try:
from ollama import Client
except ImportError:
raise ImportError(
"The 'ollama' library is required. Please install it using 'pip install ollama'."
)
from selfmemory.llms.base import LLMBase
from selfmemory.llms.configs import BaseLlmConfig, OllamaConfig
class OllamaLLM(LLMBase):
def __init__(self, config: BaseLlmConfig | OllamaConfig | dict | None = None):
# Convert to OllamaConfig if needed
if config is None:
config = OllamaConfig()
elif isinstance(config, dict):
config = OllamaConfig(**config)
elif isinstance(config, BaseLlmConfig) and not isinstance(config, OllamaConfig):
# Convert BaseLlmConfig to OllamaConfig
config = OllamaConfig(
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 = "llama3.1:70b"
self.client = Client(host=self.config.ollama_base_url)
def _parse_response(self, response, tools):
"""
Process the response based on whether tools are used or not.
Args:
response: The raw response from API.
tools: The list of tools provided in the request.
Returns:
str or dict: The processed response.
"""
# Get the content from response
if isinstance(response, dict):
content = response["message"]["content"]
else:
content = response.message.content
if tools:
processed_response = {
"content": content,
"tool_calls": [],
}
# Ollama doesn't support tool calls in the same way, so we return the content
return processed_response
return content
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 Ollama.
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 Ollama-specific parameters.
Returns:
str: The generated response.
"""
# Build parameters for Ollama
params = {
"model": self.config.model,
"messages": messages,
}
# Handle JSON response format by using Ollama's native format parameter
if response_format and response_format.get("type") == "json_object":
params["format"] = "json"
# Also add JSON format instruction to the last message as a fallback
if messages and messages[-1]["role"] == "user":
messages[-1]["content"] += "\n\nPlease respond with valid JSON only."
else:
messages.append(
{"role": "user", "content": "Please respond with valid JSON only."}
)
# Add options for Ollama (temperature, num_predict, top_p)
options = {
"temperature": self.config.temperature,
"num_predict": self.config.max_tokens,
"top_p": self.config.top_p,
}
params["options"] = options
# Remove OpenAI-specific parameters that Ollama doesn't support
params.pop("max_tokens", None) # Ollama uses different parameter names
response = self.client.chat(**params)
return self._parse_response(response, tools)
