feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,14 +1,16 @@
-# Agent with RAG Memory
+# RAG Agent with Ollama Embeddings
 
-This project implements a retrieval‑augmented generation (RAG) agent that uses **Qdrant** as the vector store and **Ollama** for local LLM inference.  
-The agent follows the latest LangChain API and is fully configurable via environment variables.
+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:
 
-## Features
+- `search_knowledge_base`: Search the knowledge base for relevant documents.
+- `add_to_knowledge_base`: Add new content to the knowledge base.
 
-- **Qdrant** vector store (via `langchain-qdrant`)
-- **Ollama** local LLM integration (via `langchain-ollama`)
-- Retrieval‑augmented generation with conversation memory
-- Simple CLI interface for quick testing
+## Prerequisites
+
+- Python 3.10+
+- An Ollama server running locally (e.g., `ollama serve`).
+- The Ollama model you want to use (default is `mistral`).
 
 ## Installation
 
@@ -17,82 +19,72 @@ The agent follows the latest LangChain API and is fully configurable via environ
 git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
 cd agent-s-rag-pamyatyu
 
-# Create a virtual environment (optional but recommended)
-python -m venv .venv
-source .venv/bin/activate  # On Windows: .venv\\Scripts\\activate
+# Create a virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
 
 # Install dependencies
 pip install -r requirements.txt
 ```
 
+`requirements.txt` contains:
+
+```text
+langchain
+langchain-community
+openai
+```
+
 ## Configuration
 
-Create a `.env` file in the project root (or set environment variables directly):
-
-```dotenv
-# Qdrant
-QDRANT_HOST=localhost
-QDRANT_PORT=6333
-QDRANT_API_KEY=  # leave empty if no key
-
-# Ollama
-OLLAMA_HOST=localhost
-OLLAMA_PORT=11434
-OLLAMA_MODEL=llama3
-
-# Optional: collection name
-QDRANT_COLLECTION=documents
-```
-
-> **Note**: The Qdrant instance must be running and accessible at the specified host/port.  
-> The Ollama server must be running locally and expose the chosen model.
-
-## Usage
-
-### Adding Documents
-
-```python
-from src.agent import Agent
-from langchain_core.documents import Document
-
-agent = Agent()
-
-docs = [
-    Document(page_content="Python is a programming language.", metadata={"source": "python.txt"}),
-    Document(page_content="LangChain is a framework for LLM applications.", metadata={"source": "langchain.txt"}),
-]
-
-agent.add_documents(docs)
-```
-
-### Querying the Agent
+Set the Ollama model via environment variable (optional):
 
 ```bash
-python -m src.agent "What is LangChain?"
+export OLLAMA_MODEL=mistral   # or any other model available in Ollama
 ```
 
-or from Python:
+If you run the Ollama server on a non‑default host/port, set:
 
-```python
-response = agent.run("What is LangChain?")
-print(response["answer"])
+```bash
+export OLLAMA_HOST=http://localhost:11434
 ```
 
-The response will include the answer and the source documents used.
+## Running the Agent
 
-## Project Structure
+```bash
+python src/agent.py
+```
+
+You will see a prompt:
 
 ```
-agent-s-rag-pamyatyu/
-├── src/
-│   ├── agent.py
-│   ├── config.py
-│   └── vector_store.py
-├── requirements.txt
-├── pyproject.toml
-└── README.md
+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.
+
 ## License
 
 MIT License