feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'

README.md

@@ -1,43 +1,68 @@
-# DeepAgent
+# Deep Search Agent – LangChain Implementation
 
-DeepAgent is a minimal example of a deep learning based search agent.  
-It demonstrates how to combine a neural network with a simple search algorithm
-(Monte‑Carlo Tree Search style) without relying on external search libraries.
+This repository contains a minimal implementation of a **search agent** built with LangChain, following the “Deep Agents from Scratch” template.  
+The agent can answer arbitrary questions by performing a web search and reasoning over the results.
 
-## Installation
+## Features
+
+- Uses **OpenAI GPT‑4o‑mini** as the language model.
+- Performs web searches via **SerpAPI** (Google/SerpAPI).
+- Maintains conversation context with a memory buffer.
+- Implements the **Zero‑Shot React** agent pattern.
+- Simple command‑line interface for interactive use.
+
+## Prerequisites
+
+- Python 3.10+
+- An OpenAI API key.
+- A SerpAPI key (free tier available).
+
+## Setup
 
 ```bash
-# Create a virtual environment (recommended)
-python -m venv .venv
-source .venv/bin/activate   # On Windows use `.venv\\Scripts\\activate`
+# Clone the repository
+git clone https://git.brojs.ru/kuzakhmetovartur/8.-samopisnyy-poiskovyy-agent-na-osnove-<repo>.git
+cd <repo>
 
-# Install the package
-pip install .
+# Create a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # On Windows: .venv\\Scripts\\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+Create a `.env` file in the project root with your credentials:
+
+```
+OPENAI_API_KEY=sk-...
+SERPAPI_KEY=your-serpapi-key
 ```
 
 ## Usage
 
-```python
-from src.search_agent import SearchAgent, PolicyValueNet
-
-# Create a policy‑value network
-net = PolicyValueNet(input_dim=1, action_space=2)
-
-# Create the agent
-agent = SearchAgent(policy_value_net=net, max_depth=3)
-
-# Run the agent on a simple state
-state = 0
-action = agent.act(state)
-print(f"Chosen action: {action}")
-```
-
-## Running Tests
+Run the agent interactively:
 
 ```bash
-pytest
+python -m src.agent
 ```
 
+You will be prompted to enter a question. The agent will search the web and return a concise answer.
+
+## Example
+
+```
+Enter your question: What is the capital of France?
+Processing...
+
+=== Answer ===
+The capital of France is Paris.
+```
+
+## Testing
+
+The agent can be tested programmatically by importing `create_search_agent` from `src.agent` and calling `agent.run("your question")`.
+
 ## License
 
-MIT License – see the [LICENSE](LICENSE) file for details.
+MIT License