feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,166 +1,74 @@
-# RAG Agent with Retrieval-Augmented Generation
+# Agent with RAG Memory
 
-**Version:** 20  
-**Author:** Artur Kuzakhmetov  
-**Course:** Deep Agents Virtual File System  
-**Deadline:** 31.08.2026
+This project implements a simple command‑line agent that uses **Ollama embeddings** for a Retrieval‑Augmented Generation (RAG) style knowledge base.  
+The agent supports two main tools:
 
----
-
-## Overview
-
-This repository implements an educational agent that uses Retrieval-Augmented Generation (RAG) to answer user queries.  
-The agent:
-
-1. **Embeds** a collection of text documents into a FAISS vector store using OpenAI embeddings.  
-2. **Retrieves** the most relevant passages for a user query.  
-3. **Generates** a response with OpenAI GPT‑4, conditioned on the retrieved context.
-
-The agent is exposed via a FastAPI web service with a single `/ask` endpoint.
-
----
-
-## Project Structure
-
-```
-.
-├── data/                # Place your .txt documents here
-├── src/
-│   └── index.py         # FastAPI app and RAG logic
-├── .env                 # (Optional) Environment variables
-├── README.md
-└── requirements.txt
-```
-
-> **Note:** The `data/` directory is **not** committed to version control.  
-> Add your own documents there before running the agent.
-
----
+- **`search_knowledge_base`** – find the most relevant documents for a query.  
+- **`add_to_knowledge_base`** – add new content to the knowledge base.
 
 ## Setup
 
-### 1. Clone the Repository
-
 ```bash
+# Clone the repository
 git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
 cd agent-s-rag-pamyatyu
+
+# Install dependencies
+npm install
 ```
 
-### 2. Create a Virtual Environment
+> **Note**: The project uses the `ollama-embeddings` package.  
+> Make sure you have an Ollama server running locally (default `http://localhost:11434`).  
+> You can change the host or model via environment variables:
 
 ```bash
-python -m venv .venv
-source .venv/bin/activate   # On Windows: .venv\Scripts\activate
+# Example .env file
+OLLAMA_HOST=http://localhost:11434
+OLLAMA_MODEL=all-minilm
 ```
 
-### 3. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-> `requirements.txt` contains:
-> ```text
-> fastapi
-> uvicorn
-> langchain
-> openai
-> faiss-cpu
-> python-dotenv
-> ```
-
-### 4. Set Up OpenAI API Key
-
-Create a file named `.env` in the project root:
-
-```dotenv
-OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
-```
-
-> **Security:** Do **not** commit the `.env` file to version control.  
-> Add it to `.gitignore` if you have one.
-
-### 5. Add Documents
-
-Place any number of `.txt` files in the `data/` directory.  
-Each file will be treated as a separate document.
-
----
-
 ## Running the Agent
 
 ```bash
-uvicorn src.index:app --reload
+npm start
 ```
 
-The API will be available at `http://127.0.0.1:8000`.
+You will see a prompt:
 
-### Example Request
-
-```bash
-curl -X POST "http://127.0.0.1:8000/ask" \
-     -H "Content-Type: application/json" \
-     -d '{"question":"What is the capital of France?"}'
+```
+Agent> 
 ```
 
-**Response**
+### Commands
 
-```json
-{
-  "answer": "The capital of France is Paris.",
-  "sources": ["data/geo_facts.txt"]
-}
+- `/search <query>` – Search the knowledge base for the most relevant documents.
+- `/add <content>` – Add new content to the knowledge base.
+- `/exit` – Exit the program.
+
+Example:
+
+```
+Agent> /add The quick brown fox jumps over the lazy dog.
+Content added with id 3f1c2e4b-...
+
+Agent> /search fox
+Searching for "fox"...
+Top results:
+1. [3f1c2e4b-...] (0.9123)
+   The quick brown fox jumps over the lazy dog.
 ```
 
----
+## Project Structure
 
-## Architecture Details
+- `src/embeddings.js` – Wrapper around `ollama-embeddings`.  
+- `src/tools/searchKnowledgeBase.js` – Implements the search tool.  
+- `src/tools/addToKnowledgeBase.js` – Implements the add tool.  
+- `src/index.js` – CLI entry point and agent logic.  
+- `package.json` – Dependencies and scripts.
 
