Patched patchwork/steps/AgenticLLM/README.md (#1285)

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patchwork/steps/AgenticLLM/README.md

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+# Documentation for Agentic LLM Code
+
+## Overview
+
+The Agentic LLM module appears to be part of the `patchwork` package designed to handle Large Language Model (LLM) interactions using a predefined agentic strategy. This implementation facilitates the elicitation of responses from a language model, manages API keys, and defines the input and output data structures for the interactions.
+
+### Structure
+
+The module is organized as follows:
+
+- **`__init__.py`**: An empty initialization file to make the directory a package.
+- **`typed.py`**: Defines the input and output data types for the agentic LLM process.
+- **`AgenticLLM.py`**: Implements the main logic for the agentic LLM step, utilizing an agentic strategy and multi-turn interactions with a language model.
+
+## Detailed Description
+
+### File: `typed.py`
+
+#### Inputs
+
+- **`base_path`**: (Optional) The base path for accessing necessary tools.
+- **`prompt_value`**: A dictionary that contains dynamic values for templated prompts.
+- **`system_prompt`**: A string used as a system prompt template.
+- **`user_prompt`**: A string used as a user prompt template.
+- **`max_llm_calls`**: An integer that indicates the maximum number of LLM API calls allowed. It is treated as a configuration parameter.
+- **API Keys**: Includes `openai_api_key`, `anthropic_api_key`, `patched_api_key`, and `google_api_key`. These configurations allow different API keys to be specified for integrations with various LLM services.
+
+#### Outputs
+
+- **`conversation_history`**: A list of dictionaries tracking the conversation steps with the model.
+- **`tool_records`**: A list of dictionaries containing records of tool usage during interactions.
+- **`request_tokens`**: An integer indicating the number of tokens sent in requests.
+- **`response_tokens`**: An integer showing the number of tokens received in responses.
+
+### File: `AgenticLLM.py`
+
+#### Functionality
+
+- **Initialization**: The class initializes by configuring the LLM client and toolset for interaction. It uses the provided input configuration to create an agentic strategy.
+- **Execution**: The `run` method executes the agentic strategy with a set conversation limit. It returns structured output data including conversation history and tool usage statistics.
+
+#### Usage
+
+The `AgenticLLM` class is likely used in scenarios where automated and dynamic interaction with a language model is needed. The inputs are configured to adjust queries dynamically, and the comprehensive configuration support ensures flexible integration across multiple API providers.
+
+---
+
+This documentation is intended to offer a comprehensive overview and guide for developers working with or extending the functionality of the AgenticLLM module within the `patchwork` framework.