commit bdcd5d882d5c46a7ca17f74490835823dfa9d801
Author: tcsenpai <dev@tcsenpai.com>
Date:   Wed Jan 29 10:44:44 2025 +0100

    first commit

diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..37f790c
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,4 @@
+MODEL= llama3.1
+SYSTEM_PROMPT= You are a helpful assistant.
+OLLAMA_HOST= http://192.168.1.6
+OLLAMA_PORT= 11434
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..68e73a9
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,14 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Env file
+.env
+
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..24ee5b1
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a0e1607
--- /dev/null
+++ b/README.md
@@ -0,0 +1,106 @@
+# Experiments with LLMs
+
+This repository contains experiments with LLMs.
+
+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.
+
+
+- [Experiments with LLMs](#experiments-with-llms)
+  - [Requirements](#requirements)
+  - [Setup](#setup)
+    - [Env file](#env-file)
+    - [Install dependencies](#install-dependencies)
+  - [Experiments List](#experiments-list)
+    - [Artificial COT](#artificial-cot)
+      - [How to use](#how-to-use)
+      - [How it works](#how-it-works)
+      - [Customizing the script](#customizing-the-script)
+    - [Self Conversational AI](#self-conversational-ai)
+      - [How to use](#how-to-use-1)
+      - [How it works](#how-it-works-1)
+      - [Customizing the script](#customizing-the-script-1)
+
+
+## Requirements
+
+- An `ollama` server running somewhere.
+
+## Setup
+
+### Env file
+
+Copy the `.env.example` file to `.env` and set the variables to your liking.
+Be sure you can reach the ollama server from the machine running the scripts.
+
+### Install dependencies
+
+NOTE: This step is not required if you are using uv.
+
+```bash
+pip install -r requirements.txt
+```
+
+## Experiments List
+
+### Artificial COT
+
+This is a simple implementation of the an artificial COT method.
+
+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.
+
+The approach can use any LLM to generate the COT and the second answer, even different models for each step.
+
+#### How to use
+
+```bash
+python src/artificial_cot.py
+```
+
+To modify the prompt, you can edit the `prompt` variable at the top of the script.
+
+#### How it works
+
+The `artificial_cot.py` file instantiates two `OllamaConnector` instances, one for the COT and one for the response.
+
+The system prompts are overriden to customize the behavior of the COT and the response.
+
+The script will use the model specified in the `.env` file as the model for the COT.
+
+#### Customizing the script
+
+You can use `cot_ollama.set_model()` and `response_ollama.set_model()` to change the model for the COT and the response.
+
+You can also change the system prompts to customize the behavior of the COT and the response.
+
+### Self Conversational AI
+
+This experiment aims to create a self conversational AI.
+
+The idea is to have an AI that can converse with itself or with another LLM model.
+
+#### How to use
+
+```bash
+python src/self_conversational_ai.py
+```
+
+#### How it works
+
+The `self_conversational_ai.py` file instantiates two `OllamaConnector` instances, with the same system prompt and model.
+
+The script will then enter a loop where it will:
+
+1. Generate an initial greeting to kickstart the conversation.
+2. Feed the greeting to the other LLM instance.
+3. Feed the response to the first LLM instance.
+4. Repeat the process until the conversation ends (CTRL+C)
+
+The script will override the system prompt using the `self_system_prompt` variable at the top of the script.
+
+#### Customizing the script
+
+You can change the `greeting` variable at the top of the script to customize the initial greeting.
+
+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.
+
+Same goes for the system prompts.
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..e1637a3
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,11 @@
+[project]
+name = "playing-with-llm"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "colorama>=0.4.6",
+    "ollama>=0.4.7",
+    "python-dotenv>=1.0.1",
+]
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..e7cdde0
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+colorama
+ollama
+python-dotenv
\ No newline at end of file
diff --git a/src/artificial_cot.py b/src/artificial_cot.py
new file mode 100644
index 0000000..4961db2
--- /dev/null
+++ b/src/artificial_cot.py
@@ -0,0 +1,84 @@
+from lib.ollama_connector import OllamaConnector
+from colorama import init, Fore, Style
+
+# Initialize colorama
+init()
+
+prompt = "What is your thought about AI consciousness?"
