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"""
Translation logic for anthropic's `/v1/complete` endpoint
Litellm provider slug: `anthropic_text/<model_name>`
"""
import json
import time
from typing import AsyncIterator, Dict, Iterator, List, Optional, Union
import httpx
import litellm
from litellm.litellm_core_utils.prompt_templates.factory import (
custom_prompt,
prompt_factory,
)
from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
from litellm.llms.base_llm.chat.transformation import (
BaseConfig,
BaseLLMException,
LiteLLMLoggingObj,
)
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import (
ChatCompletionToolCallChunk,
ChatCompletionUsageBlock,
GenericStreamingChunk,
ModelResponse,
Usage,
)
class AnthropicTextError(BaseLLMException):
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/complete"
)
self.response = httpx.Response(status_code=status_code, request=self.request)
super().__init__(
message=self.message,
status_code=self.status_code,
request=self.request,
response=self.response,
) # Call the base class constructor with the parameters it needs
class AnthropicTextConfig(BaseConfig):
"""
Reference: https://docs.anthropic.com/claude/reference/complete_post
to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
"""
max_tokens_to_sample: 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_to_sample: 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)
# makes headers for API call
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
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,
}
headers.update(_headers)
return headers
def transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
prompt = self._get_anthropic_text_prompt_from_messages(
messages=messages, model=model
)
## Load Config
config = litellm.AnthropicTextConfig.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,
"prompt": prompt,
**optional_params,
}
return data
def get_supported_openai_params(self, model: str):
"""
Anthropic /complete API Ref: https://docs.anthropic.com/en/api/complete
"""
return [
"stream",
"max_tokens",
"max_completion_tokens",
"stop",
"temperature",
"top_p",
"extra_headers",
"user",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
"""
Follows the same logic as the AnthropicConfig.map_openai_params method (which is the Anthropic /messages API)
Note: the only difference is in the get supported openai params method between the AnthropicConfig and AnthropicTextConfig
API Ref: https://docs.anthropic.com/en/api/complete
"""
for param, value in non_default_params.items():
if param == "max_tokens":
optional_params["max_tokens_to_sample"] = value
if param == "max_completion_tokens":
optional_params["max_tokens_to_sample"] = value
if param == "stream" and value is True:
optional_params["stream"] = value
if param == "stop" and (isinstance(value, str) or isinstance(value, list)):
_value = litellm.AnthropicConfig()._map_stop_sequences(value)
if _value is not None:
optional_params["stop_sequences"] = _value
if param == "temperature":
optional_params["temperature"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "user":
optional_params["metadata"] = {"user_id": value}
return optional_params
def transform_response(
self,
model: str,
raw_response: httpx.Response,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
request_data: dict,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: str,
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
try:
completion_response = raw_response.json()
except Exception:
raise AnthropicTextError(
message=raw_response.text, status_code=raw_response.status_code
)
prompt = self._get_anthropic_text_prompt_from_messages(
messages=messages, model=model
)
if "error" in completion_response:
raise AnthropicTextError(
message=str(completion_response["error"]),
status_code=raw_response.status_code,
)
else:
if len(completion_response["completion"]) > 0:
model_response.choices[0].message.content = completion_response[ # type: ignore
"completion"
]
model_response.choices[0].finish_reason = completion_response["stop_reason"]
## CALCULATING USAGE
prompt_tokens = len(
encoding.encode(prompt)
) ##[TODO] use the anthropic tokenizer here
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
) ##[TODO] use the anthropic tokenizer here
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,
)
setattr(model_response, "usage", usage)
return model_response
def get_error_class(
self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers]
) -> BaseLLMException:
return AnthropicTextError(
status_code=status_code,
message=error_message,
)
@staticmethod
def _is_anthropic_text_model(model: str) -> bool:
return model == "claude-2" or model == "claude-instant-1"
def _get_anthropic_text_prompt_from_messages(
self, messages: List[AllMessageValues], model: str
) -> str:
custom_prompt_dict = litellm.custom_prompt_dict
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"
)
return str(prompt)
def get_model_response_iterator(
self,
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
sync_stream: bool,
json_mode: Optional[bool] = False,
):
return AnthropicTextCompletionResponseIterator(
streaming_response=streaming_response,
sync_stream=sync_stream,
json_mode=json_mode,
)
class AnthropicTextCompletionResponseIterator(BaseModelResponseIterator):
def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
try:
text = ""
tool_use: Optional[ChatCompletionToolCallChunk] = None
is_finished = False
finish_reason = ""
usage: Optional[ChatCompletionUsageBlock] = None
provider_specific_fields = None
index = int(chunk.get("index", 0))
_chunk_text = chunk.get("completion", None)
if _chunk_text is not None and isinstance(_chunk_text, str):
text = _chunk_text
finish_reason = chunk.get("stop_reason", None)
if finish_reason is not None:
is_finished = True
returned_chunk = GenericStreamingChunk(
text=text,
tool_use=tool_use,
is_finished=is_finished,
finish_reason=finish_reason,
usage=usage,
index=index,
provider_specific_fields=provider_specific_fields,
)
return returned_chunk
except json.JSONDecodeError:
raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
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