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docs/llm/01_cli__click_cli_group_.md

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+# Chapter 1: cli (Click CLI group)
+
+Welcome to the first step in your journey with `llm`! This chapter will introduce you to the `cli`, which is essentially the control panel for the entire `llm` tool. Think of it like the dashboard of a car - it's how you tell the system what to do.
+
+**Why is a CLI important?**
+
+Imagine you want to ask a large language model (LLM) a question, like "What are the best types of cheese for a cheese board?". Without a command-line interface (CLI), you'd have to write a whole program to interact with the LLM. The `cli` simplifies this by providing a structured way to send commands and receive answers directly from your terminal.
+
+**Core Concept: Click CLI Group**
+
+The `cli` in `llm` is built using a Python library called Click. Click helps create command-line interfaces that are easy to use and understand. The `cli` is organized as a *group* of commands, meaning it can perform various actions, each with its own specific instructions (or "arguments").
+
+**Key Concepts to Understand**
+
+1.  **Commands:** These are the specific actions you can perform with `llm`. Examples include `prompt`, `keys`, `logs`, `models`, `templates`, `aliases`, `plugins`, `embed`, `embed_multi`, `similar`, `embed_models`, and `collections`. Each command has a specific purpose.
+
+2.  **Arguments:** These are the pieces of information you provide to a command so it knows *how* to execute. They come in two forms:
+    *   **Positional Arguments:** These are required and their meaning depends on their order.  For example, with `llm aliases set <alias> <model_id>`, you *must* provide the alias name first, then the model ID.
+    *   **Options:** These are optional arguments that are specified using flags like `-m` or `--model`. Options usually have a default value if you don't specify them.
+
+**Let's solve our use case: Asking a question**
+
+The central use case for `llm` is asking a question to a language model. We'll use the `prompt` command to do this.
+
+To ask "What are the best types of cheese for a cheese board?" you would type the following into your terminal:
+
+```bash
+llm "What are the best types of cheese for a cheese board?"
+```
+
+This tells the `llm` tool to use the `prompt` command, with the question itself as the main argument.  The `llm` tool will then send this question to the default language model and display the answer.
+
+Example output:
+
+```
+Here are some of the best types of cheese for a cheese board:
+
+*   Brie
+*   Cheddar
+*   Gouda
+*   Blue Cheese
+*   Goat Cheese
+```
+
+**Adding Options: Using a Specific Model**
+
+Let's say you want to use a specific model, like `gpt-4o-mini`. You can do this using the `-m` or `--model` option:
+
+```bash
+llm -m gpt-4o-mini "What are the best types of cheese for a cheese board?"
+```
+
+Here, `-m gpt-4o-mini` tells the `prompt` command to use the `gpt-4o-mini` model instead of the default one.
+
+**How `cli` works: A High-Level View**
+
+When you run an `llm` command, here's what happens behind the scenes:
+
+1.  **Parsing Arguments:** The `cli` uses Click to parse the command and its arguments (like the prompt and the model name).
+2.  **Calling the Correct Function:** Based on the command and arguments, the `cli` calls the appropriate function within the `llm` code. In the above examples, the relevant `prompt` function in `llm/cli.py` will be called.
+3.  **Displaying Results:** The function interacts with the language model, gets the response, and the `cli` displays that response in your terminal.
+
+**Diving into the Code (Simplified)**
+
+Let's look at a simplified version of the `prompt` command definition within `llm/cli.py`:
+
+```python
+@click.command(name="prompt")
+@click.argument("prompt", required=False)
+@click.option("model_id", "-m", "--model", help="Model to use")
+def prompt(prompt, model_id):
+    """
+    Execute a prompt
+    """
+    # Simplified: Get the model
+    model = get_model(model_id)
+
+    # Simplified: Send the prompt to the model
+    response = model.prompt(prompt)
+
+    # Simplified: Print the response
+    print(response.text())
+```
+
+Explanation:
+
+*   `@click.command(name="prompt")`: This tells Click that this function is the code to run for the `llm prompt` command.
+*   `@click.argument("prompt", required=False)`: This defines a *positional* argument called "prompt". The `required=False` means the user doesn't have to provide it (but usually will).
+*   `@click.option("model_id", "-m", "--model", help="Model to use")`: This defines an *option* called "model". Users can specify the model using `-m` or `--model` flags. The actual name of the variable in code is `model_id`.
+*   The function body then gets the model, sends the prompt to the model, and prints the response.
