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Merge pull request #1315 from spdustin/feature_allow_claude_prefill

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Adds "pre-fill" support for Claude
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Krish Dholakia 2024-01-08 10:48:15 +05:30 • committed by GitHub
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82
docs/my-website/docs/providers/anthropic.md
View file

@ -1,42 +1,102 @@
# Anthropic
LiteLLM supports
LiteLLM supports
- `claude-2.1`
- `claude-2`
- `claude-2.1`
- `claude-instant-1`
- `claude-instant-1.2`
## API Keys
```python
import os
```python
import os
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
```
## Sample Usage
## Usage
```python
import os
from litellm import completion
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-instant-1", messages=messages)
print(response)
```
## streaming
## Usage - "Assistant Pre-fill"
You can "put words in Claude's mouth" by including an `assistant` role message as the last item in the `messages` array.
> [!IMPORTANT]
> The returned completion will _not_ include your "pre-fill" text, since it is part of the prompt itself. Make sure to prefix Claude's completion with your pre-fill.
```python
import os
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [
{"role": "user", "content": "How do you say 'Hello' in German? Return your answer as a JSON object, like this:\n\n{ \"Hello\": \"Hallo\" }"},
{"role": "assistant", "content": "{"},
]
response = completion(model="claude-2.1", messages=messages)
print(response)
```
### Example prompt sent to Claude
```
Human: How do you say 'Hello' in German? Return your answer as a JSON object, like this:
{ "Hello": "Hallo" }
Assistant: {
```
## Usage - "System" messages
If you're using Anthropic's Claude 2.1 with Bedrock, `system` role messages are properly formatted for you.
```python
import os
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [
{"role": "system", "content": "You are a snarky assistant."},
{"role": "user", "content": "How do I boil water?"},
]
response = completion(model="claude-2.1", messages=messages)
```
### Example prompt sent to Claude
```
You are a snarky assistant.
Human: How do I boil water?
Assistant:
```
## Streaming
Just set `stream=True` when calling completion.
```python
import os
from litellm import completion
from litellm import completion
# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-instant-1", messages=messages, stream=True)

78
docs/my-website/docs/providers/bedrock.md
View file

@ -21,7 +21,7 @@ os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2
</a>
```python
import os
import os
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
@ -29,14 +29,77 @@ os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
response = completion(
model="anthropic.claude-instant-v1",
model="anthropic.claude-instant-v1",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
## Usage - "Assistant Pre-fill"
If you're using Anthropic's Claude with Bedrock, you can "put words in Claude's mouth" by including an `assistant` role message as the last item in the `messages` array.
> [!IMPORTANT]
> The returned completion will _**not**_ include your "pre-fill" text, since it is part of the prompt itself. Make sure to prefix Claude's completion with your pre-fill.
```python
import os
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
messages = [
{"role": "user", "content": "How do you say 'Hello' in German? Return your answer as a JSON object, like this:\n\n{ \"Hello\": \"Hallo\" }"},
{"role": "assistant", "content": "{"},
]
response = completion(model="anthropic.claude-v2", messages=messages)
```
### Example prompt sent to Claude
```
Human: How do you say 'Hello' in German? Return your answer as a JSON object, like this:
{ "Hello": "Hallo" }
Assistant: {
```
## Usage - "System" messages
If you're using Anthropic's Claude 2.1 with Bedrock, `system` role messages are properly formatted for you.
```python
import os
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
messages = [
{"role": "system", "content": "You are a snarky assistant."},
{"role": "user", "content": "How do I boil water?"},
]
response = completion(model="anthropic.claude-v2:1", messages=messages)
```
### Example prompt sent to Claude
```
You are a snarky assistant.
Human: How do I boil water?
Assistant:
```
## Usage - Streaming
```python
import os
import os
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
@ -44,7 +107,7 @@ os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
response = completion(
model="anthropic.claude-instant-v1",
model="anthropic.claude-instant-v1",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True
)
@ -79,11 +142,11 @@ for chunk in response:
### Passing credentials as parameters - Completion()
Pass AWS credentials as parameters to litellm.completion
```python
import os
import os
from litellm import completion
response = completion(
model="anthropic.claude-instant-v1",
model="anthropic.claude-instant-v1",
messages=[{ "content": "Hello, how are you?","role": "user"}],
aws_access_key_id="",
aws_secret_access_key="",
@ -133,10 +196,11 @@ response = completion(
```
## Supported AWS Bedrock Models
Here's an example of using a bedrock model with LiteLLM
Here's an example of using a bedrock model with LiteLLM
| Model Name | Command |
|--------------------------|------------------------------------------------------------------|
| Anthropic Claude-V2.1 | `completion(model='anthropic.claude-v2:1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` |
| Anthropic Claude-V2 | `completion(model='anthropic.claude-v2', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` |
| Anthropic Claude-Instant V1 | `completion(model='anthropic.claude-instant-v1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` |
| Anthropic Claude-V1 | `completion(model='anthropic.claude-v1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` |