-| Component | Purpose | Library |
-|-----------|---------|---------|
-| **Document Loader** | Reads `.txt` files from `data/` | `langchain.document_loaders.DirectoryLoader` |
-| **Embeddings** | Converts text to vectors | `langchain.embeddings.openai.OpenAIEmbeddings` |
-| **Vector Store** | Stores and queries vectors | `langchain.vectorstores.FAISS` |
-| **Retriever** | Finds top‑k relevant documents | FAISS retriever |
-| **LLM** | Generates answer | `langchain.llms.OpenAI` (GPT‑4) |
-| **Chain** | Combines retrieval and generation | `langchain.chains.RetrievalQA` |
-| **API** | Exposes the agent | `FastAPI` |
-
----
-
-## Testing
-
-The repository includes a simple integration test in `tests/test_agent.py` (not shown here).  
-Run tests with:
-
-```bash
-pytest
-```
-
----
-
-## Compliance with Course Guidelines
-
-- **Educational Agent Solution**: The agent follows the structure outlined in the Deep Agents lecture, using a clear separation between data ingestion, retrieval, and generation.
-- **RAG Memory**: Implemented via FAISS vector store and OpenAI embeddings.
-- **Python 3.11+**: All code is compatible with Python 3.11 and above.
-- **Individual Assignment**: All work is authored by a single developer (Artur Kuzakhmetov).
-- **Versioning**: The repository is tagged as `v20` and the README reflects version 20.
-
----
-
-## License
-
-This project is released under the MIT License.  
-Feel free to adapt and extend it for your own educational projects.
-
----
-
-## Contact
-
-For questions or feedback, contact:
-
-- **Email:** artur.kuzakhmetov@example.com
-- **GitLab:** https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu
+## Extending
+
+The current implementation uses an in‑memory vector store.  
+To persist data or use a more sophisticated vector database, replace the `knowledgeBase` array in `searchKnowledgeBase.js` with your preferred storage solution.
 
 ---

SOLUTION.md

@@ -1,72 +1,80 @@
-**What was implemented**  
-- A FastAPI service exposing a single `/ask` endpoint that accepts a user question and returns an answer together with the sources used.  
-- RAG (Retrieval‑Augmented Generation) logic built with LangChain: documents from `data/` are embedded with OpenAI embeddings, stored in a FAISS vector store, and queried by a `RetrievalQA` chain that feeds the retrieved passages to GPT‑4.  
-- Automatic startup loading of documents, vector store creation, and agent construction so the API is ready to serve immediately after launch.  
+**SOLUTION.md**
 
-**Why the main parts satisfy the assignment**  
-- **RAG memory**: `create_vectorstore` builds a FAISS index from the loaded documents, and `build_agent` wires this index into a `RetrievalQA` chain that retrieves relevant passages before generation.  
-- **Course guidelines**: The solution follows the Deep Agents Virtual File System pattern – a single `src/index.py` module, clear separation of concerns (loading, vector store, agent, API), and use of environment variables for secrets.  
-- **Python implementation**: All code is pure Python 3.11+, uses only standard libraries and well‑documented third‑party packages (`fastapi`, `langchain`, `openai`, `dotenv`).  
-- **Individual assignment**: No shared state or external services beyond the OpenAI API; the repository contains only the student’s code.  
+---
 
-**Key code excerpts**  
+### Что реализовано  
+1. **Инструменты RAG**  
+   * `search_knowledge_base(query, topK)` – ищет наиболее релевантные документы в памяти.  
+   * `add_to_knowledge_base(content)` – добавляет новый контент в память.  
 
-*Loading documents* (`src/index.py`)  
-```python
-def load_documents(path: Path) -> List:
-    if not path.exists() or not path.is_dir():
-        print(f"Warning: Data directory '{path}' not found. No documents loaded.")
-        return []
+2. **Стек эмбеддингов**  
+   * Заменён `OpenAIEmbeddings` на `OllamaEmbeddings`.  
+   * В `package.json` добавлена зависимость `ollama-embeddings`.  
 
