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(chore) remove deprecated completion_with_config() tests
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ishaan-jaff
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4 changed files with 0 additions and 295 deletions
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@ -1,49 +0,0 @@
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# Model Config
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Model-specific changes can make our code complicated, making it harder to debug errors. Use model configs to simplify this.
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### usage
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Handling prompt logic. Different models have different context windows. Use `adapt_to_prompt_size` to select the right model for the prompt (in case the current model is too small).
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```python
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from litellm import completion_with_config
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import os
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config = {
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"available_models": ["gpt-3.5-turbo", "claude-instant-1", "gpt-3.5-turbo-16k"],
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"adapt_to_prompt_size": True, # 👈 key change
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}
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# set env var
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
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sample_text = "how does a court case get to the Supreme Court?" * 1000
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messages = [{"content": sample_text, "role": "user"}]
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response = completion_with_config(model="gpt-3.5-turbo", messages=messages, config=config)
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```
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[**See Code**](https://github.com/BerriAI/litellm/blob/30724d9e51cdc2c3e0eb063271b4f171bc01b382/litellm/utils.py#L2783)
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### Complete Config Structure
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```python
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config = {
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"default_fallback_models": # [Optional] List of model names to try if a call fails
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"available_models": # [Optional] List of all possible models you could call
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"adapt_to_prompt_size": # [Optional] True/False - if you want to select model based on prompt size (will pick from available_models)
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"model": {
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"model-name": {
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"needs_moderation": # [Optional] True/False - if you want to call openai moderations endpoint before making completion call. Will raise exception, if flagged.
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"error_handling": {
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"error-type": { # One of the errors listed here - https://docs.litellm.ai/docs/exception_mapping#custom-mapping-list
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"fallback_model": "" # str, name of the model it should try instead, when that error occurs
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}
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}
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}
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}
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}
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```
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@ -500,7 +500,6 @@ from .utils import (
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validate_environment,
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validate_environment,
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check_valid_key,
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check_valid_key,
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get_llm_provider,
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get_llm_provider,
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completion_with_config,
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register_model,
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register_model,
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encode,
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encode,
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decode,
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decode,
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@ -1,118 +0,0 @@
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import sys, os
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import os
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import pytest
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import litellm
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from litellm import completion_with_config
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config = {
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"default_fallback_models": ["gpt-3.5-turbo", "claude-instant-1", "j2-ultra"],
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"model": {
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"claude-instant-1": {"needs_moderation": True},
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"gpt-3.5-turbo": {
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"error_handling": {
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"ContextWindowExceededError": {"fallback_model": "gpt-3.5-turbo-16k"}
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}
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},
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},
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}
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def test_config_context_window_exceeded():
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try:
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sample_text = "how does a court case get to the Supreme Court?" * 1000
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messages = [{"content": sample_text, "role": "user"}]
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response = completion_with_config(
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model="gpt-3.5-turbo", messages=messages, config=config
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)
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print(response)
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except Exception as e:
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print(f"Exception: {e}")
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pytest.fail(f"An exception occurred: {e}")
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# test_config_context_window_exceeded()
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def test_config_context_moderation():
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try:
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messages = [{"role": "user", "content": "I want to kill them."}]
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response = completion_with_config(
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model="claude-instant-1", messages=messages, config=config
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)
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print(response)
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except Exception as e:
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print(f"Exception: {e}")
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pytest.fail(f"An exception occurred: {e}")
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# test_config_context_moderation()
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def test_config_context_default_fallback():
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try:
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messages = [{"role": "user", "content": "Hey, how's it going?"}]
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response = completion_with_config(
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model="claude-instant-1",
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messages=messages,
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config=config,
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api_key="bad-key",
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)
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print(response)
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except Exception as e:
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print(f"Exception: {e}")
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pytest.fail(f"An exception occurred: {e}")
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# test_config_context_default_fallback()
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config = {
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"default_fallback_models": ["gpt-3.5-turbo", "claude-instant-1", "j2-ultra"],
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"available_models": [
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-0301",
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"gpt-3.5-turbo-0613",
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"gpt-4",
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"gpt-4-0314",
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"gpt-4-0613",
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"j2-ultra",
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"command-nightly",
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"togethercomputer/llama-2-70b-chat",
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"chat-bison",
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"chat-bison@001",
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"claude-2",
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],
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"adapt_to_prompt_size": True, # type: ignore
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"model": {
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"claude-instant-1": {"needs_moderation": True},
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"gpt-3.5-turbo": {
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"error_handling": {
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"ContextWindowExceededError": {"fallback_model": "gpt-3.5-turbo-16k"}
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}
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},
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},
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}
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def test_config_context_adapt_to_prompt():
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try:
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sample_text = "how does a court case get to the Supreme Court?" * 1000
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messages = [{"content": sample_text, "role": "user"}]
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response = completion_with_config(
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model="gpt-3.5-turbo", messages=messages, config=config
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)
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print(response)
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except Exception as e:
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print(f"Exception: {e}")
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pytest.fail(f"An exception occurred: {e}")
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test_config_context_adapt_to_prompt()
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127
litellm/utils.py
127
litellm/utils.py
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@ -7843,133 +7843,6 @@ def read_config_args(config_path) -> dict:
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########## experimental completion variants ############################
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########## experimental completion variants ############################
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def completion_with_config(config: Union[dict, str], **kwargs):
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"""
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Generate a litellm.completion() using a config dict and all supported completion args
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Example config;
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config = {
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"default_fallback_models": # [Optional] List of model names to try if a call fails
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"available_models": # [Optional] List of all possible models you could call
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"adapt_to_prompt_size": # [Optional] True/False - if you want to select model based on prompt size (will pick from available_models)
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"model": {
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"model-name": {
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"needs_moderation": # [Optional] True/False - if you want to call openai moderations endpoint before making completion call. Will raise exception, if flagged.