15
litellm/llms/prompt_templates/factory.py
View file

@ -295,6 +295,9 @@ def claude_2_1_pt(
if system message is passed in, you can only do system, human, assistant or system, human
if a system message is passed in and followed by an assistant message, insert a blank human message between them.
Additionally, you can "put words in Claude's mouth" by ending with an assistant message.
See: https://docs.anthropic.com/claude/docs/put-words-in-claudes-mouth
"""
class AnthropicConstants(Enum):
@ -311,7 +314,8 @@ def claude_2_1_pt(
if idx > 0 and messages[idx - 1]["role"] == "system":
prompt += f"{AnthropicConstants.HUMAN_PROMPT.value}" # Insert a blank human message
prompt += f"{AnthropicConstants.AI_PROMPT.value}{message['content']}"
prompt += f"{AnthropicConstants.AI_PROMPT.value}" # prompt must end with \"\n\nAssistant: " turn
if messages[-1]["role"] != "assistant":
prompt += f"{AnthropicConstants.AI_PROMPT.value}" # prompt must end with \"\n\nAssistant: " turn
return prompt
@ -364,6 +368,10 @@ def format_prompt_togetherai(messages, prompt_format, chat_template):
def anthropic_pt(
messages: list,
): # format - https://docs.anthropic.com/claude/reference/complete_post
"""
You can "put words in Claude's mouth" by ending with an assistant message.
See: https://docs.anthropic.com/claude/docs/put-words-in-claudes-mouth
"""
class AnthropicConstants(Enum):
HUMAN_PROMPT = "\n\nHuman: "
AI_PROMPT = "\n\nAssistant: "
@ -382,7 +390,8 @@ def anthropic_pt(
idx == 0 and message["role"] == "assistant"
): # ensure the prompt always starts with `\n\nHuman: `
prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}" + prompt
prompt += f"{AnthropicConstants.AI_PROMPT.value}"
if messages[-1]["role"] != "assistant":
prompt += f"{AnthropicConstants.AI_PROMPT.value}"
return prompt
@ -580,7 +589,7 @@ def prompt_factory(
if custom_llm_provider == "ollama":
return ollama_pt(model=model, messages=messages)
elif custom_llm_provider == "anthropic":
if "claude-2.1" in model:
if any(_ in model for _ in ["claude-2.1","claude-v2:1"]):
return claude_2_1_pt(messages=messages)
else:
return anthropic_pt(messages=messages)

37
litellm/tests/test_bedrock_completion.py
View file

@ -66,6 +66,43 @@ def test_completion_bedrock_claude_completion_auth():
test_completion_bedrock_claude_completion_auth()
def test_completion_bedrock_claude_2_1_completion_auth():
print("calling bedrock claude 2.1 completion params auth")
import os
aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"]
aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"]
aws_region_name = os.environ["AWS_REGION_NAME"]
os.environ.pop("AWS_ACCESS_KEY_ID", None)
os.environ.pop("AWS_SECRET_ACCESS_KEY", None)
os.environ.pop("AWS_REGION_NAME", None)
try:
response = completion(
model="bedrock/anthropic.claude-v2:1",
messages=messages,
max_tokens=10,
temperature=0.1,
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_region_name=aws_region_name,
)
# Add any assertions here to check the response
print(response)
os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id
os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key
os.environ["AWS_REGION_NAME"] = aws_region_name
except RateLimitError:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_completion_bedrock_claude_2_1_completion_auth()
def test_completion_bedrock_claude_external_client_auth():
print("\ncalling bedrock claude external client auth")
import os

