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import json
import backoff
import openai
# Ollama
ollama_choices = [
"mistral-nemo",
"llama3.1",
]
# hyperbolic
hyperbolic_choices = [
"Qwen/Qwen2.5-72B-Instruct",
"meta-llama/Meta-Llama-3.1-70B-Instruct",
]
allchoices = [
"Qwen/Qwen2.5-72B-Instruct",
"deepseek-ai/DeepSeek-V2.5",
"claude-3-5-sonnet-20240620",
"gpt-4o-2024-05-13",
"deepseek-coder-v2-0724",
"llama3.1-405b",
# Anthropic Claude models via Amazon Bedrock
"bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
"bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
"bedrock/anthropic.claude-3-haiku-20240307-v1:0",
"bedrock/anthropic.claude-3-opus-20240229-v1:0",
]
for item in ollama_choices:
allchoices.append("ollama/" + item)
for item in hyperbolic_choices:
allchoices.append("hyperbolic/" + item)
# Get N responses from a single message, used for ensembling.
@backoff.on_exception(backoff.expo, (openai.RateLimitError, openai.APITimeoutError))
def get_batch_responses_from_llm(
msg,
client,
model,
system_message,
print_debug=False,
msg_history=None,
temperature=0.75,
n_responses=1,
):
if msg_history is None:
msg_history = []
if model in [
"gpt-4o-2024-05-13",
"gpt-4o-mini-2024-07-18",
"gpt-4o-2024-08-06",
"Qwen/Qwen2.5-72B-Instruct"
]:
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=3000,
n=n_responses,
stop=None,
seed=0,
)
content = [r.message.content for r in response.choices]
new_msg_history = [
new_msg_history + [{"role": "assistant", "content": c}] for c in content
]
elif model == "deepseek-coder-v2-0724":
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model="deepseek-coder",
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=3000,
n=n_responses,
stop=None,
)
content = [r.message.content for r in response.choices]
new_msg_history = [
new_msg_history + [{"role": "assistant", "content": c}] for c in content
]
# ------------------------------------------------------------------------------------------------------
elif model == "Qwen/Qwen2.5-72B-Instruct":
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model="Qwen/Qwen2.5-72B-Instruct",
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=3000,
n=n_responses,
stop=None,
)
content = [r.message.content for r in response.choices]
new_msg_history = [
new_msg_history + [{"role": "assistant", "content": c}] for c in content
]
# elif model in hyperbolic_choices:
# content, new_msg_history = [], []
# for i in range(n_responses):
# print(f"Getting {i+1}/{n_responses} response from {model}")
# c, hist = get_response_from_llm(
# msg,
# client,
# model,
# system_message,
# print_debug=False,
# msg_history=None,
# temperature=temperature,
# )
# content.append(c)
# new_msg_history.append(hist)
# ------------------------------------------------------------------------------------------------------
elif model == "llama-3-1-405b-instruct":
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model="meta-llama/llama-3.1-405b-instruct",
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=3000,
n=n_responses,
stop=None,
)
content = [r.message.content for r in response.choices]
new_msg_history = [
new_msg_history + [{"role": "assistant", "content": c}] for c in content
]
elif model == "claude-3-5-sonnet-20240620":
content, new_msg_history = [], []
for _ in range(n_responses):
c, hist = get_response_from_llm(
msg,
client,
model,
system_message,
print_debug=False,
msg_history=None,
temperature=temperature,
)
content.append(c)
new_msg_history.append(hist)
# ollama models
elif model in ollama_choices:
content, new_msg_history = [], []
for i in range(n_responses):
print(f"Getting {i+1}/{n_responses} response from {model}")
c, hist = get_response_from_llm(
msg,
client,
model,
system_message,
print_debug=False,
msg_history=None,
temperature=temperature,
)
content.append(c)
new_msg_history.append(hist)
else:
raise ValueError(f"Model {model} not supported.")
if print_debug:
# Just print the first one.
print()
print("*" * 20 + " LLM START " + "*" * 20)
for j, msg in enumerate(new_msg_history[0]):
print(f'{j}, {msg["role"]}: {msg["content"]}')
print(content)
print("*" * 21 + " LLM END " + "*" * 21)
print()
return content, new_msg_history
@backoff.on_exception(backoff.expo, (openai.RateLimitError, openai.APITimeoutError))
def get_response_from_llm(
msg,
client,
model,
system_message,
print_debug=False,
msg_history=None,
temperature=0.75,
):
if msg_history is None:
msg_history = []
if model == "claude-3-5-sonnet-20240620":
new_msg_history = msg_history + [
{
"role": "user",
"content": [
{
"type": "text",
"text": msg,
}
],
}
]
response = client.messages.create(
model="claude-3-5-sonnet-20240620",
max_tokens=3000,
temperature=temperature,
system=system_message,
messages=new_msg_history,
)
content = response.content[0].text
new_msg_history = new_msg_history + [
{
"role": "assistant",
"content": [
{
"type": "text",
"text": content,
}
],
}
]
# ------------------------------------------------------------------------------------------------------
elif model in [
"gpt-4o-2024-05-13",
"gpt-4o-mini-2024-07-18",
"gpt-4o-2024-08-06",
"Qwen/Qwen2.5-72B-Instruct"
]:
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=3000,
n=1,
stop=None,
seed=0,
)
content = response.choices[0].message.content
new_msg_history = new_msg_history + [{"role": "assistant", "content": content}]
# ------------------------------------------------------------------------------------------------------
elif model in ["meta-llama/llama-3.1-405b-instruct", "llama-3-1-405b-instruct"]:
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model="meta-llama/llama-3.1-405b-instruct",
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=3000,
n=1,
stop=None,
)
content = response.choices[0].message.content
new_msg_history = new_msg_history + [{"role": "assistant", "content": content}]
elif model in ollama_choices:
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": system_message},
*new_msg_history,
],
temperature=temperature,
max_tokens=6000,
n=1,
stop=None,
seed=0,
)
content = response.choices[0].message.content
# print("\nget_response_from_llm\n")
# print(content)
new_msg_history = new_msg_history + [{"role": "assistant", "content": content}]
else:
raise ValueError(f"Model {model} not supported.")
if print_debug:
print()
print("*" * 20 + " LLM START " + "*" * 20)
for j, msg in enumerate(new_msg_history):
print(f'{j}, {msg["role"]}: {msg["content"]}')
print(content)
print("*" * 21 + " LLM END " + "*" * 21)
print()
return content, new_msg_history
def llm_json_auto_correct(system_prompt: str, user_prompt: str) -> str:
import os
client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"], base_url="https://api.hyperbolic.xyz/v1")
response = client.chat.completions.create(
model="Qwen/Qwen2.5-72B-Instruct",
temperature=0,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
)
return response.choices[0].message.content
def extract_json_between_markers(llm_output):
json_start_marker = "```json"
json_end_marker = "```"
# Find the start and end indices of the JSON string
start_index = llm_output.find(json_start_marker)
if start_index != -1:
start_index += len(json_start_marker) # Move past the marker
end_index = llm_output.find(json_end_marker, start_index)
else:
return None # JSON markers not found
if end_index == -1:
return None # End marker not found
# Extract the JSON string
json_string = llm_output[start_index:end_index].strip()
# print(json_string)
try:
parsed_json = json.loads(json_string)
return parsed_json
except json.JSONDecodeError:
return None # Invalid JSON format |