import os
try:
import anthropic
except ImportError:
raise ImportError(
"The 'anthropic' library is required. Please install it using 'pip install anthropic'."
)
from selfmemory.llms.base import LLMBase
from selfmemory.llms.configs import AnthropicConfig, BaseLlmConfig
class AnthropicLLM(LLMBase):
def __init__(self, config: BaseLlmConfig | AnthropicConfig | dict | None = None):
# Convert to AnthropicConfig if needed
if config is None:
config = AnthropicConfig()
elif isinstance(config, dict):
config = AnthropicConfig(**config)
elif isinstance(config, BaseLlmConfig) and not isinstance(
config, AnthropicConfig
):
# Convert BaseLlmConfig to AnthropicConfig
config = AnthropicConfig(
model=config.model,
temperature=config.temperature,
api_key=config.api_key,
max_tokens=config.max_tokens,
top_p=config.top_p,
top_k=config.top_k,
enable_vision=config.enable_vision,
vision_details=config.vision_details,
http_client_proxies=config.http_client,
)
super().__init__(config)
if not self.config.model:
self.config.model = "claude-3-5-sonnet-20240620"
api_key = self.config.api_key or os.getenv("ANTHROPIC_API_KEY")
self.client = anthropic.Anthropic(api_key=api_key)
def generate_response(
self,
messages: list[dict[str, str]],
response_format=None,
tools: list[dict] | None = None,
tool_choice: str = "auto",
**kwargs,
):
"""
Generate a response based on the given messages using Anthropic.
Args:
messages (list): List of message dicts containing 'role' and 'content'.
response_format (str or object, optional): Format of the response. Defaults to "text".
tools (list, optional): List of tools that the model can call. Defaults to None.
tool_choice (str, optional): Tool choice method. Defaults to "auto".
**kwargs: Additional Anthropic-specific parameters.
Returns:
str: The generated response.
"""
# Separate system message from other messages
system_message = ""
filtered_messages = []
for message in messages:
if message["role"] == "system":
system_message = message["content"]
else:
filtered_messages.append(message)
params = self._get_supported_params(messages=messages, **kwargs)
params.update(
{
"model": self.config.model,
"messages": filtered_messages,
"system": system_message,
}
)
if (
tools
): # TODO: Remove tools if no issues found with new memory addition logic
params["tools"] = tools
params["tool_choice"] = tool_choice
response = self.client.messages.create(**params)
return response.content[0].text
