Spaces:
Sleeping
Sleeping
File size: 10,481 Bytes
7524f15 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 |
# copied from SakanaAI's AI-Scientist 29/11/2024
import json
import os
import re
import anthropic
import backoff
import openai
MAX_NUM_TOKENS = 4096
AVAILABLE_LLMS = [
"gpt-4o-2024-08-06",
"gpt-4o-2024-05-13",
"gpt-4o-mini-2024-07-18",
"claude-3-5-sonnet-20241022",
"claude-3-5-sonnet-20240620",
"o1-preview-2024-09-12",
"o1-mini-2024-09-12",
"deepseek-coder-v2-0724",
"llama3.1-405b",
"llama-3-1-405b-instruct"
]
# 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",
]:
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=MAX_NUM_TOKENS,
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=MAX_NUM_TOKENS,
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 == "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=MAX_NUM_TOKENS,
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
]
else:
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)
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 "claude" in model:
new_msg_history = msg_history + [
{
"role": "user",
"content": [
{
"type": "text",
"text": msg,
}
],
}
]
response = client.messages.create(
model=model,
max_tokens=MAX_NUM_TOKENS,
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",
]:
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=MAX_NUM_TOKENS,
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 ["o1-preview-2024-09-12", "o1-mini-2024-09-12"]:
new_msg_history = msg_history + [{"role": "user", "content": msg}]
response = client.chat.completions.create(
model=model,
messages=[
{"role": "user", "content": system_message},
*new_msg_history,
],
temperature=1,
max_completion_tokens=MAX_NUM_TOKENS,
n=1,
#stop=None,
seed=0,
)
content = response.choices[0].message.content
new_msg_history = new_msg_history + [{"role": "assistant", "content": 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=MAX_NUM_TOKENS,
n=1,
stop=None,
)
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=MAX_NUM_TOKENS,
n=1,
stop=None,
)
content = response.choices[0].message.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 extract_json_between_markers(llm_output):
# Regular expression pattern to find JSON content between ```json and ```
json_pattern = r"```json(.*?)```"
matches = re.findall(json_pattern, llm_output, re.DOTALL)
if not matches:
# Fallback: Try to find any JSON-like content in the output
json_pattern = r"\{.*?\}"
matches = re.findall(json_pattern, llm_output, re.DOTALL)
for json_string in matches:
json_string = json_string.strip()
try:
parsed_json = json.loads(json_string)
return parsed_json
except json.JSONDecodeError:
# Attempt to fix common JSON issues
try:
# Remove invalid control characters
json_string_clean = re.sub(r"[\x00-\x1F\x7F]", "", json_string)
parsed_json = json.loads(json_string_clean)
return parsed_json
except json.JSONDecodeError:
continue # Try next match
return None # No valid JSON found
def create_client(model):
if model.startswith("claude-"):
print(f"Using Anthropic API with model {model}.")
return anthropic.Anthropic(), model
elif model.startswith("bedrock") and "claude" in model:
client_model = model.split("/")[-1]
print(f"Using Amazon Bedrock with model {client_model}.")
return anthropic.AnthropicBedrock(), client_model
elif model.startswith("vertex_ai") and "claude" in model:
client_model = model.split("/")[-1]
print(f"Using Vertex AI with model {client_model}.")
return anthropic.AnthropicVertex(), client_model
elif 'gpt' in model:
print(f"Using OpenAI API with model {model}.")
return openai.OpenAI(), model
elif model in ["o1-preview-2024-09-12", "o1-mini-2024-09-12"]:
print(f"Using OpenAI API with model {model}.")
return openai.OpenAI(), model
elif model == "deepseek-coder-v2-0724":
print(f"Using OpenAI API with {model}.")
return openai.OpenAI(
api_key=os.environ["DEEPSEEK_API_KEY"],
base_url="https://api.deepseek.com"
), model
elif model in ["llama3.1-405b", "llama3.1-405b-instruct"]:
print(f"Using OpenAI API with {model}.")
return openai.OpenAI(
api_key=os.environ["OPENROUTER_API_KEY"],
base_url="https://openrouter.ai/api/v1"
), "meta-llama/llama-3.1-405b-instruct"
else:
raise ValueError(f"Model {model} not supported.") |