+
+**Internal Implementation Walkthrough**
+
+Let's visualize how this `prompt` command works internally:
+
+```mermaid
+sequenceDiagram
+    participant User
+    participant CLI
+    participant LLM Core
+    participant Model
+
+    User->>CLI: llm "Hello, world!" -m gpt-4o-mini
+    CLI->>LLM Core: Parse command and arguments
+    CLI->>LLM Core:  Call prompt(prompt="Hello, world!", model_id="gpt-4o-mini")
+    LLM Core->>LLM Core: model = get_model("gpt-4o-mini")
+    LLM Core->>Model: Initialize gpt-4o-mini model
+    LLM Core->>Model: prompt("Hello, world!")
+    Model-->>LLM Core: Response from model
+    LLM Core-->>CLI: Response text
+    CLI->>User: Display response in terminal
+```
+
+This diagram shows:
+
+1.  The user enters the command.
+2.  The `cli` parses the command and calls the `prompt` function in the `llm` core.
+3.  The `llm` core figures out the model based on the `-m` option and fetches it, initializing it if needed.
+4.  The prompt is passed to the model, which generates a response.
+5.  The response is sent back to the `cli` and displayed to the user.
+
+**Other Useful Commands**
+
+The `cli` provides many more commands!  Here's a quick overview:
+
+*   `llm keys`: Manages API keys needed to access certain models (like OpenAI).
+*   `llm models`: Lists the available models.
+*   `llm templates`:  Allows you to create and use pre-defined prompts. See [Template](06_template.md) for details.
+*   `llm aliases`: Lets you create shorter names for models.
+
+**Conclusion**
+
+The `cli` is the foundation for interacting with `llm`. It provides a structured and easy-to-use interface for running prompts, managing models, and much more. As you continue through the tutorial, you'll explore the individual commands and options in greater detail.
+
+Now that you have a grasp of the `cli`, let's move on to understanding the concept of a [Prompt](02_prompt.md).
+
+
+---
+
+Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)

docs/llm/02_prompt.md

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+# Chapter 2: Prompt
+
+In the previous chapter, [cli (Click CLI group)](01_cli__click_cli_group_.md), you learned how to use the command-line interface (`cli`) to interact with `llm`. Now, we'll dive into the heart of that interaction: the `Prompt`.
+
+Imagine you're at a restaurant. The `cli` is like the waiter, and the `Prompt` is your order. You need to clearly specify what you want so the kitchen (the Large Language Model, or LLM) can prepare the right dish (the response).
+
+**Why do we need a `Prompt` object?**
+
+Think about asking an LLM a question. You don't just send the question directly. You might also need to specify which LLM to use, any additional instructions, or even files to reference. The `Prompt` object bundles all of this information together in a structured way. Without it, things could get messy and the LLM might not understand what you're asking!
+
+**Core Concepts: What's inside a `Prompt`?**
+
+The `Prompt` object has a few key ingredients:
+
+1.  **`prompt` (The Main Text):** This is the core of your request – the actual question or instruction you want the LLM to process.  Like "Summarize this article" or "Translate this sentence into French."
+
+2.  **`model` (The LLM to Use):**  This specifies which Large Language Model you want to use. Think of it as choosing a chef at the restaurant. Different LLMs (like `gpt-4o-mini` or `llama-3`) have different strengths.
+
+3.  **`attachments` (Additional Files):**  Sometimes you want the LLM to consider extra information, like a document or an image. These are called attachments. Imagine showing the chef a picture of the dish you want!
+
+4.  **`system` (System Instructions):** This provides high-level instructions to guide the LLM's behavior. Think of it as telling the chef your dietary restrictions or preferred cooking style. For example, "You are a helpful AI assistant."
+
+5.  **`options` (Configuration Settings):** These are extra settings that control *how* the LLM generates its response, like the maximum length of the response or how "creative" it should be. These are covered in detail in the [Options](05_options.md) chapter.
+
+**Solving the Use Case: Asking a Question with a Specific Model**
+
+Let's revisit our cheese board question from the last chapter. This time, we'll look at how the `Prompt` object is constructed behind the scenes when you use the `cli`.
+
+When you run the command:
+
+```bash
+llm -m gpt-4o-mini "What are the best types of cheese for a cheese board?"
+```
+
+The `cli` creates a `Prompt` object that looks something like this (in Python-like terms):
+
+```python
+prompt_object = Prompt(
+    prompt="What are the best types of cheese for a cheese board?",
+    model=gpt_4o_mini_model,  # Assume we have access to a gpt-4o-mini model object
+    attachments=[],
+    system=None,
+    prompt_json=None,
+    options={}
+)
+```
+
+Explanation:
+
+*   `prompt="What are the best types of cheese for a cheese board?"`: This sets the main question.
