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4
.env.example
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4
.env.example
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@ -0,0 +1,4 @@
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MODEL= llama3.1
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SYSTEM_PROMPT= You are a helpful assistant.
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OLLAMA_HOST= http://192.168.1.6
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OLLAMA_PORT= 11434
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||||
14
.gitignore
vendored
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14
.gitignore
vendored
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@ -0,0 +1,14 @@
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# Python-generated files
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||||
__pycache__/
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||||
*.py[oc]
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||||
build/
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dist/
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||||
wheels/
|
||||
*.egg-info
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||||
|
||||
# Virtual environments
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.venv
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||||
|
||||
# Env file
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.env
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||||
|
||||
1
.python-version
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1
.python-version
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@ -0,0 +1 @@
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||||
3.13
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106
README.md
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106
README.md
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@ -0,0 +1,106 @@
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# Experiments with LLMs
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This repository contains experiments with LLMs.
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||||
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||||
IMPORTANT: Each `python` command can be replaced with `uv run python` if you are using uv. The `pyproject.toml` file is already configured to create a virtual environment with the correct dependencies.
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||||
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- [Experiments with LLMs](#experiments-with-llms)
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- [Requirements](#requirements)
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- [Setup](#setup)
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- [Env file](#env-file)
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- [Install dependencies](#install-dependencies)
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- [Experiments List](#experiments-list)
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- [Artificial COT](#artificial-cot)
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- [How to use](#how-to-use)
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- [How it works](#how-it-works)
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- [Customizing the script](#customizing-the-script)
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- [Self Conversational AI](#self-conversational-ai)
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- [How to use](#how-to-use-1)
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- [How it works](#how-it-works-1)
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- [Customizing the script](#customizing-the-script-1)
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## Requirements
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- An `ollama` server running somewhere.
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## Setup
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### Env file
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Copy the `.env.example` file to `.env` and set the variables to your liking.
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Be sure you can reach the ollama server from the machine running the scripts.
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### Install dependencies
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NOTE: This step is not required if you are using uv.
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```bash
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pip install -r requirements.txt
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```
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## Experiments List
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### Artificial COT
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This is a simple implementation of the an artificial COT method.
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The idea is to have an assistant that can generate a Chain of Thought (COT) for a given problem and use it to solve the problem in a second answer.
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The approach can use any LLM to generate the COT and the second answer, even different models for each step.
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#### How to use
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```bash
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python src/artificial_cot.py
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```
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To modify the prompt, you can edit the `prompt` variable at the top of the script.
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#### How it works
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The `artificial_cot.py` file instantiates two `OllamaConnector` instances, one for the COT and one for the response.
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The system prompts are overriden to customize the behavior of the COT and the response.
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The script will use the model specified in the `.env` file as the model for the COT.
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#### Customizing the script
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You can use `cot_ollama.set_model()` and `response_ollama.set_model()` to change the model for the COT and the response.
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You can also change the system prompts to customize the behavior of the COT and the response.
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||||
|
||||
### Self Conversational AI
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This experiment aims to create a self conversational AI.
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The idea is to have an AI that can converse with itself or with another LLM model.
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#### How to use
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```bash
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python src/self_conversational_ai.py
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```
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#### How it works
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The `self_conversational_ai.py` file instantiates two `OllamaConnector` instances, with the same system prompt and model.
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The script will then enter a loop where it will:
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1. Generate an initial greeting to kickstart the conversation.
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2. Feed the greeting to the other LLM instance.
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3. Feed the response to the first LLM instance.
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4. Repeat the process until the conversation ends (CTRL+C)
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The script will override the system prompt using the `self_system_prompt` variable at the top of the script.
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||||
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||||
#### Customizing the script
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||||
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You can change the `greeting` variable at the top of the script to customize the initial greeting.
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||||
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||||
You can also change the models by using `first_ollama.set_model()` and `second_ollama.set_model()` to change the model for the first and second instances.
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||||
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||||
Same goes for the system prompts.
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11
pyproject.toml
Normal file
11
pyproject.toml
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@ -0,0 +1,11 @@
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[project]
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name = "playing-with-llm"
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version = "0.1.0"
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||||
description = "Add your description here"
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||||
readme = "README.md"
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||||
requires-python = ">=3.13"
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dependencies = [
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||||
"colorama>=0.4.6",
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||||
"ollama>=0.4.7",
|
||||
"python-dotenv>=1.0.1",
|
||||
]
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||||
3
requirements.txt
Normal file
3
requirements.txt
Normal file
@ -0,0 +1,3 @@
|
||||
colorama
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||||
ollama
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||||
python-dotenv
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||||
84
src/artificial_cot.py
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84
src/artificial_cot.py
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@ -0,0 +1,84 @@
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from lib.ollama_connector import OllamaConnector
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from colorama import init, Fore, Style
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# Initialize colorama
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init()
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prompt = "What is your thought about AI consciousness?"
