mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
clean out AI21 Init Client calls
This commit is contained in:
ishaan-jaff
parent
31ebbf5208
commit
c0c499f7db
2 changed files with 94 additions and 107 deletions
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@ -1,4 +1,5 @@
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import os, json
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import os
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import json
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from enum import Enum
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import requests
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import time
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@ -13,115 +14,102 @@ class AI21Error(Exception):
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self.message
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) # Call the base class constructor with the parameters it needs
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def validate_environment(api_key):
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if api_key is None:
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raise ValueError(
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"Missing AI21 API Key - A call is being made to ai21 but no key is set either in the environment variables or via params"
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)
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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"Authorization": "Bearer " + api_key,
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}
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return headers
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class AI21LLM:
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def __init__(
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self, encoding, logging_obj, api_key=None
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):
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self.encoding = encoding
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self.completion_url_fragment_1 = "https://api.ai21.com/studio/v1/"
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self.completion_url_fragment_2 = "/complete"
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self.api_key = api_key
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self.logging_obj = logging_obj
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self.validate_environment(api_key=api_key)
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def validate_environment(
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self, api_key
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): # set up the environment required to run the model
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# set the api key
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if self.api_key == None:
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raise ValueError(
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"Missing AI21 API Key - A call is being made to ai21 but no key is set either in the environment variables or via params"
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)
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self.api_key = api_key
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self.headers = {
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"accept": "application/json",
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"content-type": "application/json",
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"Authorization": "Bearer " + self.api_key,
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}
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def completion(
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self,
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model: str,
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messages: list,
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model_response: ModelResponse,
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print_verbose: Callable,
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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): # logic for parsing in - calling - parsing out model completion calls
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model = model
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prompt = ""
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for message in messages:
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if "role" in message:
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if message["role"] == "user":
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prompt += (
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f"{message['content']}"
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)
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else:
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prompt += (
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f"{message['content']}"
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)
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def completion(
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model: str,
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messages: list,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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):
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headers = validate_environment(api_key)
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model = model
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prompt = ""
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for message in messages:
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if "role" in message:
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if message["role"] == "user":
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prompt += (
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f"{message['content']}"
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)
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else:
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prompt += f"{message['content']}"
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data = {
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"prompt": prompt,
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# "instruction": prompt, # some baseten models require the prompt to be passed in via the 'instruction' kwarg
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**optional_params,
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}
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prompt += (
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f"{message['content']}"
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)
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else:
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prompt += f"{message['content']}"
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data = {
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"prompt": prompt,
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# "instruction": prompt, # some baseten models require the prompt to be passed in via the 'instruction' kwarg
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**optional_params,
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}
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## LOGGING
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self.logging_obj.pre_call(
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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api_key=self.api_key,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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)
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## COMPLETION CALL
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response = requests.post(
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self.completion_url_fragment_1 + model + self.completion_url_fragment_2, headers=self.headers, data=json.dumps(data)
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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return response.iter_lines()
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else:
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## LOGGING
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self.logging_obj.post_call(
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## COMPLETION CALL
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response = requests.post(
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"https://api.ai21.com/studio/v1/" + model + "/complete", headers=headers, data=json.dumps(data)
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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return response.iter_lines()
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else:
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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api_key=self.api_key,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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if "error" in completion_response:
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raise AI21Error(
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message=completion_response["error"],
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status_code=response.status_code,
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)
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else:
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try:
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model_response["choices"][0]["message"]["content"] = completion_response["completions"][0]["data"]["text"]
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except:
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raise AI21Error(message=json.dumps(completion_response), status_code=response.status_code)
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## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
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prompt_tokens = len(
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self.encoding.encode(prompt)
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)
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completion_tokens = len(
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self.encoding.encode(model_response["choices"][0]["message"]["content"])
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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if "error" in completion_response:
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raise AI21Error(
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message=completion_response["error"],
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status_code=response.status_code,
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)
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else:
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try:
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model_response["choices"][0]["message"]["content"] = completion_response["completions"][0]["data"]["text"]
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except:
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raise AI21Error(message=json.dumps(completion_response), status_code=response.status_code)
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model_response["created"] = time.time()
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model_response["model"] = model
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model_response["usage"] = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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}
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return model_response
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## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
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prompt_tokens = len(
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encoding.encode(prompt)
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)
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"]["content"])
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)
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def embedding(
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self,
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): # logic for parsing in - calling - parsing out model embedding calls
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pass
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model_response["created"] = time.time()
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model_response["model"] = model
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model_response["usage"] = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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}
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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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@ -21,9 +21,9 @@ from litellm.utils import (
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)
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from .llms import anthropic
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from .llms import together_ai
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from .llms import ai21
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from .llms.huggingface_restapi import HuggingfaceRestAPILLM
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from .llms.baseten import BasetenLLM
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from .llms.ai21 import AI21LLM
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from .llms.aleph_alpha import AlephAlphaLLM
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import tiktoken
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from concurrent.futures import ThreadPoolExecutor
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@ -658,11 +658,7 @@ def completion(
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or litellm.ai21_key
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or os.environ.get("AI21_API_KEY")
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)
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ai21_client = AI21LLM(
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encoding=encoding, api_key=ai21_key, logging_obj=logging
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)
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model_response = ai21_client.completion(
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model_response = ai21.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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@ -670,6 +666,9 @@ def completion(
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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encoding=encoding,
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api_key=ai21_key,
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logging_obj=logging
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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