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aider/aider/models.py
Paul Gauthier 19ad89e1d7 Added gpt-4o-2024-08-06
2024-08-07 11:26:15 -03:00

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Python
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import difflib
import importlib
import json
import math
import os
import sys
from dataclasses import dataclass, fields
from pathlib import Path
from typing import Optional
import yaml
from PIL import Image
from aider import urls
from aider.dump import dump # noqa: F401
from aider.llm import litellm
DEFAULT_MODEL_NAME = "gpt-4o"
OPENAI_MODELS = """
gpt-4
gpt-4o
gpt-4o-2024-05-13
gpt-4-turbo-preview
gpt-4-0314
gpt-4-0613
gpt-4-32k
gpt-4-32k-0314
gpt-4-32k-0613
gpt-4-turbo
gpt-4-turbo-2024-04-09
gpt-4-1106-preview
gpt-4-0125-preview
gpt-4-vision-preview
gpt-4-1106-vision-preview
gpt-4o-mini
gpt-4o-mini-2024-07-18
gpt-3.5-turbo
gpt-3.5-turbo-0301
gpt-3.5-turbo-0613
gpt-3.5-turbo-1106
gpt-3.5-turbo-0125
gpt-3.5-turbo-16k
gpt-3.5-turbo-16k-0613
"""
OPENAI_MODELS = [ln.strip() for ln in OPENAI_MODELS.splitlines() if ln.strip()]
ANTHROPIC_MODELS = """
claude-2
claude-2.1
claude-3-haiku-20240307
claude-3-opus-20240229
claude-3-sonnet-20240229
claude-3-5-sonnet-20240620
"""
ANTHROPIC_MODELS = [ln.strip() for ln in ANTHROPIC_MODELS.splitlines() if ln.strip()]
@dataclass
class ModelSettings:
# Model class needs to have each of these as well
name: str
edit_format: str = "whole"
weak_model_name: Optional[str] = None
use_repo_map: bool = False
send_undo_reply: bool = False
accepts_images: bool = False
lazy: bool = False
reminder_as_sys_msg: bool = False
examples_as_sys_msg: bool = False
can_prefill: bool = False
extra_headers: Optional[dict] = None
max_tokens: Optional[int] = None
# https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
# https://platform.openai.com/docs/models/gpt-3-5-turbo
# https://openai.com/pricing
MODEL_SETTINGS = [
# gpt-3.5
ModelSettings(
"gpt-3.5-turbo",
"whole",
weak_model_name="gpt-4o-mini",
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-3.5-turbo-0125",
"whole",
weak_model_name="gpt-4o-mini",
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-3.5-turbo-1106",
"whole",
weak_model_name="gpt-4o-mini",
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-3.5-turbo-0613",
"whole",
weak_model_name="gpt-4o-mini",
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-3.5-turbo-16k-0613",
"whole",
weak_model_name="gpt-4o-mini",
reminder_as_sys_msg=True,
),
# gpt-4
ModelSettings(
"gpt-4-turbo-2024-04-09",
"udiff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4-turbo",
"udiff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"openai/gpt-4o",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"openai/gpt-4o-2024-08-06",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4o-2024-08-06",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4o",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4o-mini",
"whole",
weak_model_name="gpt-4o-mini",
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"openai/gpt-4o-mini",
"whole",
weak_model_name="openai/gpt-4o-mini",
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4-0125-preview",
"udiff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
lazy=True,
reminder_as_sys_msg=True,
examples_as_sys_msg=True,
),
ModelSettings(
"gpt-4-1106-preview",
"udiff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
lazy=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4-vision-preview",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4-0314",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
reminder_as_sys_msg=True,
examples_as_sys_msg=True,
),
ModelSettings(
"gpt-4-0613",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"gpt-4-32k-0613",
"diff",
weak_model_name="gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
