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import os
import json
import time
import requests
import openai
import copy
from loguru import logger
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv("API_KEY")
API_BASE = os.getenv("API_BASE")
API_KEY_2 = os.getenv("API_KEY_2")
API_BASE_2 = os.getenv("API_BASE_2")
MAX_TOKENS = os.getenv("MAX_TOKENS")
TEMPERATURE = os.getenv("TEMPERATURE")
DEBUG = int(os.environ.get("DEBUG", "0"))
def generate_together(
model,
messages,
max_tokens=MAX_TOKENS,
temperature=TEMPERATURE,
api_key=API_KEY,
streaming=False,
):
logger.info(
f"Input data: model={model}, messages={messages}, max_tokens={max_tokens}, temperature={temperature}"
)
output = None
for sleep_time in [1, 2, 4, 8, 16, 32]:
try:
endpoint = "http://localhost:11434/v1/chat/completions"
logger.info(f"Sending request to {endpoint}")
# Assuming model is a list with one element, e.g., ['qwen2']
chat_model = model[0] if isinstance(model, list) else model
# Convert temperature to float
temperature = float(temperature)
# Ensure messages are in the correct format
formatted_messages = []
for msg in messages:
if isinstance(msg['content'], list):
# If content is a list, join it into a single string
msg['content'] = ' '.join([m['content'] for m in msg['content'] if 'content' in m])
formatted_messages.append(msg)
res = requests.post(
endpoint,
json={
"model": chat_model,
"max_tokens": int(max_tokens),
"temperature": temperature if temperature > 1e-4 else 0,
"messages": formatted_messages,
},
headers={
"Authorization": f"Bearer {api_key}",
},
)
res.raise_for_status() # This will raise an exception for HTTP errors
output = res.json()["choices"][0]["message"]["content"]
break
except Exception as e:
logger.error(f"Error in generate_together: {str(e)}")
output = f"Error: {str(e)}"
logger.info(f"Retry in {sleep_time}s..")
time.sleep(sleep_time)
if output is None:
return output
output = output.strip()
logger.info(f"Output: `{output[:20]}...`.")
return output
def generate_together_stream(
model,
messages,
max_tokens=MAX_TOKENS,
temperature=TEMPERATURE,
api_key=API_KEY
):
# endpoint = f"{api_base}/chat/completions"
endpoint = API_BASE
client = openai.OpenAI(api_key=api_key, base_url=endpoint)
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature if temperature > 1e-4 else 0,
max_tokens=max_tokens,
stream=True, # this time, we set stream=True
)
return response
def generate_openai(
model,
messages,
max_tokens=MAX_TOKENS,
temperature=TEMPERATURE,
):
client = openai.OpenAI(
base_url=API_BASE_2,
api_key=API_KEY_2,
)
for sleep_time in [1, 2, 4, 8, 16, 32]:
try:
if DEBUG:
logger.debug(
f"Sending messages ({len(messages)}) (last message: `{messages[-1]['content'][:20]}`) to `{model}`."
)
completion = client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
output = completion.choices[0].message.content
break
except Exception as e:
logger.error(e)
logger.info(f"Retry in {sleep_time}s..")
time.sleep(sleep_time)
output = output.strip()
return output
def inject_references_to_messages(
messages,
references,
):
messages = copy.deepcopy(messages)
system = f"""You have been provided with a set of responses from various open-source models to the latest user query. Your task is to synthesize these responses into a single, high-quality response. It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability.
Responses from models:"""
for i, reference in enumerate(references):
system += f"\n{i+1}. {reference}"
# if messages[0]["role"] == "system":
# messages[0]["content"] += "\n\n" + system
# else:
messages = [{"role": "system", "content": system}] + messages
return messages
def generate_with_references(
model,
messages,
references=[],
max_tokens=MAX_TOKENS,
temperature=TEMPERATURE,
generate_fn=generate_together,
api_key=API_KEY
):
if len(references) > 0:
messages = inject_references_to_messages(messages, references)
return generate_fn(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
api_key=api_key
) |