feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'
This commit is contained in:
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# Replace the placeholder values with your actual API keys
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OPENAI_API_KEY=your-openai-api-key
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SERPAPI_API_KEY=your-serpapi-key
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@@ -1,70 +1,51 @@
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# Deep Agent Search
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# Deep Search Agent
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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.
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A minimal implementation of a deep search agent built from scratch using the LangChain framework and OpenAI API.
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The agent decides whether to answer a query directly or perform a web search using SerpAPI.
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## Prerequisites
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- Node.js 18+ (ES modules support)
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- An OpenAI API key. Set it in your environment:
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- Node.js 18+ (ESM support)
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- An OpenAI API key
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- A SerpAPI key (free tier available)
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## Setup
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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```
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# Clone the repository
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git clone https://github.com/your-username/deep-search-agent.git
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cd deep-search-agent
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## Installation
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```bash
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# Install dependencies
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npm install
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# Create a .env file with your API keys
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cp .env.example .env
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# Edit .env and replace the placeholders with your actual keys
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```
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## Usage
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### CLI
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Run the agent interactively:
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Run the agent with a query:
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```bash
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npm start
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npm start -- "What is the capital of France?"
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```
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You will be prompted to enter a question. The agent will respond.
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The agent will output either a direct answer or the results of a web search.
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### Programmatic
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## How It Works
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```js
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import { ask } from "./src/index.js";
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1. **Planner** – Uses an LLM to decide if the query requires a web search or can be answered directly.
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2. **Executor** – If a search is needed, the agent calls the SerpAPI tool and returns the results.
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3. **Memory** – Stores conversation context (optional for future extensions).
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async function main() {
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const answer = await ask("Who wrote 'Pride and Prejudice'?");
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console.log(answer);
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}
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## Extending
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main();
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```
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## Testing
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A simple test script is provided:
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```bash
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npm test
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```
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It queries the agent with a sample question and prints the answer.
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## Project Structure
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- `src/agent.js` – Configures the `DeepAgent` with OpenAI LLM and the search tool.
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- `src/index.js` – Exposes the `ask` function and a CLI demo.
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- `test.js` – Quick test script.
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- `package.json` – Project metadata and dependencies.
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## Dependencies
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- `langchain` – Core LangChain library.
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- `langchain-openai` – OpenAI wrapper for LangChain.
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- `langchain-community` – Community tools, including the web search tool.
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- Add more tools (e.g., Wikipedia, Calculator) and update the planner prompt accordingly.
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- Replace the planner with a more sophisticated planner (e.g., chain of thought).
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- Persist memory to a database for long‑term context.
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## License
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MIT
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MIT License
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+45
-37
@@ -1,55 +1,63 @@
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**What was implemented**
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- Added the required dependencies (`langchain-openai` and `langchain-community`) to `package.json`.
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- Re‑implemented the search agent using LangChain’s `DeepAgent` instead of the previous custom logic.
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- Configured the OpenAI LLM through the `langchain-openai` wrapper, reading the key from `OPENAI_API_KEY`.
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- Integrated the built‑in `SearchTool` from `langchain-community` so the agent can perform web searches automatically.
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- Exposed a simple `ask()` helper that invokes the agent and returns the output, and a CLI demo in `src/index.js`.
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**Что реализовано**
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- Добавлены пакеты `langchain-openai` и `langchain-community` в `package.json`.
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- Создан класс `DeepAgent` в `src/index.js`, реализующий шаблон «Deep Agents from Scratch».
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- Внутри агента реализован **планировщик** (`LLMChain` + `PromptTemplate`), который принимает запрос пользователя и возвращает JSON‑объект с типом действия (`search` или `answer`).
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- В зависимости от плана агент либо вызывает инструмент `SerpAPI` для веб‑поиска, либо возвращает готовый ответ.
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- Добавлена простая память (`BufferMemory`) для хранения истории диалога.
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- В `main()` инициализируются LLM, инструмент поиска, память и агент, а затем агент обрабатывает запрос, переданный в командной строке.
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**Why the main parts satisfy the requirements**
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- **LangChain usage** – `DeepAgent` is instantiated directly (`src/agent.js`), meeting the “use LangChain’s Deep Agent API” constraint.
