmbeddings(
**envs, batch_size=batch_size, model_kwargs={'select_expert': select_expert}
)
else:
if batch_size is None:
batch_size = 32
embeddings = SambaStudioEmbeddings(**envs, batch_size=batch_size)
elif type == 'cpu':
encode_kwargs = {'normalize_embeddings': NORMALIZE_EMBEDDINGS}
embedding_model = EMBEDDING_MODEL
embeddings = HuggingFaceInstructEmbeddings(
model_name=embedding_model,
embed_instruction='', # no instruction is needed for candidate passages
query_instruction='Represent this sentence for searching relevant passages: ',
encode_kwargs=encode_kwargs,
)
else:
raise ValueError(f'{type} is not a valid embedding model type')
return embeddings
@staticmethod
def load_llm(
type: str,
streaming: bool = False,
coe: bool = False,
do_sample: Optional[bool] = None,
max_tokens_to_generate: Optional[int] = None,
temperature: Optional[float] = None,
select_expert: Optional[str] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
repetition_penalty: Optional[float] = None,
stop_sequences: Optional[str] = None,
process_prompt: Optional[bool] = False,
sambastudio_base_url: Optional[str] = None,
sambastudio_base_uri: Optional[str] = None,
sambastudio_project_id: Optional[str] = None,
sambastudio_endpoint_id: Optional[str] = None,
sambastudio_api_key: Optional[str] = None,
sambanova_url: Optional[str] = None,
sambanova_api_key: Optional[str] = None,
) -> LLM:
"""Loads a langchain Sambanova llm model given a type and parameters
Args:
type (str): wether to use sambastudio, or SambaNova Cloud model "sncloud"
streaming (bool): wether to use streaming method. Defaults to False.
coe (bool): whether to use coe model. Defaults to False.
do_sample (bool) : Optional wether to do sample.
max_tokens_to_generate (int) : Optional max number of tokens to generate.
temperature (float) : Optional model temperature.
select_expert (str) : Optional expert to use when using CoE models.
top_p (float) : Optional model top_p.
top_k (int) : Optional model top_k.
repetition_penalty (float) : Optional model repetition penalty.
stop_sequences (str) : Optional model stop sequences.
process_prompt (bool) : Optional default to false.
sambastudio_base_url (str): Optional SambaStudio environment URL".
sambastudio_base_uri (str): Optional SambaStudio-base-URI".
sambastudio_project_id (str): Optional SambaStudio project ID.
sambastudio_endpoint_id (str): Optional SambaStudio endpoint ID.
sambastudio_api_token (str): Optional SambaStudio endpoint API key.
sambanova_url (str): Optional SambaNova Cloud URL",
sambanova_api_key (str): Optional SambaNovaCloud API key.
Returns:
langchain llm model
"""
if type == 'sambastudio':
envs = {
'sambastudio_base_url': sambastudio_base_url,
'sambastudio_base_uri': sambastudio_base_uri,
'sambastudio_project_id': sambastudio_project_id,
'sambastudio_endpoint_id': sambastudio_endpoint_id,
'sambastudio_api_key': sambastudio_api_key,
}
envs = {k: v for k, v in envs.items() if v is not None}
if coe:
model_kwargs = {
'do_sample': do_sample,
'max_tokens_to_generate': max_tokens_to_generate,
'temperature': temperature,
'select_expert': select_expert,
'top_p': top_p,
'top_k': top_k,
'repetition_penalty': repetition_penalty,
'stop_sequences': stop_sequences,
'process_prompt': process_prompt,
}
model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None}
llm = SambaStudio(
**envs,
streaming=streaming,
model_kwargs=model_kwargs,
)
else:
model_kwargs = {
'do_sample': do_sample,
'max_tokens_to_generate': max_tokens_to_generate,
'temperature': temperature,
'top_p': top_p,
'top_k': top_k,
'repetition_penalty': repetition_penalty,
'stop_sequences': stop_sequences,
}
model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None}
llm = SambaStudio(
**envs,
streaming=streaming,
model_kwargs=model_kwargs,
)
elif type == 'sncloud':
envs = {
'sambanova_url': sambanova_url,
'sambanova_api_key': sambanova_api_key,
}
envs = {k: v for k, v in envs.items() if v is not None}
llm = SambaNovaCloud(
**envs,
max_tokens=max_tokens_to_generate,
model=select_expert,
temperature=temperature,
top_k=top_k,
top_p=top_p,
)
else:
raise ValueError(f"Invalid LLM API: {type}, only 'sncloud' and 'sambastudio' are supported.")
return llm
@staticmethod
def load_chat(
model: str,
streaming: bool = False,
max_tokens: int = 1024,
temperature: Optional[float] = 0.0,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
stream_options: Optional[Dict[str, bool]] = {"include_usage": True},
sambanova_url: Optional[str] = None,
sambanova_api_key: Optional[str] = None,
) -> BaseChatModel:
"""
Loads a langchain SambanovaCloud chat model given some parameters
Args:
model (str): The name of the model to use, e.g., llama3-8b.
streaming (bool): whether to use streaming method. Defaults to False.
max_tokens (int) : Optional max number of tokens to generate.
temperature (float) : Optional model temperature.
top_p (float) : Optional model top_p.
top_k (int) : Optional model top_k.
stream_options (dict) : stream options, include usage to get generation metrics
sambanova_url (str): Optional SambaNova Cloud URL",
sambanova_api_key (str): Optional SambaNovaCloud API key.
Returns:
langchain BaseChatModel
"""
envs = {
'sambanova_url': sambanova_url,
'sambanova_api_key': sambanova_api_key,
}
envs = {k: v for k, v in envs.items() if v is not None}
model = ChatSambaNovaCloud(
**envs,
model= model,
streaming=streaming,
max_tokens=max_tokens,
temperature=temperature,
top_k=top_k,
top_p=top_p,
stream_options=stream_options
)
return model |