-    loader = DirectoryLoader(str(path), glob="**/*.txt")
-    documents = loader.load()
-    print(f"Loaded {len(documents)} documents from '{path}'.")
-    return documents
+3. **Интеграция**  
+   * Инструменты подключены в `src/index.js` и доступны через CLI‑команды `/search` и `/add`.  
+   * Все операции с эмбеддингами используют экземпляр `OllamaEmbeddings` из `src/embeddings.js`.  
+
+---
+
+### Почему это соответствует требованиям  
+* **Наличие инструментов** – файлы `searchKnowledgeBase.js` и `addToKnowledgeBase.js` экспортируют требуемые функции, которые можно вызывать из любого модуля.  
+* **Использование OllamaEmbeddings** – в `embeddings.js` создаётся единственный экземпляр `OllamaEmbeddings`, а в инструментах вызывается `embeddings.embedQuery`.  
+* **Обновлённые импорты** – все модули импортируют `embeddings` из `src/embeddings.js`, а не из OpenAI.  
+* **Пакетная зависимость** – `ollama-embeddings` присутствует в `package.json`, что позволяет npm установить нужный пакет.  
+
+---
+
+### Ключевые фрагменты кода  
+
+**src/embeddings.js** – инициализация OllamaEmbeddings  
+```js
+import { OllamaEmbeddings } from 'ollama-embeddings';
+const modelName = process.env.OLLAMA_MODEL || 'all-minilm';
+export const embeddings = new OllamaEmbeddings({
+  model: modelName,
+  host: process.env.OLLAMA_HOST || 'http://localhost:11434'
+});
 ```
 
-*Creating the vector store* (`src/index.py`)  
-```python
-def create_vectorstore(documents: List) -> FAISS:
-    embeddings = OpenAIEmbeddings()
-    vectorstore = FAISS.from_documents(documents, embeddings)
-    print("FAISS vector store created.")
-    return vectorstore
+**src/tools/searchKnowledgeBase.js** – поиск по памяти  
+```js
+export async function search_knowledge_base(query, topK = 3) {
+  const queryEmbedding = await embeddings.embedQuery(query);
+  const scored = knowledgeBase.map(entry => ({
+    id: entry.id,
+    content: entry.content,
+    score: cosineSimilarity(queryEmbedding, entry.embedding)
+  }));
+  scored.sort((a, b) => b.score - a.score);
+  return scored.slice(0, topK);
+}
 ```
 
-*Building the RetrievalQA agent* (`src/index.py`)  
-```python
-def build_agent(vectorstore: FAISS) -> RetrievalQA:
-    llm = OpenAI(model_name="gpt-4", temperature=0, openai_api_key=OPENAI_API_KEY)
-    retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
-        return_source_documents=True,
-    )
-    print("RetrievalQA agent constructed.")
-    return qa_chain
+**src/tools/addToKnowledgeBase.js** – добавление контента  
+```js
+export async function add_to_knowledge_base(content) {
+  const embedding = await embeddings.embedQuery(content);
+  const id = uuidv4();
+  knowledgeBase.push({ id, content, embedding });
+  return { id };
+}
 ```
 
-*FastAPI endpoint* (`src/index.py`)  
-```python
-@app.post("/ask", response_model=AnswerResponse)
-def ask_question(request: QuestionRequest):
-    if not agent:
-        raise HTTPException(status_code=500, detail="Agent not initialized.")
-    try:
-        result = agent({"question": request.question})
-        answer = result.get("answer", "")
-        sources = [doc.metadata.get("source", "") for doc in result.get("source_documents", [])]
-        return AnswerResponse(answer=answer, sources=sources)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+**src/index.js** – CLI‑интеграция инструментов  
+```js
+import { search_knowledge_base } from './tools/searchKnowledgeBase.js';
+import { add_to_knowledge_base } from './tools/addToKnowledgeBase.js';
+...
+if (trimmed.startsWith('/search ')) { … }
+else if (trimmed.startsWith('/add ')) { … }
 ```
 
-**Honest limitations**  
-- The vector store is rebuilt on every server restart; no persistence across restarts.  
-- No caching of embeddings or query results, which may increase latency for repeated queries.  
-- Error handling is minimal – any exception during a request returns a generic 500 error.  
-- The solution assumes all documents are plain `.txt`; other formats would need additional loaders.  
+---
 
-These points are acceptable for the current assignment scope and can be refined in future iterations.
+### Ограничения  
+* Память реализована как простая массив‑объект, поэтому данные не сохраняются между перезапусками.  
+* Нет обработки ошибок при работе с Ollama (например, недоступность сервера).  
+* Для больших наборов данных поиск выполняется линейно; в продакшене стоит использовать индексирование.  
+
+---

package.json

@@ -1,15 +1,15 @@
 {
   "name": "agent-s-rag-pamyatyu",
   "version": "1.0.0",
-  "description": "RAG agent using ChromaDB as the vector store",
+  "description": "Agent with RAG memory using Ollama embeddings",
   "main": "src/index.js",
-  "type": "commonjs",
+  "type": "module",
   "scripts": {
     "start": "node src/index.js"
   },
   "dependencies": {
-    "chromadb": "^0.3.0",
-    "openai": "^3.3.0",
-    "dotenv": "^16.0.0"
+    "ollama-embeddings": "^1.0.0",
+    "dotenv": "^16.4.5",
+    "node-fetch": "^3.3.2"
   }
 }

src/embeddings.js

@@ -0,0 +1,21 @@
+import { OllamaEmbeddings } from 'ollama-embeddings';
+
+/**
+ * Singleton instance of OllamaEmbeddings.
+ * The model name can be overridden via the OLLAMA_MODEL environment variable.
+ */
+const modelName = process.env.OLLAMA_MODEL || 'all-minilm';
+export const embeddings = new OllamaEmbeddings({
+  model: modelName,
+  // Optional: specify the Ollama host if not default
+  host: process.env.OLLAMA_HOST || 'http://localhost:11434'
+});
+
+/**
+ * Utility to embed a single string.
+ * @param {string} text
+ * @returns {Promise<number[]>} embedding vector
+ */
+export async function embedText(text) {
+  return await embeddings.embedQuery(text);
+}

src/index.js

@@ -1,3 +1,67 @@
-const Agent = require('./agent');
+import dotenv from 'dotenv';
+import readline from 'readline';
+import { search_knowledge_base } from './tools/searchKnowledgeBase.js';
+import { add_to_knowledge_base } from './tools/addToKnowledgeBase.js';
 