reminder_as_sys_msg=True,
),
# Claude
ModelSettings(
"claude-3-opus-20240229",
"diff",
weak_model_name="claude-3-haiku-20240307",
use_repo_map=True,
send_undo_reply=True,
can_prefill=True,
),
ModelSettings(
"openrouter/anthropic/claude-3-opus",
"diff",
weak_model_name="openrouter/anthropic/claude-3-haiku",
use_repo_map=True,
send_undo_reply=True,
can_prefill=True,
),
ModelSettings(
"claude-3-sonnet-20240229",
"whole",
weak_model_name="claude-3-haiku-20240307",
can_prefill=True,
),
ModelSettings(
"claude-3-5-sonnet-20240620",
"diff",
weak_model_name="claude-3-haiku-20240307",
use_repo_map=True,
examples_as_sys_msg=True,
can_prefill=True,
accepts_images=True,
max_tokens=8192,
extra_headers={"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15"},
),
ModelSettings(
"anthropic/claude-3-5-sonnet-20240620",
"diff",
weak_model_name="claude-3-haiku-20240307",
use_repo_map=True,
examples_as_sys_msg=True,
can_prefill=True,
max_tokens=8192,
extra_headers={
"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15",
"HTTP-Referer": "https://aider.chat",
"X-Title": "Aider",
},
),
ModelSettings(
"openrouter/anthropic/claude-3.5-sonnet",
"diff",
weak_model_name="openrouter/anthropic/claude-3-haiku-20240307",
use_repo_map=True,
examples_as_sys_msg=True,
can_prefill=True,
accepts_images=True,
max_tokens=8192,
extra_headers={
"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15",
"HTTP-Referer": "https://aider.chat",
"X-Title": "Aider",
},
),
# Vertex AI Claude models
# Does not yet support 8k token
ModelSettings(
"vertex_ai/claude-3-5-sonnet@20240620",
"diff",
weak_model_name="vertex_ai/claude-3-haiku@20240307",
use_repo_map=True,
examples_as_sys_msg=True,
can_prefill=True,
accepts_images=True,
),
ModelSettings(
"vertex_ai/claude-3-opus@20240229",
"diff",
weak_model_name="vertex_ai/claude-3-haiku@20240307",
use_repo_map=True,
send_undo_reply=True,
can_prefill=True,
),
ModelSettings(
"vertex_ai/claude-3-sonnet@20240229",
"whole",
weak_model_name="vertex_ai/claude-3-haiku@20240307",
can_prefill=True,
),
# Cohere
ModelSettings(
"command-r-plus",
"whole",
weak_model_name="command-r-plus",
use_repo_map=True,
send_undo_reply=True,
),
# Groq llama3
ModelSettings(
"groq/llama3-70b-8192",
"diff",
weak_model_name="groq/llama3-8b-8192",
use_repo_map=False,
send_undo_reply=False,
examples_as_sys_msg=True,
),
# Openrouter llama3
ModelSettings(
"openrouter/meta-llama/llama-3-70b-instruct",
"diff",
weak_model_name="openrouter/meta-llama/llama-3-70b-instruct",
use_repo_map=False,
send_undo_reply=False,
examples_as_sys_msg=True,
),
# Gemini
ModelSettings(
"gemini/gemini-1.5-pro",
"diff-fenced",
use_repo_map=True,
send_undo_reply=True,
),
ModelSettings(
"gemini/gemini-1.5-pro-latest",
"diff-fenced",
use_repo_map=True,
send_undo_reply=True,
),
ModelSettings(
"deepseek/deepseek-chat",
"diff",
use_repo_map=True,
send_undo_reply=True,
examples_as_sys_msg=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"deepseek/deepseek-coder",
"diff",
use_repo_map=True,
send_undo_reply=True,
examples_as_sys_msg=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"openrouter/deepseek/deepseek-coder",
"diff",
use_repo_map=True,
send_undo_reply=True,
examples_as_sys_msg=True,
reminder_as_sys_msg=True,
),
ModelSettings(
"openrouter/openai/gpt-4o",
"diff",
weak_model_name="openrouter/openai/gpt-4o-mini",
use_repo_map=True,
send_undo_reply=True,
accepts_images=True,
lazy=True,
reminder_as_sys_msg=True,
),
]
class Model:
def __init__(self, model, weak_model=None):
# Set defaults from ModelSettings
default_settings = ModelSettings(name="")
for field in fields(ModelSettings):
setattr(self, field.name, getattr(default_settings, field.name))
self.name = model
self.max_chat_history_tokens = 1024
self.weak_model = None
self.info = self.get_model_info(model)
