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import logging
from collections import namedtuple
import tiktoken
from langchain import OpenAI
LLM_NAME = "text-davinci-003"
# Encoding for text-davinci-003
ENCODING_NAME = "p50k_base"
ENCODING = tiktoken.get_encoding(ENCODING_NAME)
# Max input tokens for text-davinci-003
LLM_MAX_TOKENS = 4096
# As specified in huggingGPT paper
TASK_PLANNING_LOGIT_BIAS = 0.1
MODEL_SELECTION_LOGIT_BIAS = 5
logger = logging.getLogger(__name__)
LLMs = namedtuple(
"LLMs",
[
"task_planning_llm",
"model_selection_llm",
"model_inference_llm",
"response_generation_llm",
"output_fixing_llm",
],
)
def create_llms() -> LLMs:
"""Create various LLM agents according to the huggingGPT paper's specifications."""
logger.info(f"Creating {LLM_NAME} LLMs")
task_parsing_highlight_ids = get_token_ids_for_task_parsing()
choose_model_highlight_ids = get_token_ids_for_choose_model()
task_planning_llm = OpenAI(
model_name=LLM_NAME,
temperature=0,
logit_bias={
token_id: TASK_PLANNING_LOGIT_BIAS
for token_id in task_parsing_highlight_ids
},
)
model_selection_llm = OpenAI(
model_name=LLM_NAME,
temperature=0,
logit_bias={
token_id: MODEL_SELECTION_LOGIT_BIAS
for token_id in choose_model_highlight_ids
},
)
model_inference_llm = OpenAI(model_name=LLM_NAME, temperature=0)
response_generation_llm = OpenAI(model_name=LLM_NAME, temperature=0)
output_fixing_llm = OpenAI(model_name=LLM_NAME, temperature=0)
return LLMs(
task_planning_llm=task_planning_llm,
model_selection_llm=model_selection_llm,
model_inference_llm=model_inference_llm,
response_generation_llm=response_generation_llm,
output_fixing_llm=output_fixing_llm,
)
def get_token_ids_for_task_parsing() -> list[int]:
text = """{"task": "text-classification", "token-classification", "text2text-generation", "summarization", "translation", "question-answering", "conversational", "text-generation", "sentence-similarity", "tabular-classification", "object-detection", "image-classification", "image-to-image", "image-to-text", "text-to-image", "visual-question-answering", "document-question-answering", "image-segmentation", "text-to-speech", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "args", "text", "path", "dep", "id", "<GENERATED>-"}"""
res = ENCODING.encode(text)
res = list(set(res))
return res
def get_token_ids_for_choose_model() -> list[int]:
text = """{"id": "reason"}"""
res = ENCODING.encode(text)
res = list(set(res))
return res
def count_tokens(text: str) -> int:
return len(ENCODING.encode(text))
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