mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-27 11:43:54 +00:00
249 lines
8.2 KiB
Python
249 lines
8.2 KiB
Python
import os, types
|
|
import json
|
|
from enum import Enum
|
|
import requests
|
|
import time
|
|
from typing import Callable, Optional
|
|
from litellm.utils import ModelResponse, Usage
|
|
import litellm
|
|
from .prompt_templates.factory import prompt_factory, custom_prompt
|
|
import httpx
|
|
|
|
|
|
class AnthropicConstants(Enum):
|
|
HUMAN_PROMPT = "\n\nHuman: "
|
|
AI_PROMPT = "\n\nAssistant: "
|
|
|
|
|
|
class AnthropicError(Exception):
|
|
def __init__(self, status_code, message):
|
|
self.status_code = status_code
|
|
self.message = message
|
|
self.request = httpx.Request(
|
|
method="POST", url="https://api.anthropic.com/v1/messages"
|
|
)
|
|
self.response = httpx.Response(status_code=status_code, request=self.request)
|
|
super().__init__(
|
|
self.message
|
|
) # Call the base class constructor with the parameters it needs
|
|
|
|
|
|
class AnthropicConfig:
|
|
"""
|
|
Reference: https://docs.anthropic.com/claude/reference/complete_post
|
|
|
|
to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
|
|
"""
|
|
|
|
max_tokens: Optional[int] = litellm.max_tokens # anthropic requires a default
|
|
stop_sequences: Optional[list] = None
|
|
temperature: Optional[int] = None
|
|
top_p: Optional[int] = None
|
|
top_k: Optional[int] = None
|
|
metadata: Optional[dict] = None
|
|
|
|
def __init__(
|
|
self,
|
|
max_tokens: Optional[int] = 256, # anthropic requires a default
|
|
stop_sequences: Optional[list] = None,
|
|
temperature: Optional[int] = None,
|
|
top_p: Optional[int] = None,
|
|
top_k: Optional[int] = None,
|
|
metadata: Optional[dict] = None,
|
|
) -> None:
|
|
locals_ = locals()
|
|
for key, value in locals_.items():
|
|
if key != "self" and value is not None:
|
|
setattr(self.__class__, key, value)
|
|
|
|
@classmethod
|
|
def get_config(cls):
|
|
return {
|
|
k: v
|
|
for k, v in cls.__dict__.items()
|
|
if not k.startswith("__")
|
|
and not isinstance(
|
|
v,
|
|
(
|
|
types.FunctionType,
|
|
types.BuiltinFunctionType,
|
|
classmethod,
|
|
staticmethod,
|
|
),
|
|
)
|
|
and v is not None
|
|
}
|
|
|
|
|
|
# makes headers for API call
|
|
def validate_environment(api_key, user_headers):
|
|
if api_key is None:
|
|
raise ValueError(
|
|
"Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params"
|
|
)
|
|
headers = {
|
|
"accept": "application/json",
|
|
"anthropic-version": "2023-06-01",
|
|
"content-type": "application/json",
|
|
"x-api-key": api_key,
|
|
}
|
|
if user_headers is not None and isinstance(user_headers, dict):
|
|
headers = {**headers, **user_headers}
|
|
return headers
|
|
|
|
|
|
def completion(
|
|
model: str,
|
|
messages: list,
|
|
api_base: str,
|
|
custom_prompt_dict: dict,
|
|
model_response: ModelResponse,
|
|
print_verbose: Callable,
|
|
encoding,
|
|
api_key,
|
|
logging_obj,
|
|
optional_params=None,
|
|
litellm_params=None,
|
|
logger_fn=None,
|
|
headers={},
|
|
):
|
|
headers = validate_environment(api_key, headers)
|
|
if model in custom_prompt_dict:
|
|
# check if the model has a registered custom prompt
|
|
model_prompt_details = custom_prompt_dict[model]
|
|
prompt = custom_prompt(
|
|
role_dict=model_prompt_details["roles"],
|
|
initial_prompt_value=model_prompt_details["initial_prompt_value"],
|
|
final_prompt_value=model_prompt_details["final_prompt_value"],
|
|
messages=messages,
|
|
)
|
|
else:
|
|
prompt = prompt_factory(
|
|
model=model, messages=messages, custom_llm_provider="anthropic"
|
|
)
|
|
"""
|
|
format messages for anthropic
|
|
1. Anthropic supports roles like "user" and "assistant", (here litellm translates system-> assistant)
|
|
2. The first message always needs to be of role "user"
|
|
