Patched patchwork/steps/AgenticLLMV2/README.md (#1329)

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patchwork/steps/AgenticLLMV2/README.md

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+# AgenticLLMV2 Module Documentation
+
+## Overview
+The `AgenticLLMV2` module provides a step for executing a multi-turn conversation strategy using the `AgenticStrategyV2` framework. This is part of a system that involves complex conversational models and tool integrations. The module is composed of three scripts: an initializer, the main class implementation, and type definitions for controlled inputs and outputs.
+
+## Components
+
+### 1. AgenticLLMV2/__init__.py
+This script is an initializer for the `AgenticLLMV2` module. It currently contains no code but serves to correctly package the Python files in this directory.
+
+### 2. AgenticLLMV2/AgenticLLMV2.py
+
+#### Description
+This file implements the `AgenticLLMV2` class, which extends the `Step` class and is responsible for managing the setup and execution of an `AgenticStrategyV2` conversation process.
+
+#### Inputs
+
+- `base_path` (str): The base path for tool configurations. Defaults to the current working directory if not specified.
+- `prompt_value` (Dict[str, Any]): Template data used in the prompts.
+- `system_prompt` (str): The system prompt used if not overridden.
+- `user_prompt` (str): Prompt provided for user-specific queries.
+- `max_agent_calls` (int): Maximum number of turns for agent calls. Defaults to 1.
+- `anthropic_api_key` (str): API key for Anthropic.
+- `agent_system_prompt` (str): Custom system prompt for agents.
+- `example_json` (str): JSON format data for prompt examples.
+
+#### Outputs
+
+- `request_tokens` (int): The number of tokens in the request.
+- `response_tokens` (int): The number of tokens in the response.
+
+#### Usage
+To use the `AgenticLLMV2` class, instantiate it with `AgenticLLMV2Inputs`, then call the `run()` method to perform the conversation operations. It integrates tools found at the specified base path and supports a configurable number of conversation turns.
+
+### 3. AgenticLLMV2/typed.py
+
+#### Description
+This script defines the data types used for input and output of the `AgenticLLMV2` class using Python's `TypedDict` for structured data handling.
+
+#### Inputs
+- Inputs are defined in `AgenticLLMV2Inputs` which includes optional attributes such as `base_path`, `prompt_value`, and more, facilitating flexible configuration of the agent strategy.
+
+#### Outputs
+- Outputs are defined in `AgenticLLMV2Outputs`, ensuring that only request and response token counts are recorded as outputs.
+
+## How to Use
+1. Define input parameters using `AgenticLLMV2Inputs`.
+2. Initialize the `AgenticLLMV2` class with these inputs.
+3. Execute the `run()` method to process a conversation based on the configured strategy.
+4. Analyze the token usage from the outputs to understand usage and billing metrics if needed.
+
+This module is useful for developers working on advanced AI conversational systems, offering customization and detailed control over conversation flows and tools used within those flows.