-module.exports = { Agent };
+dotenv.config();
+
+/**
+ * Simple command-line agent that supports two commands:
+ * 1. /search <query>  - searches the knowledge base
+ * 2. /add <content>   - adds content to the knowledge base
+ * Any other input is treated as a normal message and the agent echoes it back.
+ */
+const rl = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+  prompt: 'Agent> '
+});
+
+console.log('Agent with RAG memory using Ollama embeddings.');
+console.log('Commands:');
+console.log('  /search <query>   - Search knowledge base');
+console.log('  /add <content>    - Add content to knowledge base');
+console.log('  /exit             - Exit');
+rl.prompt();
+
+rl.on('line', async (line) => {
+  const trimmed = line.trim();
+  if (trimmed === '/exit') {
+    rl.close();
+    return;
+  }
+
+  if (trimmed.startsWith('/search ')) {
+    const query = trimmed.slice(8).trim();
+    if (!query) {
+      console.log('Please provide a query.');
+    } else {
+      console.log(`Searching for "${query}"...`);
+      const results = await search_knowledge_base(query);
+      if (results.length === 0) {
+        console.log('No relevant documents found.');
+      } else {
+        console.log('Top results:');
+        results.forEach((res, idx) => {
+          console.log(`${idx + 1}. [${res.id}] (${res.score.toFixed(4)})`);
+          console.log(`   ${res.content}`);
+        });
+      }
+    }
+  } else if (trimmed.startsWith('/add ')) {
+    const content = trimmed.slice(5).trim();
+    if (!content) {
+      console.log('Please provide content to add.');
+    } else {
+      const { id } = await add_to_knowledge_base(content);
+      console.log(`Content added with id ${id}.`);
+    }
+  } else {
+    // Echo back the message (placeholder for more complex agent logic)
+    console.log(`You said: ${trimmed}`);
+  }
+  rl.prompt();
+}).on('close', () => {
+  console.log('Goodbye!');
+  process.exit(0);
+});

src/tools/addToKnowledgeBase.js

@@ -0,0 +1,15 @@
+import { embeddings } from '../embeddings.js';
+import { knowledgeBase } from './searchKnowledgeBase.js';
+import { v4 as uuidv4 } from 'uuid';
+
+/**
+ * Add new content to the knowledge base.
+ * @param {string} content
+ * @returns {Promise<{id: string}>}
+ */
+export async function add_to_knowledge_base(content) {
+  const embedding = await embeddings.embedQuery(content);
+  const id = uuidv4();
+  knowledgeBase.push({ id, content, embedding });
+  return { id };
+}

src/tools/searchKnowledgeBase.js

@@ -0,0 +1,42 @@
+import { embeddings } from '../embeddings.js';
+
+/**
+ * In-memory knowledge base.
+ * Each entry: { id, content, embedding }
+ */
+const knowledgeBase = [];
+
+/**
+ * Compute cosine similarity between two vectors.
+ * @param {number[]} a
+ * @param {number[]} b
+ * @returns {number}
+ */
+function cosineSimilarity(a, b) {
+  const dot = a.reduce((sum, ai, i) => sum + ai * b[i], 0);
+  const normA = Math.sqrt(a.reduce((sum, ai) => sum + ai * ai, 0));
+  const normB = Math.sqrt(b.reduce((sum, bi) => sum + bi * bi, 0));
+  return dot / (normA * normB);
+}
+
+/**
+ * Search the knowledge base for the most relevant documents.
+ * @param {string} query
+ * @param {number} topK
+ * @returns {Promise<Array<{id: string, content: string, score: number}>>}
+ */
+export async function search_knowledge_base(query, topK = 3) {
+  const queryEmbedding = await embeddings.embedQuery(query);
+  const scored = knowledgeBase.map(entry => ({
+    id: entry.id,
+    content: entry.content,
+    score: cosineSimilarity(queryEmbedding, entry.embedding)
+  }));
+  scored.sort((a, b) => b.score - a.score);
+  return scored.slice(0, topK);
+}
+
+/**
+ * Expose the knowledge base for other modules (e.g., add tool).
+ */
+export { knowledgeBase };