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docs(completion-docs): adds more details on provider-specific params

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Krrish Dholakia 2023-10-07 13:49:19 -07:00
parent b4501f0241
commit c6d36fb59d
8 changed files with 749 additions and 35 deletions
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90
litellm/tests/test_provider_specific_config.py
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@ -50,7 +50,7 @@ def claude_test_completion():
try:
# OVERRIDE WITH DYNAMIC MAX TOKENS
response_1 = litellm.completion(
model="claude-instant-1",
model="together_ai/togethercomputer/llama-2-70b-chat",
messages=[{ "content": "Hello, how are you?","role": "user"}],
max_tokens=10
)
@ -60,7 +60,7 @@ def claude_test_completion():
# USE CONFIG TOKENS
response_2 = litellm.completion(
model="claude-instant-1",
model="together_ai/togethercomputer/llama-2-70b-chat",
messages=[{ "content": "Hello, how are you?","role": "user"}],
)
# Add any assertions here to check the response
@ -392,4 +392,88 @@ def bedrock_test_completion():
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# bedrock_test_completion()
# bedrock_test_completion()
# OpenAI Chat Completion
def openai_test_completion():
litellm.OpenAIConfig(max_tokens=10)
# litellm.set_verbose=True
try:
# OVERRIDE WITH DYNAMIC MAX TOKENS
response_1 = litellm.completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
max_tokens=100
)
response_1_text = response_1.choices[0].message.content
print(f"response_1_text: {response_1_text}")
# USE CONFIG TOKENS
response_2 = litellm.completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
)
response_2_text = response_2.choices[0].message.content
print(f"response_2_text: {response_2_text}")
assert len(response_2_text) < len(response_1_text)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# openai_test_completion()
# OpenAI Text Completion
def openai_text_completion_test():
litellm.OpenAITextCompletionConfig(max_tokens=10)
# litellm.set_verbose=True
try:
# OVERRIDE WITH DYNAMIC MAX TOKENS
response_1 = litellm.completion(
model="text-davinci-003",
messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
max_tokens=100
)
response_1_text = response_1.choices[0].message.content
print(f"response_1_text: {response_1_text}")
# USE CONFIG TOKENS
response_2 = litellm.completion(
model="text-davinci-003",
messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
)
response_2_text = response_2.choices[0].message.content
print(f"response_2_text: {response_2_text}")
assert len(response_2_text) < len(response_1_text)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# openai_text_completion_test()
# Azure OpenAI
def azure_openai_test_completion():
litellm.AzureOpenAIConfig(max_tokens=10)
# litellm.set_verbose=True
try:
# OVERRIDE WITH DYNAMIC MAX TOKENS
response_1 = litellm.completion(
model="azure/chatgpt-v-2",
messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
max_tokens=100
)
response_1_text = response_1.choices[0].message.content
print(f"response_1_text: {response_1_text}")
# USE CONFIG TOKENS
response_2 = litellm.completion(
model="azure/chatgpt-v-2",
messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
)
response_2_text = response_2.choices[0].message.content
print(f"response_2_text: {response_2_text}")
assert len(response_2_text) < len(response_1_text)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# azure_openai_test_completion()