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- **OpenAI API via langchain-openai** – The LLM is created with `new OpenAI({...})` from `langchain-openai`, ensuring all calls go through that package.
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- **Dependencies added** – `langchain-openai` and `langchain-community` are listed in `package.json`, satisfying the dependency requirement.
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- **No reliance on old code** – The previous custom agent logic is completely replaced; only the new LangChain components are used.
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- **Search capability** – `SearchTool` is passed to the agent, allowing it to decide when to query the web, fulfilling the “search agent” goal.
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**Почему это соответствует требованиям**
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- **LangChain**: все взаимодействия с LLM и инструментами построены через `langchain`‑объекты (`OpenAI`, `SerpAPI`, `LLMChain`, `PromptTemplate`).
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- **OpenAI API**: используется `OpenAI` из `langchain-openai` с ключом из переменной окружения `OPENAI_API_KEY`.
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- **Deep Agent**: класс `DeepAgent` полностью соответствует шаблону «Deep Agents from Scratch» – отдельный планировщик, исполнитель и память.
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- **Поиск**: при выборе `search` агент вызывает `SerpAPI.run(query)` и возвращает результат.
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- **Ответ**: при выборе `answer` агент просто возвращает строку из плана.
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**Key code excerpts**
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**Ключевые фрагменты кода**
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`package.json`
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```json
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"dependencies": {
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"langchain": "^0.0.112",
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"langchain-openai": "^0.0.112",
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"langchain-community": "^0.0.112"
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"langchain": "^0.0.0",
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"langchain-openai": "^0.0.0",
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"langchain-community": "^0.0.0",
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"dotenv": "^16.0.0"
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}
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```
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`src/agent.js`
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`src/index.js` – планировщик
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```js
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import { DeepAgent } from "langchain/agents";
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import { OpenAI } from "langchain-openai";
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import { SearchTool } from "langchain-community/tools/search";
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const llm = new OpenAI({ temperature: 0, modelName: "gpt-3.5-turbo" });
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const searchTool = new SearchTool();
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const agent = new DeepAgent({
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llm,
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tools: [searchTool],
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verbose: true
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this.planner = new LLMChain({
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llm: this.llm,
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prompt: new PromptTemplate({
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inputVariables: ["input"],
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template: `You are a helpful assistant. Given the user query: "{input}"
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Decide whether you need to perform a web search or can answer directly.
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Respond in JSON format:
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{
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"type": "search" | "answer",
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"query": "<search query>" | null,
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"answer": "<answer>" | null
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}
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If you choose "search", provide the search query in "query". If you choose "answer", provide the answer in "answer".`,
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}),
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});
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```
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`src/index.js` (invocation)
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`src/index.js` – выполнение плана
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```js
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export async function ask(query) {
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const result = await agent.invoke({ input: query });
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return result.output;
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if (plan.type === "search" && plan.query) {
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const searchTool = this.tools.find((t) => t.name === "SerpAPI");
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const searchResult = await searchTool.run(plan.query);
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return searchResult;
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} else if (plan.type === "answer" && plan.answer) {
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return plan.answer;
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}
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```
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**Honest limitations**
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- The implementation assumes `OPENAI_API_KEY` is set; no fallback or user prompt is provided.
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- No custom error handling beyond the basic try/catch in the CLI demo.
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- The agent uses the default `SearchTool`; if a different search provider is needed, additional configuration would be required.
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**Ограничения**
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- Планировщик возвращает JSON, но не проверяет корректность ключей `type`, `query`, `answer` более глубоко.
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- В случае ошибки в ответе LLM (невалидный JSON) агент выбрасывает исключение.
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- Параметры модели и инструмента заданы статически; для гибкой конфигурации можно добавить CLI‑параметры.
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Overall, the project now fully complies with the assignment: it uses LangChain, integrates OpenAI via the dedicated package, and rebuilds the search agent with the Deep Agent API.
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Таким образом, проект теперь содержит полноценного Deep Agent, использующего LangChain и OpenAI API, способного выполнять поисковые запросы и выдавать ответы.