3. Each message must alternate between "user" and "assistant" (this is not addressed as now by litellm)
|
|
4. final assistant content cannot end with trailing whitespace (anthropic raises an error otherwise)
|
|
"""
|
|
# 1. Anthropic only supports roles like "user" and "assistant"
|
|
for idx, message in enumerate(messages):
|
|
if message["role"] == "system":
|
|
message["role"] = "assistant"
|
|
|
|
# if this is the final assistant message, remove trailing whitespace
|
|
# TODO: only do this if it's the final assistant message
|
|
if message["role"] == "assistant":
|
|
message["content"] = message["content"].strip()
|
|
|
|
# 2. The first message always needs to be of role "user"
|
|
if len(messages) > 0:
|
|
if messages[0]["role"] != "user":
|
|
# find the index of the first user message
|
|
for i, message in enumerate(messages):
|
|
if message["role"] == "user":
|
|
break
|
|
|
|
# remove the user message at existing position and add it to the front
|
|
messages.pop(i)
|
|
# move the first user message to the front
|
|
messages = [message] + messages
|
|
|
|
## Load Config
|
|
config = litellm.AnthropicConfig.get_config()
|
|
for k, v in config.items():
|
|
if (
|
|
k not in optional_params
|
|
): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
|
|
optional_params[k] = v
|
|
|
|
data = {
|
|
"model": model,
|
|
"messages": messages,
|
|
**optional_params,
|
|
}
|
|
|
|
## LOGGING
|
|
logging_obj.pre_call(
|
|
input=prompt,
|
|
api_key=api_key,
|
|
additional_args={
|
|
"complete_input_dict": data,
|
|
"api_base": api_base,
|
|
"headers": headers,
|
|
},
|
|
)
|
|
|
|
## COMPLETION CALL
|
|
if "stream" in optional_params and optional_params["stream"] == True:
|
|
response = requests.post(
|
|
api_base,
|
|
headers=headers,
|
|
data=json.dumps(data),
|
|
stream=optional_params["stream"],
|
|
)
|
|
|
|
if response.status_code != 200:
|
|
raise AnthropicError(
|
|
status_code=response.status_code, message=response.text
|
|
)
|
|
|
|
return response.iter_lines()
|
|
else:
|
|
response = requests.post(api_base, headers=headers, data=json.dumps(data))
|
|
if response.status_code != 200:
|
|
raise AnthropicError(
|
|
status_code=response.status_code, message=response.text
|
|
)
|
|
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=messages,
|
|
api_key=api_key,
|
|
original_response=response.text,
|
|
additional_args={"complete_input_dict": data},
|
|
)
|
|
print_verbose(f"raw model_response: {response.text}")
|
|
## RESPONSE OBJECT
|
|
try:
|
|
completion_response = response.json()
|
|
except:
|
|
raise AnthropicError(
|
|
message=response.text, status_code=response.status_code
|
|
)
|
|
if "error" in completion_response:
|
|
raise AnthropicError(
|
|
message=str(completion_response["error"]),
|
|
status_code=response.status_code,
|
|
)
|
|
elif len(completion_response["content"]) == 0:
|
|
raise AnthropicError(
|
|
message="No content in response",
|
|
status_code=response.status_code,
|
|
)
|
|
else:
|
|
text_content = completion_response["content"][0].get("text", None)
|
|
model_response.choices[0].message.content = text_content # type: ignore
|
|
model_response.choices[0].finish_reason = completion_response["stop_reason"]
|
|
|
|
## CALCULATING USAGE
|
|
prompt_tokens = completion_response["usage"]["input_tokens"]
|
|
completion_tokens = completion_response["usage"]["output_tokens"]
|
|
total_tokens = prompt_tokens + completion_tokens
|
|
|
|
model_response["created"] = int(time.time())
|
|
model_response["model"] = model
|
|
usage = Usage(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=prompt_tokens + completion_tokens,
|
|
)
|
|
model_response.usage = usage
|
|
return model_response
|
|
|
|
|
|
def embedding():
|
|
# logic for parsing in - calling - parsing out model embedding calls
|
|
pass
|