+*   `model=gpt_4o_mini_model`: This specifies that we want to use the `gpt-4o-mini` model. The `cli` figures out which model you meant based on the `-m gpt-4o-mini` option.
+*   `attachments=[], system=None, prompt_json=None, options={}`: We're not using any attachments, system instructions, or special options in this case.
+
+The `cli` then passes this `prompt_object` to the `Model` object (we'll learn more about [Model](03_model.md) in the next chapter) to generate a response.
+
+**Adding System Instructions**
+
+Let's say we want to tell the LLM to respond in a funny way. We can use the `--system` option (via the `cli` or when constructing the `Prompt` object directly if writing Python code):
+
+```bash
+llm --system "Respond in the style of a pirate." "What are the best types of cheese for a cheese board?"
+```
+
+Now the `Prompt` object would look like this:
+
+```python
+prompt_object = Prompt(
+    prompt="What are the best types of cheese for a cheese board?",
+    model=default_model,  # Assuming the default model is being used
+    attachments=[],
+    system="Respond in the style of a pirate.",
+    prompt_json=None,
+    options={}
+)
+```
+
+The LLM should now respond in a pirate-like tone!
+
+Example output:
+
+```
+Ahoy, matey!  For a cheese board fit for a pirate, ye'll be wantin' these:
+
+*   Cheddar, aged like buried treasure!
+*   Brie, smooth as calm waters.
+*   Gouda, round like a doubloon!
+```
+
+**Internal Implementation Walkthrough**
+
+Let's see what happens under the hood when the `cli` uses the `Prompt` object:
+
+```mermaid
+sequenceDiagram
+    participant User
+    participant CLI
+    participant LLM Core
+    participant Model
+
+    User->>CLI: llm "Tell me a joke." -m gpt-4o-mini
+    CLI->>LLM Core: Parse command, create Prompt object
+    LLM Core->>Model: model.prompt(prompt_object)
+    Model->>Model: Execute prompt using LLM API
+    Model-->>LLM Core: Response from LLM
+    LLM Core-->>CLI: Response text
+    CLI->>User: Display response in terminal
+```
+
+Here's the breakdown:
+
+1.  The user enters a command with a prompt and, optionally, a model choice.
+2.  The `cli` parses the command and creates a `Prompt` object, packaging the prompt text, model ID, and any other options.
+3.  The `cli` calls the `prompt` method on the selected [Model](03_model.md) (like `gpt-4o-mini`).
+4.  The `Model` object then interacts with the actual LLM API to get a response based on the information provided in the `Prompt` object.
+5.  Finally, the LLM's response is displayed to the user.
+
+**Code Example (Simplified)**
+
+Here's a simplified view of how the `Prompt` object is used within the `llm` code (referencing the `llm/models.py` file):
+
+```python
+@dataclass
+class Prompt:
+    prompt: str
+    model: "Model" # type: ignore
+    attachments: Optional[List[Attachment]]
+    system: Optional[str]
+    options: "Options" # type: ignore
+
+# ... later in the code ...
+
+class Model(ABC):
+    # ...
+    def prompt(
+        self,
+        prompt: str,
+        *,
+        attachments: Optional[List[Attachment]] = None,
+        system: Optional[str] = None,
+        stream: bool = True,
+        **options
+    ):
+        return self.response(
+            Prompt(
+                prompt,
+                attachments=attachments,
+                system=system,
+                model=self,
+                options=self.Options(**options),
+            ),
+            stream=stream,
+        )
+```
+
+Explanation:
+
+*   The `Prompt` class (using `@dataclass`) clearly defines the structure of a prompt object, including the prompt text, model, attachments, system instructions, and options.  The `type: ignore` comments are for static type checking and can be ignored for understanding the core concepts.
+*   The `Model.prompt` method creates a `Prompt` object using the provided arguments and then calls the `response` method (returning a [Response](04_response.md) object, which we'll explore in a future chapter). This illustrates how the `Prompt` object is constructed and then passed to the model for processing.
+
+**Conclusion**
+
+The `Prompt` object is a container that holds all the information needed to get a response from a Large Language Model. It ensures that your requests are structured and clear, allowing you to get the most out of `llm`.
+
+In the next chapter, we'll explore the [Model](03_model.md) object, which is responsible for actually interacting with the LLM and generating the response based on the `Prompt`.
+
+
+---
+
+Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)