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

README.md

@@ -1,87 +1,70 @@
-# Custom Search Agent – DeepAgents from Scratch
+# Deep Agent Search
 
-This repository contains a minimal implementation of a **deep search agent** that:
+This project demonstrates a simple search agent built with **LangChain**'s `DeepAgent` and the **OpenAI** language model. The agent can answer user queries and perform web searches when needed.
 
-* Generates deterministic mock search results.
-* Creates *virtual files* in memory during execution.
-* Exports those virtual files to a specified directory on disk.
+## Prerequisites
 
-The agent is fully self‑contained, does not rely on external APIs, and is fully testable.
+- Node.js 18+ (ES modules support)
+- An OpenAI API key. Set it in your environment:
 
-## Project Structure
-
-```
-.
-├── src
-│   ├── agent.py      # Core agent implementation
-│   └── run.py        # CLI entry point
-├── tests
-│   └── test_agent.py # Unit tests
-├── requirements.txt
-└── README.md
+```bash
+export OPENAI_API_KEY="your-api-key-here"
 ```
 
 ## Installation
 
 ```bash
-# Create a virtual environment (recommended)
-python -m venv venv
-source venv/bin/activate   # On Windows: venv\Scripts\activate
-
-# Install dependencies
-pip install -r requirements.txt
+npm install
 ```
 
 ## Usage
 
-### Command‑line
+### CLI
+
+Run the agent interactively:
 
 ```bash
-python -m src.run --query "python" --output "./search_results"
+npm start
 ```
 
-This will:
-
-1. Search for `"python"` (mock results).
-2. Create two virtual files (`result_1.txt`, `result_2.txt`) in memory.
-3. Export those files to `./search_results`.
+You will be prompted to enter a question. The agent will respond.
 
 ### Programmatic
 
-```python
-from src.agent import CustomSearchAgent
+```js
+import { ask } from "./src/index.js";
 
-agent = CustomSearchAgent(max_results=3)
-results = agent.search("deep learning")
-print(results)          # List of (title, snippet) tuples
-agent.export_virtual_files("./output")
+async function main() {
+  const answer = await ask("Who wrote 'Pride and Prejudice'?");
+  console.log(answer);
+}
+
+main();
 ```
 
 ## Testing
 
-Run the unit tests with:
+A simple test script is provided:
 
 ```bash
-python -m unittest discover -s tests
+npm test
 ```
 
-All tests should pass, confirming that:
+It queries the agent with a sample question and prints the answer.
 
-* The agent initializes correctly.
-* Search results are deterministic.
-* Virtual files are created during search.
-* Export writes the correct files to disk.
+## Project Structure
 
-## Extending the Agent
+- `src/agent.js` – Configures the `DeepAgent` with OpenAI LLM and the search tool.
+- `src/index.js` – Exposes the `ask` function and a CLI demo.
+- `test.js` – Quick test script.
+- `package.json` – Project metadata and dependencies.
 
-The `CustomSearchAgent` inherits from `DeepAgent`.  To add real search logic:
+## Dependencies
 
-1. Override `search` to perform actual queries (e.g., to a local index).
-2. Use `create_virtual_file` to store any generated data.
-3. Call `export_virtual_files` when you need to persist the data.
-
-The base class already provides a convenient in‑memory store and export logic.
+- `langchain` – Core LangChain library.
+- `langchain-openai` – OpenAI wrapper for LangChain.
+- `langchain-community` – Community tools, including the web search tool.
 
 ## License
 
-This project is released under the MIT License.
+MIT