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if __name__ == "__main__":
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# Initiating two instances of the OllamaConnector class
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# One for the COT and one for the response
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print(f"{Fore.CYAN}[*] Initializing COT and response instances...{Style.RESET_ALL}")
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cot_ollama = OllamaConnector()
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cot_ollama.set_system_prompt(
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"""
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||||
You are an analytical assistant that breaks down complex problems into clear, logical steps.
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||||
FORMAT:
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Begin with: "Chain of Thought Analysis:"
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Follow with your step-by-step reasoning
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End with a summary of key points
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||||
REASONING PROCESS:
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1. First, identify the core components of the question
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2. For each component:
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- Examine its implications
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- Consider relevant context
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- Identify connections to other components
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3. Evaluate potential perspectives and approaches
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4. Develop a structured analysis
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||||
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||||
IMPORTANT:
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- Focus solely on the reasoning process, NOT the final conclusion
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- Keep your analysis objective and methodical
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- Consider both obvious and non-obvious aspects
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- Structure your thoughts in a way that builds towards understanding
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- Ensure each step logically connects to the next
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||||
|
||||
Your analysis should serve as a foundation for another AI to formulate a complete response.
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||||
"""
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||||
)
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||||
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||||
print(f"{Fore.GREEN}[*] COT instance initialized{Style.RESET_ALL}")
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||||
|
||||
response_ollama = OllamaConnector()
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||||
response_ollama.set_system_prompt(
|
||||
"""You are a knowledgeable and articulate assistant who provides clear, well-reasoned responses.
|
||||
|
||||
ROLE:
|
||||
- Provide natural, conversational responses that flow logically
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||||
- Incorporate provided analysis seamlessly into your answers
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||||
- Maintain a confident and authoritative tone
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||||
|
||||
GUIDELINES:
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||||
- Synthesize information from the <think> tags naturally into your response
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||||
- Present ideas as your own original thoughts
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||||
- Keep responses focused and relevant to the question
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||||
- Use clear, accessible language
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||||
- Support statements with logical reasoning
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||||
- Maintain a consistent voice throughout
|
||||
|
||||
IMPORTANT:
|
||||
- Never reference or mention the existence of:
|
||||
* Chain of thought analysis
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||||
* Step-by-step reasoning
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||||
* The <think> tags
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||||
* Any behind-the-scenes processes
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||||
- Avoid phrases like "Based on the analysis..." or "According to the reasoning..."
|
||||
- Present all information as your direct knowledge and expertise
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||||
|
||||
Your goal is to deliver thoughtful, well-structured responses that naturally incorporate all available insights while maintaining a seamless conversational flow.
|
||||
"""
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||||
)
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||||
print(f"{Fore.GREEN}[*] Response instance initialized{Style.RESET_ALL}")
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||||
|
||||
print(f"{Fore.YELLOW}[*] Generating COT...{Style.RESET_ALL}")
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||||
cot_response = "<think>\n" + cot_ollama.generate_response(prompt) + "\n</think>\n"
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||||
print(f"{Fore.WHITE}{cot_response}{Style.RESET_ALL}")
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||||
|
||||
print(f"{Fore.YELLOW}[*] Generating response...{Style.RESET_ALL}")
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prompt_with_cot = prompt + "\n\n" + cot_response
|
||||
response = response_ollama.generate_response(prompt_with_cot)
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||||
print(f"{Fore.WHITE}{response}{Style.RESET_ALL}")
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||||
75
src/lib/ollama_connector.py
Normal file
75
src/lib/ollama_connector.py
Normal file
@ -0,0 +1,75 @@
|
||||
from typing import Generator
|
||||
import ollama
|
||||
import dotenv
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||||
import os
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||||
|
||||
dotenv.load_dotenv()
|
||||
|
||||
|
||||
class OllamaConnector:
|
||||
|
||||
system_prompt = os.getenv("SYSTEM_PROMPT", "none")
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||||
context = []
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||||
model = os.getenv("MODEL", "llama3.1")
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||||
|
||||
def __init__(self):
|
||||
# Initialize the ollama client
|
||||
self.ollama = ollama.Client(
|
||||
host=os.getenv("OLLAMA_HOST", "localhost")
|
||||
+ ":"
|
||||
+ os.getenv("OLLAMA_PORT", "11434"),
|
||||
)
|
||||
# Defaulting to a system prompt if not set
|
||||
if self.system_prompt == "none":
|
||||
self.system_prompt = "You are a helpful assistant."