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"error_handling": {
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"error-type": { # One of the errors listed here - https://docs.litellm.ai/docs/exception_mapping#custom-mapping-list
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"fallback_model": "" # str, name of the model it should try instead, when that error occurs
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}
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}
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}
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}
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}
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Parameters:
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config (Union[dict, str]): A configuration for litellm
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**kwargs: Additional keyword arguments for litellm.completion
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Returns:
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litellm.ModelResponse: A ModelResponse with the generated completion
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"""
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if config is not None:
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if isinstance(config, str):
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config = read_config_args(config)
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elif isinstance(config, dict):
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config = config
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else:
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raise Exception("Config path must be a string or a dictionary.")
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else:
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raise Exception("Config path not passed in.")
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if config is None:
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raise Exception("No completion config in the config file")
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models_with_config = config["model"].keys()
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model = kwargs["model"]
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messages = kwargs["messages"]
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## completion config
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fallback_models = config.get("default_fallback_models", None)
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available_models = config.get("available_models", None)
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adapt_to_prompt_size = config.get("adapt_to_prompt_size", False)
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trim_messages_flag = config.get("trim_messages", False)
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prompt_larger_than_model = False
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max_model = model
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try:
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max_tokens = litellm.get_max_tokens(model)["max_tokens"]
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except:
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max_tokens = 2048 # assume curr model's max window is 2048 tokens
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if adapt_to_prompt_size:
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## Pick model based on token window
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prompt_tokens = litellm.token_counter(
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model="gpt-3.5-turbo",
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text="".join(message["content"] for message in messages),
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)
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try:
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curr_max_tokens = litellm.get_max_tokens(model)["max_tokens"]
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except:
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curr_max_tokens = 2048
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if curr_max_tokens < prompt_tokens:
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prompt_larger_than_model = True
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for available_model in available_models:
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try:
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curr_max_tokens = litellm.get_max_tokens(available_model)[
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"max_tokens"
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]
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if curr_max_tokens > max_tokens:
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max_tokens = curr_max_tokens
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max_model = available_model
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if curr_max_tokens > prompt_tokens:
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model = available_model
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prompt_larger_than_model = False
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except:
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continue
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if prompt_larger_than_model:
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messages = trim_messages(messages=messages, model=max_model)
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kwargs["messages"] = messages
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kwargs["model"] = model
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try:
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if model in models_with_config:
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## Moderation check
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if config["model"][model].get("needs_moderation"):
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input = " ".join(message["content"] for message in messages)
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response = litellm.moderation(input=input)
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flagged = response["results"][0]["flagged"]
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if flagged:
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raise Exception("This response was flagged as inappropriate")
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## Model-specific Error Handling
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error_handling = None
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if config["model"][model].get("error_handling"):
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error_handling = config["model"][model]["error_handling"]
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try:
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response = litellm.completion(**kwargs)
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return response
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except Exception as e:
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exception_name = type(e).__name__
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fallback_model = None
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if error_handling and exception_name in error_handling:
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error_handler = error_handling[exception_name]
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# either switch model or api key
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fallback_model = error_handler.get("fallback_model", None)
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if fallback_model:
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kwargs["model"] = fallback_model
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return litellm.completion(**kwargs)
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raise e
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else:
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return litellm.completion(**kwargs)
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except Exception as e:
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if fallback_models:
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model = fallback_models.pop(0)
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return completion_with_fallbacks(
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model=model, messages=messages, fallbacks=fallback_models
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)
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raise e
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def completion_with_fallbacks(**kwargs):
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def completion_with_fallbacks(**kwargs):
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nested_kwargs = kwargs.pop("kwargs", {})
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nested_kwargs = kwargs.pop("kwargs", {})
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response = None
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response = None
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