# Are all needed keys/params available?
res = self.validate_environment()
self.missing_keys = res.get("missing_keys")
self.keys_in_environment = res.get("keys_in_environment")
max_input_tokens = self.info.get("max_input_tokens") or 0
if max_input_tokens < 32 * 1024:
self.max_chat_history_tokens = 1024
else:
self.max_chat_history_tokens = 2 * 1024
self.configure_model_settings(model)
if weak_model is False:
self.weak_model_name = None
else:
self.get_weak_model(weak_model)
def get_model_info(self, model):
# Try and do this quickly, without triggering the litellm import
spec = importlib.util.find_spec("litellm")
if spec:
origin = Path(spec.origin)
fname = origin.parent / "model_prices_and_context_window_backup.json"
if fname.exists():
data = json.loads(fname.read_text())
info = data.get(model)
if info:
return info
# Do it the slow way...
try:
return litellm.get_model_info(model)
except Exception:
return dict()
def configure_model_settings(self, model):
for ms in MODEL_SETTINGS:
# direct match, or match "provider/<model>"
if model == ms.name:
for field in fields(ModelSettings):
val = getattr(ms, field.name)
setattr(self, field.name, val)
return # <--
model = model.lower()
if ("llama3" in model or "llama-3" in model) and "70b" in model:
self.edit_format = "diff"
self.use_repo_map = True
self.send_undo_reply = True
self.examples_as_sys_msg = True
return # <--
if "gpt-4-turbo" in model or ("gpt-4-" in model and "-preview" in model):
self.edit_format = "udiff"
self.use_repo_map = True
self.send_undo_reply = True
return # <--
if "gpt-4" in model or "claude-3-opus" in model:
self.edit_format = "diff"
self.use_repo_map = True
self.send_undo_reply = True
return # <--
if "gpt-3.5" in model or "gpt-4" in model:
self.reminder_as_sys_msg = True
if "anthropic" in model:
self.can_prefill = True
if "3.5-sonnet" in model or "3-5-sonnet" in model:
self.edit_format = "diff"
self.use_repo_map = True
self.examples_as_sys_msg = True
self.can_prefill = True
# use the defaults
if self.edit_format == "diff":
self.use_repo_map = True
def __str__(self):
return self.name
def get_weak_model(self, provided_weak_model_name):
# If weak_model_name is provided, override the model settings
if provided_weak_model_name:
self.weak_model_name = provided_weak_model_name
if not self.weak_model_name:
self.weak_model = self
return
if self.weak_model_name == self.name:
self.weak_model = self
return
self.weak_model = Model(
self.weak_model_name,
weak_model=False,
)
return self.weak_model
def commit_message_models(self):
return [self.weak_model, self]
def tokenizer(self, text):
return litellm.encode(model=self.name, text=text)
def token_count(self, messages):
if not self.tokenizer:
return
if type(messages) is str:
msgs = messages
else:
msgs = json.dumps(messages)
return len(self.tokenizer(msgs))
def token_count_for_image(self, fname):
"""
Calculate the token cost for an image assuming high detail.
The token cost is determined by the size of the image.
:param fname: The filename of the image.
:return: The token cost for the image.
"""
width, height = self.get_image_size(fname)
# If the image is larger than 2048 in any dimension, scale it down to fit within 2048x2048
max_dimension = max(width, height)
if max_dimension > 2048:
scale_factor = 2048 / max_dimension
width = int(width * scale_factor)
height = int(height * scale_factor)
# Scale the image such that the shortest side is 768 pixels long
min_dimension = min(width, height)
scale_factor = 768 / min_dimension
width = int(width * scale_factor)
height = int(height * scale_factor)
# Calculate the number of 512x512 tiles needed to cover the image
tiles_width = math.ceil(width / 512)
tiles_height = math.ceil(height / 512)
num_tiles = tiles_width * tiles_height
# Each tile costs 170 tokens, and there's an additional fixed cost of 85 tokens
token_cost = num_tiles * 170 + 85
return token_cost
def get_image_size(self, fname):
"""
Retrieve the size of an image.
:param fname: The filename of the image.
:return: A tuple (width, height) representing the image size in pixels.