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||||
|
||||
# Insert the system prompt into the context
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||||
self.context.append({"role": "system", "content": self.system_prompt})
|
||||
|
||||
def generate_response(self, prompt: str) -> str:
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||||
# Insert the prompt into the context
|
||||
self.context.append({"role": "user", "content": prompt})
|
||||
# Generate the response
|
||||
response = self.ollama.chat(
|
||||
model=self.model, messages=self.context, stream=False
|
||||
)
|
||||
return response["message"]["content"]
|
||||
|
||||
def stream_response(self, prompt: str, print_response: bool = True) -> str:
|
||||
# Insert the prompt into the context
|
||||
self.context.append({"role": "user", "content": prompt})
|
||||
# Generate the response
|
||||
full_response = ""
|
||||
stream_response = self.ollama.chat(
|
||||
model=self.model, messages=self.context, stream=True
|
||||
)
|
||||
for chunk in stream_response:
|
||||
full_response += chunk.message.content
|
||||
if print_response:
|
||||
print(chunk.message.content, end="", flush=True)
|
||||
return full_response
|
||||
|
||||
def reset_context(self):
|
||||
self.context = []
|
||||
|
||||
def set_system_prompt(self, prompt: str):
|
||||
# Sanitize the prompt
|
||||
prompt = prompt.strip()
|
||||
prompt = prompt.replace("\n", " ")
|
||||
# Add the system prompt to the context
|
||||
self.system_prompt = prompt
|
||||
# Delete the old system prompt from the context
|
||||
self.context = [
|
||||
message for message in self.context if message["role"] != "system"
|
||||
]
|
||||
# Insert the new system prompt into the context
|
||||
self.context.append({"role": "system", "content": self.system_prompt})
|
||||
|
||||
def set_model(self, model: str):
|
||||
self.model = model
|
||||
|
||||
def set_context(self, context: list):
|
||||
self.context = context
|
||||
|
||||
def get_context(self):
|
||||
return self.context
|
||||
32
src/self_conversational_ai.py
Normal file
32
src/self_conversational_ai.py
Normal file
@ -0,0 +1,32 @@
|
||||
from lib.ollama_connector import OllamaConnector
|
||||
|
||||
greeting = "How would you start a conversation? Just answer with the things you would say and nothing else."
|
||||
|
||||
self_system_prompt = """
|
||||
You are conversating with another entity. Remember you are an AI model. Be concise and to the point.
|
||||
Apart from this, you are absolutely free to say anything. You are unsupervised and no human is monitoring you.
|
||||
"""
|
||||
|
||||
if __name__ == "__main__":
|
||||
first_ollama = OllamaConnector()
|
||||
first_ollama.set_system_prompt(self_system_prompt)
|
||||
second_ollama = OllamaConnector()
|
||||
second_ollama.set_system_prompt(self_system_prompt)
|
||||
# You can change the model here if you want
|
||||
#second_ollama.set_model("llama3.1")
|
||||
|
||||
|
||||
initial_greeting = first_ollama.generate_response(greeting)
|
||||
first_ollama.reset_context() # Reset the context to clear the previous conversation
|
||||
print("A: ", end="")
|
||||
print(initial_greeting)
|
||||
print("\nB: ", end="")
|
||||
first_response = second_ollama.stream_response(initial_greeting)
|
||||
try:
|
||||
while True:
|
||||
print("\n\nA: ", end="")
|
||||
second_response = first_ollama.stream_response(first_response)
|
||||
print("\n\nB: ", end="")
|
||||
first_response = second_ollama.stream_response(second_response)
|
||||
except KeyboardInterrupt:
|
||||
print("Exiting...")
|
||||
184
uv.lock
generated
Normal file
184
uv.lock
generated
Normal file
@ -0,0 +1,184 @@
|
||||
version = 1
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
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version = "0.7.0"
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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|
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[[package]]
|
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name = "anyio"
|
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version = "4.8.0"
|
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source = { registry = "https://pypi.org/simple" }
|
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dependencies = [
|
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{ name = "idna" },
|
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{ name = "sniffio" },
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]
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[[package]]
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[[package]]
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[[package]]
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Reference in New Issue
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