uv use

README.md

@@ -2,6 +2,11 @@
 
 AllEndpoints is a powerful Python module for making inferences with various LLM providers through a unified interface. It supports multiple providers including Ollama (local), HuggingFace, Together, Google Gemini, AIQL, Groq, NVIDIA, and GitHub Copilot APIs.
 
+> **Quick Start**: With [uv](https://github.com/astral-sh/uv) installed, you can run AllEndpoints without explicit dependency installation:
+> ```bash
+> uv run allendpoints.py --list
+> ```
+
 ## Table of Contents
 
 - [Installation](#installation)
@@ -26,10 +31,24 @@ AllEndpoints is a powerful Python module for making inferences with various LLM
    cd allendpoints
    ```
 
-2. Install the required dependencies using the requirements.txt file:
+2. Choose one of the following installation methods:
+
+   ### Option A: Using pip
+   Install the required dependencies using the requirements.txt file:
    ```bash
    pip install -r requirements.txt
    ```
+   Then run the script directly:
+   ```bash
+   python allendpoints.py [arguments]
+   ```
+
+   ### Option B: Using uv (Recommended)
+   If you have [uv](https://github.com/astral-sh/uv) installed, you can run the script without explicitly installing dependencies:
+   ```bash
+   uv run allendpoints.py [arguments]
+   ```
+   This will automatically create a virtual environment and install all required dependencies on first run.
 
 3. Install Ollama (optional, for local inference):
    - [Ollama Installation Guide](https://github.com/ollama/ollama)
@@ -140,32 +159,56 @@ options:
 
 **List all available providers and models:**
 ```bash
+# Using python directly
 python allendpoints.py --list
+
+# Using uv run
+uv run allendpoints.py --list
 ```
 
 **Run inference with a specific provider and model:**
 ```bash
+# Using python directly
 python allendpoints.py "What is the capital of France?" --provider ollama --model llama3.2:3b
+
+# Using uv run
+uv run allendpoints.py "What is the capital of France?" --provider ollama --model llama3.2:3b
 ```
 
 **Run inference with a specific provider and its default model:**
 ```bash
+# Using python directly
 python allendpoints.py "Explain quantum computing" --provider gemini
+
+# Using uv run
+uv run allendpoints.py "Explain quantum computing" --provider gemini
 ```
 
 **Run inference with a custom system prompt:**
 ```bash
+# Using python directly
 python allendpoints.py "Write a poem about AI" --provider ollama --model llama3.2:3b --system "You are a poetic assistant."
+
+# Using uv run
+uv run allendpoints.py "Write a poem about AI" --provider ollama --model llama3.2:3b --system "You are a poetic assistant."
 ```
 
 **Run inference on all available providers and models:**
 ```bash
+# Using python directly
 python allendpoints.py "What is the meaning of life?" -a
+
+# Using uv run
+uv run allendpoints.py "What is the meaning of life?" -a
 ```
 
 **Run with debug output:**
 ```bash
+# Using python directly
 python allendpoints.py "How does a nuclear reactor work?" --provider nvidia --model qwen2.5-coder-32b --debug
+
+# Using uv run
+uv run allendpoints.py "How does a nuclear reactor work?" --provider nvidia --model qwen2.5-coder-32b --debug
 ```
 
 ## Using as a Python Module

pyproject.toml

@@ -0,0 +1,17 @@
+[project]
+name = "allendpoints"
+version = "0.1.0"
+description = "A powerful Python module for making inferences with various LLM providers through a unified interface."
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "ollama>=0.1.6",
+    "requests>=2.31.0",
+    "google-generativeai>=0.3.0",
+    "huggingface_hub>=0.19.0",
+    "together>=0.2.8",
+    "groq>=0.4.0",
+    "openai>=1.6.0",
+    "colorama>=0.4.6",
+    "python-dotenv>=1.0.0"
+]