"""
with Image.open(fname) as img:
return img.size
def fast_validate_environment(self):
"""Fast path for common models. Avoids forcing litellm import."""
model = self.name
if model in OPENAI_MODELS or model.startswith("openai/"):
var = "OPENAI_API_KEY"
elif model in ANTHROPIC_MODELS or model.startswith("anthropic/"):
var = "ANTHROPIC_API_KEY"
else:
return
if os.environ.get(var):
return dict(keys_in_environment=[var], missing_keys=[])
def validate_environment(self):
res = self.fast_validate_environment()
if res:
return res
# https://github.com/BerriAI/litellm/issues/3190
model = self.name
res = litellm.validate_environment(model)
if res["keys_in_environment"]:
return res
if res["missing_keys"]:
return res
provider = self.info.get("litellm_provider", "").lower()
if provider == "cohere_chat":
return validate_variables(["COHERE_API_KEY"])
if provider == "gemini":
return validate_variables(["GEMINI_API_KEY"])
if provider == "groq":
return validate_variables(["GROQ_API_KEY"])
return res
def register_models(model_settings_fnames):
files_loaded = []
for model_settings_fname in model_settings_fnames:
if not os.path.exists(model_settings_fname):
continue
try:
with open(model_settings_fname, "r") as model_settings_file:
model_settings_list = yaml.safe_load(model_settings_file)
for model_settings_dict in model_settings_list:
model_settings = ModelSettings(**model_settings_dict)
existing_model_settings = next(
(ms for ms in MODEL_SETTINGS if ms.name == model_settings.name), None
)
if existing_model_settings:
MODEL_SETTINGS.remove(existing_model_settings)
MODEL_SETTINGS.append(model_settings)
except Exception as e:
raise Exception(f"Error loading model settings from {model_settings_fname}: {e}")
files_loaded.append(model_settings_fname)
return files_loaded
def register_litellm_models(model_fnames):
files_loaded = []
for model_fname in model_fnames:
if not os.path.exists(model_fname):
continue
try:
with open(model_fname, "r") as model_def_file:
model_def = json.load(model_def_file)
litellm.register_model(model_def)
except Exception as e:
raise Exception(f"Error loading model definition from {model_fname}: {e}")
files_loaded.append(model_fname)
return files_loaded
def validate_variables(vars):
missing = []
for var in vars:
if var not in os.environ:
missing.append(var)
if missing:
return dict(keys_in_environment=False, missing_keys=missing)
return dict(keys_in_environment=True, missing_keys=missing)
def sanity_check_models(io, main_model):
sanity_check_model(io, main_model)
if main_model.weak_model and main_model.weak_model is not main_model:
sanity_check_model(io, main_model.weak_model)
def sanity_check_model(io, model):
show = False
if model.missing_keys:
show = True
io.tool_error(f"Model {model}: Missing these environment variables:")
for key in model.missing_keys:
io.tool_error(f"- {key}")
elif not model.keys_in_environment:
show = True
io.tool_output(f"Model {model}: Unknown which environment variables are required.")
if not model.info:
show = True
io.tool_output(
f"Model {model}: Unknown context window size and costs, using sane defaults."
)
possible_matches = fuzzy_match_models(model.name)
if possible_matches:
io.tool_output("Did you mean one of these?")
for match in possible_matches:
io.tool_output(f"- {match}")
if show:
io.tool_output(f"For more info, see: {urls.model_warnings}\n")
def fuzzy_match_models(name):
name = name.lower()
chat_models = set()
for model, attrs in litellm.model_cost.items():
model = model.lower()
if attrs.get("mode") != "chat":
continue
provider = (attrs["litellm_provider"] + "/").lower()
if model.startswith(provider):
fq_model = model
else:
fq_model = provider + model
chat_models.add(fq_model)
chat_models.add(model)
chat_models = sorted(chat_models)
# exactly matching model
# matching_models = [
# (fq,m) for fq,m in chat_models
# if name == fq or name == m
# ]
# if matching_models:
# return matching_models
# Check for model names containing the name
matching_models = [m for m in chat_models if name in m]
if matching_models:
return sorted(set(matching_models))
# Check for slight misspellings
models = set(chat_models)
matching_models = difflib.get_close_matches(name, models, n=3, cutoff=0.8)
return sorted(set(matching_models))
def print_matching_models(io, search):
matches = fuzzy_match_models(search)
if matches:
io.tool_output(f'Models which match "{search}":')
for model in matches:
io.tool_output(f"- {model}")
else:
io.tool_output(f'No models match "{search}".')
def main():
if len(sys.argv) != 2:
print("Usage: python models.py <model_name>")
sys.exit(1)
model_name = sys.argv[1]
matching_models = fuzzy_match_models(model_name)
if matching_models:
print(f"Matching models for '{model_name}':")
for model in matching_models:
print(model)
else:
print(f"No matching models found for '{model_name}'.")
if __name__ == "__main__":
main()
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