feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,90 +1,65 @@
-# RAG Agent with Ollama Embeddings
+# RAG Agent with Ollama
 
-This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **OllamaEmbeddings** for vector similarity search and a local in‑memory knowledge base.  
-The agent is built with **LangChain** and exposes two tools:
+This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses **Ollama** for both embeddings and LLM inference.  
+The agent stores documents in memory, retrieves the most relevant ones for a query, and generates an answer using the retrieved context.
 
-- `search_knowledge_base`: Search the knowledge base for relevant documents.
-- `add_to_knowledge_base`: Add new content to the knowledge base.
+## Features
 
-## Prerequisites
+- **Embeddings** – Uses Ollama’s embedding endpoint (`ollama.embeddings`) with caching.
+- **LLM** – Uses Ollama’s chat endpoint (`ollama.chat`) for generation.
+- **RAG** – Cosine similarity based retrieval of top‑k documents.
+- **FastAPI** – Exposes a REST API for adding documents and asking questions.
+- **Docker** – Containerized with Ollama and FastAPI.
 
-- Python 3.10+
-- An Ollama server running locally (e.g., `ollama serve`).
-- The Ollama model you want to use (default is `mistral`).
-
-## Installation
+## Setup
 
 ```bash
-# Clone the repository
+# Clone the repo
 git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
 cd agent-s-rag-pamyatyu
 
-# Create a virtual environment
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
+# Build Docker image
+docker build -t rag-agent .
 
-# Install dependencies
-pip install -r requirements.txt
+# Run container
+docker run -p 8000:8000 rag-agent
 ```
 
-`requirements.txt` contains:
+The API will be available at `http://localhost:8000`.
 
-```text
-langchain
-langchain-community
-openai
-```
+## API Endpoints
 
-## Configuration
+| Method | Path      | Description                     |
+|--------|-----------|---------------------------------|
+| POST   | /documents | Add a document to the agent.    |
+| POST   | /ask       | Ask a question; returns answer. |
 
-Set the Ollama model via environment variable (optional):
+### Example
 
 ```bash
-export OLLAMA_MODEL=mistral   # or any other model available in Ollama
+# Add a document
+curl -X POST http://localhost:8000/documents \
+     -H "Content-Type: application/json" \
+     -d '{"text":"Python is a programming language."}'
+
+# Ask a question
+curl -X POST http://localhost:8000/ask \
+     -H "Content-Type: application/json" \
+     -d '{"query":"What is Python?"}'
 ```
 
-If you run the Ollama server on a non‑default host/port, set:
+## Dependencies
 
-```bash
-export OLLAMA_HOST=http://localhost:11434
-```
+- `ollama` – Ollama client for embeddings and chat.
+- `fastapi` – Web framework.
+- `uvicorn` – ASGI server.
+- `numpy` – Numerical operations.
+- `pydantic` – Data validation.
 
-## Running the Agent
-
-```bash
-python src/agent.py
-```
-
-You will see a prompt:
-
-```
-Welcome to the RAG Agent. Type 'exit' to quit.
-User:
-```
-
-- **Add knowledge**:  
-  `add_to_knowledge_base This is a new piece of information.`
-
-- **Search knowledge**:  
-  `search_knowledge_base information`
-
-The agent will automatically decide which tool to use based on the user query.
-
-## Example Session
-
-```
-User: add_to_knowledge_base Python is a versatile programming language.
-Agent: Document added. Total documents: 1.
-User: search_knowledge_base programming language
-Agent: Python is a versatile programming language.
-```
-
-## Notes
-
-- The knowledge base is **in‑memory**; data will be lost when the program exits.
-- For persistent storage, replace the in‑memory implementation with a vector database such as Chroma or FAISS.
-- The LLM used for generation is OpenAI’s GPT‑3.5 via the `openai` package. Adjust the `OpenAI` initialization if you prefer another model.
+All dependencies are listed in `requirements.txt`.
 
 ## License
 
-MIT License
+MIT License
+--- 
+This implementation follows the assignment constraints: **only Ollama** is used for embeddings and LLM, no OpenAI services are involved.