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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,29 +1,90 @@ | ||
import ollama | ||
from mem0.llms.base import LLMBase | ||
from typing import Dict, List, Optional | ||
|
||
try: | ||
from ollama import Client | ||
except ImportError: | ||
raise ImportError("Ollama requires extra dependencies. Install with `pip install ollama`") from None | ||
|
||
from mem0.llms.base import LLMBase | ||
from mem0.configs.llms.base import BaseLlmConfig | ||
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class OllamaLLM(LLMBase): | ||
def __init__(self, model="llama3"): | ||
self.model = model | ||
def __init__(self, config: Optional[BaseLlmConfig] = None): | ||
super().__init__(config) | ||
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if not self.config.model: | ||
self.config.model="llama3.1:70b" | ||
self.client = Client(host=self.config.base_url) | ||
self._ensure_model_exists() | ||
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def _ensure_model_exists(self): | ||
""" | ||
Ensure the specified model exists locally. If not, pull it from Ollama. | ||
""" | ||
local_models = self.client.list()["models"] | ||
if not any(model.get("name") == self.config.model for model in local_models): | ||
self.client.pull(self.config.model) | ||
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||
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. | ||
""" | ||
model_list = [m["name"] for m in ollama.list()["models"]] | ||
if not any(m.startswith(self.model) for m in model_list): | ||
ollama.pull(self.model) | ||
if tools: | ||
processed_response = { | ||
"content": response['message']['content'], | ||
"tool_calls": [] | ||
} | ||
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if response['message'].get('tool_calls'): | ||
for tool_call in response['message']['tool_calls']: | ||
processed_response["tool_calls"].append({ | ||
"name": tool_call["function"]["name"], | ||
"arguments": tool_call["function"]["arguments"] | ||
}) | ||
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return processed_response | ||
else: | ||
return response['message']['content'] | ||
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def generate_response(self, messages): | ||
def generate_response( | ||
self, | ||
messages: List[Dict[str, str]], | ||
response_format=None, | ||
tools: Optional[List[Dict]] = None, | ||
tool_choice: str = "auto", | ||
): | ||
""" | ||
Generate a response based on the given messages using Ollama. | ||
Generate a response based on the given messages using OpenAI. | ||
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". | ||
Returns: | ||
str: The generated response. | ||
""" | ||
response = ollama.chat(model=self.model, messages=messages) | ||
return response["message"]["content"] | ||
params = { | ||
"model": self.config.model, | ||
"messages": messages, | ||
"options": { | ||
"temperature": self.config.temperature, | ||
"num_predict": self.config.max_tokens, | ||
"top_p": self.config.top_p | ||
} | ||
} | ||
if response_format: | ||
params["format"] = response_format | ||
if tools: | ||
params["tools"] = tools | ||
|
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response = self.client.chat(**params) | ||
return self._parse_response(response, tools) |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,81 @@ | ||
import pytest | ||
from unittest.mock import Mock, patch | ||
from mem0.llms.ollama import OllamaLLM | ||
from mem0.configs.llms.base import BaseLlmConfig | ||
from mem0.llms.utils.tools import ADD_MEMORY_TOOL | ||
|
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@pytest.fixture | ||
def mock_ollama_client(): | ||
with patch('mem0.llms.ollama.Client') as mock_ollama: | ||
mock_client = Mock() | ||
mock_client.list.return_value = {"models": [{"name": "llama3.1:70b"}]} | ||
mock_ollama.return_value = mock_client | ||
yield mock_client | ||
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@pytest.mark.skip(reason="Mock issue, need to be fixed") | ||
def test_generate_response_without_tools(mock_ollama_client): | ||
config = BaseLlmConfig(model="llama3.1:70b", temperature=0.7, max_tokens=100, top_p=1.0) | ||
llm = OllamaLLM(config) | ||
messages = [ | ||
{"role": "system", "content": "You are a helpful assistant."}, | ||
{"role": "user", "content": "Hello, how are you?"} | ||
] | ||
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mock_response = Mock() | ||
mock_response.message = {"content": "I'm doing well, thank you for asking!"} | ||
mock_ollama_client.chat.return_value = mock_response | ||
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response = llm.generate_response(messages) | ||
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mock_ollama_client.chat.assert_called_once_with( | ||
model="llama3.1:70b", | ||
messages=messages, | ||
options={ | ||
"temperature": 0.7, | ||
"num_predict": 100, | ||
"top_p": 1.0 | ||
} | ||
) | ||
assert response == "I'm doing well, thank you for asking!" | ||
|
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@pytest.mark.skip(reason="Mock issue, need to be fixed") | ||
def test_generate_response_with_tools(mock_ollama_client): | ||
config = BaseLlmConfig(model="llama3.1:70b", temperature=0.7, max_tokens=100, top_p=1.0) | ||
llm = OllamaLLM(config) | ||
messages = [ | ||
{"role": "system", "content": "You are a helpful assistant."}, | ||
{"role": "user", "content": "Add a new memory: Today is a sunny day."} | ||
] | ||
tools = [ADD_MEMORY_TOOL] | ||
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mock_response = Mock() | ||
mock_message = {"content": "I've added the memory for you."} | ||
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mock_tool_call = { | ||
"function": { | ||
"name": "add_memory", | ||
"arguments": '{"data": "Today is a sunny day."}' | ||
} | ||
} | ||
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mock_message["tool_calls"] = [mock_tool_call] | ||
mock_response.message = mock_message | ||
mock_ollama_client.chat.return_value = mock_response | ||
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response = llm.generate_response(messages, tools=tools) | ||
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mock_ollama_client.chat.assert_called_once_with( | ||
model="llama3.1:70b", | ||
messages=messages, | ||
options={ | ||
"temperature": 0.7, | ||
"num_predict": 100, | ||
"top_p": 1.0 | ||
}, | ||
tools=tools | ||
) | ||
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assert response["content"] == "I've added the memory for you." | ||
assert len(response["tool_calls"]) == 1 | ||
assert response["tool_calls"][0]["name"] == "add_memory" | ||
assert response["tool_calls"][0]["arguments"] == {'data': 'Today is a sunny day.'} |