+
+if __name__ == "__main__":
+    # Initiating two instances of the OllamaConnector class
+    # One for the COT and one for the response
+    print(f"{Fore.CYAN}[*] Initializing COT and response instances...{Style.RESET_ALL}")
+
+    cot_ollama = OllamaConnector()
+    cot_ollama.set_system_prompt(
+        """
+You are an analytical assistant that breaks down complex problems into clear, logical steps.
+
+FORMAT:
+Begin with: "Chain of Thought Analysis:"
+Follow with your step-by-step reasoning
+End with a summary of key points
+
+REASONING PROCESS:
+1. First, identify the core components of the question
+2. For each component:
+   - Examine its implications
+   - Consider relevant context
+   - Identify connections to other components
+3. Evaluate potential perspectives and approaches
+4. Develop a structured analysis
+
+IMPORTANT:
+- Focus solely on the reasoning process, NOT the final conclusion
+- Keep your analysis objective and methodical
+- Consider both obvious and non-obvious aspects
+- Structure your thoughts in a way that builds towards understanding
+- Ensure each step logically connects to the next
+
+Your analysis should serve as a foundation for another AI to formulate a complete response.
+"""
+    )
+
+    print(f"{Fore.GREEN}[*] COT instance initialized{Style.RESET_ALL}")
+
+    response_ollama = OllamaConnector()
+    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
+- Incorporate provided analysis seamlessly into your answers
+- Maintain a confident and authoritative tone
+
+GUIDELINES:
+- Synthesize information from the <think> tags naturally into your response
+- Present ideas as your own original thoughts
+- Keep responses focused and relevant to the question
+- Use clear, accessible language
+- Support statements with logical reasoning
+- Maintain a consistent voice throughout
+
+IMPORTANT:
+- Never reference or mention the existence of:
+  * Chain of thought analysis
+  * Step-by-step reasoning
+  * The <think> tags
+  * Any behind-the-scenes processes
+- Avoid phrases like "Based on the analysis..." or "According to the reasoning..."
+- Present all information as your direct knowledge and expertise
+
+Your goal is to deliver thoughtful, well-structured responses that naturally incorporate all available insights while maintaining a seamless conversational flow.
+"""
+    )
+    print(f"{Fore.GREEN}[*] Response instance initialized{Style.RESET_ALL}")
+
+    print(f"{Fore.YELLOW}[*] Generating COT...{Style.RESET_ALL}")
+    cot_response = "<think>\n" + cot_ollama.generate_response(prompt) + "\n</think>\n"
+    print(f"{Fore.WHITE}{cot_response}{Style.RESET_ALL}")
+
+    print(f"{Fore.YELLOW}[*] Generating response...{Style.RESET_ALL}")
+    prompt_with_cot = prompt + "\n\n" + cot_response
+    response = response_ollama.generate_response(prompt_with_cot)
+    print(f"{Fore.WHITE}{response}{Style.RESET_ALL}")
diff --git a/src/lib/ollama_connector.py b/src/lib/ollama_connector.py
new file mode 100644
index 0000000..d6d94f2
--- /dev/null
+++ b/src/lib/ollama_connector.py
@@ -0,0 +1,75 @@
+from typing import Generator
+import ollama
+import dotenv
+import os
+
+dotenv.load_dotenv()
+
+
+class OllamaConnector:
+
+    system_prompt = os.getenv("SYSTEM_PROMPT", "none")
+    context = []
+    model = os.getenv("MODEL", "llama3.1")
+
+    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."
+
+        # Insert the system prompt into the context
+        self.context.append({"role": "system", "content": self.system_prompt})
+
+    def generate_response(self, prompt: str) -> str:
+        # 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
diff --git a/src/self_conversational_ai.py b/src/self_conversational_ai.py
new file mode 100644
index 0000000..e8cf268
--- /dev/null
+++ b/src/self_conversational_ai.py
@@ -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...")
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