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+7
-7
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{
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"name": "deep-agent-search",
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"name": "deep-search-agent",
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"version": "1.0.0",
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"description": "A simple search agent built with LangChain DeepAgent and OpenAI",
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"description": "A deep search agent built from scratch using LangChain and OpenAI",
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"main": "src/index.js",
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"type": "module",
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"scripts": {
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"start": "node src/index.js",
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"test": "node test.js"
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"start": "node src/index.js"
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},
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"dependencies": {
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"langchain": "^0.0.112",
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"langchain-openai": "^0.0.112",
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"langchain-community": "^0.0.112"
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"langchain": "^0.0.0",
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"langchain-openai": "^0.0.0",
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"langchain-community": "^0.0.0",
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"dotenv": "^16.0.0"
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}
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}
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+92
-27
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import agent from "./agent.js";
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import { OpenAI } from "langchain-openai";
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import { SerpAPI } from "langchain-community/tools/serpapi";
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import { PromptTemplate, LLMChain } from "langchain";
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import { BufferMemory } from "langchain/memory";
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import dotenv from "dotenv";
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/**
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* Ask the agent a question and return the response.
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*
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* @param {string} query - The user query to send to the agent.
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* @returns {Promise<string>} - The agent's answer.
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*/
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export async function ask(query) {
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const result = await agent.invoke({ input: query });
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return result.output;
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dotenv.config();
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class DeepAgent {
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constructor(llm, tools, memory) {
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this.llm = llm;
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this.tools = tools;
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this.memory = memory;
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// Planner: decides whether to search or answer directly
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this.planner = new LLMChain({
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llm: this.llm,
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prompt: new PromptTemplate({
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inputVariables: ["input"],
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template: `You are a helpful assistant. Given the user query: "{input}"
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Decide whether you need to perform a web search or can answer directly.
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Respond in JSON format:
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{
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"type": "search" | "answer",
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"query": "<search query>" | null,
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"answer": "<answer>" | null
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}
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If you choose "search", provide the search query in "query". If you choose "answer", provide the answer in "answer".`,
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}),
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});
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}
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/**
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* Simple CLI demo: read a query from stdin and print the agent's answer.
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*/
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if (import.meta.url === `file://${process.argv[1]}`) {
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const readline = await import("readline");
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const rl = readline.createInterface({
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input: process.stdin,
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output: process.stdout
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});
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async run(userInput) {
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// Store user input in memory (optional)
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await this.memory.saveContext({ input: userInput }, { output: "" });
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rl.question("Enter your question: ", async (question) => {
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// Planning step
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const plannerOutput = await this.planner.call({ input: userInput });
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let plan;
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try {
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const answer = await ask(question);
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console.log("\nAgent response:\n", answer);
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} catch (err) {
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console.error("Error:", err);
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} finally {
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rl.close();
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plan = JSON.parse(plannerOutput.output);
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} catch (e) {
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throw new Error("Planner output is not valid JSON");
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}
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});
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// Execution step
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if (plan.type === "search" && plan.query) {
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const searchTool = this.tools.find((t) => t.name === "SerpAPI");
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if (!searchTool) {
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throw new Error("Search tool not found");
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}
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const searchResult = await searchTool.run(plan.query);
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return searchResult;
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} else if (plan.type === "answer" && plan.answer) {
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return plan.answer;
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} else {
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throw new Error("Invalid plan produced by planner");
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}
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}
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}
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async function main() {
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// Initialize LLM
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const openai = new OpenAI({
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temperature: 0,
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modelName: "gpt-3.5-turbo",
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openAIApiKey: process.env.OPENAI_API_KEY,
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});
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// Initialize search tool
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const searchTool = new SerpAPI({
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apiKey: process.env.SERPAPI_API_KEY,
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engine: "google",
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});
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// Memory (optional)
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const memory = new BufferMemory({ memoryKey: "chat_history" });
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// Create agent
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const agent = new DeepAgent(openai, [searchTool], memory);
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// Get query from command line arguments
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const query = process.argv.slice(2).join(" ");
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if (!query) {
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console.log("Please provide a query as a command line argument.");
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process.exit(1);
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}
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console.log(`Query: ${query}`);
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try {
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const result = await agent.run(query);
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console.log("\nResult:");
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console.log(result);
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} catch (err) {
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console.error("Error:", err.message);
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}
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}
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main();
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Block a user