89
litellm/tests/test_prompt_factory.py
View file

@ -2,27 +2,96 @@
# This tests if prompts are being correctly formatted
import sys
import os
import io
sys.path.insert(0, os.path.abspath("../.."))
# from litellm.llms.prompt_templates.factory import prompt_factory
from litellm import completion
from litellm.llms.prompt_templates.factory import (
anthropic_pt,
claude_2_1_pt,
llama_2_chat_pt,
)
def codellama_prompt_format():
model = "huggingface/codellama/CodeLlama-7b-Instruct-hf"
def test_codellama_prompt_format():
messages = [
{"role": "system", "content": "You are a good bot"},
{"role": "user", "content": "Hey, how's it going?"},
]
expected_response = """[INST] <<SYS>>
You are a good bot
<</SYS>>
[/INST]
[INST] Hey, how's it going? [/INST]"""
response = completion(model=model, messages=messages)
print(response)
expected_prompt = "<s>[INST] <<SYS>>\nYou are a good bot\n<</SYS>>\n [/INST]\n[INST] Hey, how's it going? [/INST]\n"
assert llama_2_chat_pt(messages) == expected_prompt
def test_claude_2_1_pt_formatting():
# Test case: User only, should add Assistant
messages = [{"role": "user", "content": "Hello"}]
expected_prompt = "\n\nHuman: Hello\n\nAssistant: "
assert claude_2_1_pt(messages) == expected_prompt
# Test case: System, User, and Assistant "pre-fill" sequence,
# Should return pre-fill
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": 'Please return "Hello World" as a JSON object.'},
{"role": "assistant", "content": "{"},
]
expected_prompt = 'You are a helpful assistant.\n\nHuman: Please return "Hello World" as a JSON object.\n\nAssistant: {'
assert claude_2_1_pt(messages) == expected_prompt
# Test case: System, Assistant sequence, should insert blank Human message
# before Assistant pre-fill
messages = [
{"role": "system", "content": "You are a storyteller."},
{"role": "assistant", "content": "Once upon a time, there "},
]
expected_prompt = (
"You are a storyteller.\n\nHuman: \n\nAssistant: Once upon a time, there "
)
assert claude_2_1_pt(messages) == expected_prompt
# Test case: System, User sequence
messages = [
{"role": "system", "content": "System reboot"},
{"role": "user", "content": "Is everything okay?"},
]
expected_prompt = "System reboot\n\nHuman: Is everything okay?\n\nAssistant: "
assert claude_2_1_pt(messages) == expected_prompt
def test_anthropic_pt_formatting():
# Test case: User only, should add Assistant
messages = [{"role": "user", "content": "Hello"}]
expected_prompt = "\n\nHuman: Hello\n\nAssistant: "
assert anthropic_pt(messages) == expected_prompt
# Test case: System, User, and Assistant "pre-fill" sequence,
# Should return pre-fill
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": 'Please return "Hello World" as a JSON object.'},
{"role": "assistant", "content": "{"},
]
expected_prompt = '\n\nHuman: <admin>You are a helpful assistant.</admin>\n\nHuman: Please return "Hello World" as a JSON object.\n\nAssistant: {'
assert anthropic_pt(messages) == expected_prompt
# Test case: System, Assistant sequence, should NOT insert blank Human message
# before Assistant pre-fill, because "System" messages are Human
# messages wrapped with <admin></admin>
messages = [
{"role": "system", "content": "You are a storyteller."},
{"role": "assistant", "content": "Once upon a time, there "},
]
expected_prompt = "\n\nHuman: <admin>You are a storyteller.</admin>\n\nAssistant: Once upon a time, there "
assert anthropic_pt(messages) == expected_prompt
# Test case: System, User sequence
messages = [
{"role": "system", "content": "System reboot"},
{"role": "user", "content": "Is everything okay?"},
]
expected_prompt = "\n\nHuman: <admin>System reboot</admin>\n\nHuman: Is everything okay?\n\nAssistant: "
assert anthropic_pt(messages) == expected_prompt
# codellama_prompt_format()