feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,113 +1,64 @@
-# Agent with RAG Memory
+# Agent with RAG Memory using Qdrant and Ollama
 
-This repository contains a simple **Retrieval‑Augmented Generation (RAG)** agent
-implemented with LangChain, FAISS for vector storage, and OpenAI embeddings
-and LLM.  It also provides an `auto_check_graph` function that verifies the
-generated answer against a ground‑truth mapping and returns a `verdict_row`.
+This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent built with LangChain, Qdrant, and Ollama. The agent uses Ollama embeddings for vector representation and Qdrant as the vector store.
 
-> **Important**  
-> The auto‑check graph must return a `verdict_row`.  The implementation
-> below guarantees that by always including the key in the returned
-> dictionary.
+## Prerequisites
 
-## Features
-
-- **RAG Agent** – Load documents, embed them, store in FAISS, and answer queries.
-- **Auto‑Check Graph** – Run a query, generate an answer, compare it to a
-  ground‑truth answer, and return a verdict (`PASS`, `FAIL`, or `UNKNOWN`).
-- **Unit Tests** – Verify that the agent and auto‑check graph work as
-  expected.
+- Python 3.10+
+- Qdrant server running locally or accessible remotely
+- Ollama server running locally or accessible remotely
 
 ## Installation
 
 ```bash
-# Create a virtual environment (recommended)
-python -m venv .venv
-source .venv/bin/activate   # On Windows: .venv\Scripts\activate
+# Clone the repository
+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 use `venv\Scripts\activate`
 
 # Install dependencies
 pip install -r requirements.txt
 ```
 
-`requirements.txt` contains:
+## Configuration
 
-```
-langchain
-openai
-faiss-cpu
-pytest
-```
-
-> **OpenAI API Key**  
-> If you want to use real embeddings and LLM, set the environment variable
-> `OPENAI_API_KEY`:
-
-```bash
-export OPENAI_API_KEY="sk-..."
-```
-
-If the key is not set, the agent falls back to `FakeEmbeddings` and
-`FakeLLM`, which are suitable for local testing and unit tests.
-
-## Usage
+Edit `config.py` to match your environment:
 
 ```python
-from src.index import RAGAgent, auto_check_graph
+# Qdrant settings
+QDRANT_HOST = "localhost"
+QDRANT_PORT = 6333
+QDRANT_API_KEY = None
+QDRANT_COLLECTION = "rag_collection"
 
-# Create agent
-agent = RAGAgent()
-
-# Add documents (e.g., from a directory)
-agent.add_documents([
-    "The capital of France is Paris.",
-    "William Shakespeare wrote Hamlet."
-])
-
-# Define ground truth mapping
-ground_truth = {
-    "What is the capital of France?": "Paris",
-    "Who wrote Hamlet?": "William Shakespeare",
-}
-
-# Run auto‑check graph
-result = auto_check_graph(
-    "What is the capital of France?",
-    agent,
-    ground_truth
-)
-
-print(result)
-# Output:
-# {
-#   "verdict_row": "PASS",
-#   "answer": "Paris",
-#   "expected": "Paris"
-# }
+# Ollama settings
+OLLAMA_MODEL = "llama3"
 ```
 
-## Running Tests
+## Running the Agent
 
 ```bash
-pytest
+python src/main.py
 ```
 
-The tests cover:
+The script will:
 
-- Adding documents and querying.
-- Auto‑check graph returning `PASS`, `FAIL`, and `UNKNOWN` verdicts.
-- Handling of empty queries and missing ground‑truth.
+1. Connect to Qdrant.
+2. Create an Ollama embeddings instance.
+3. Add sample documents to the collection if it is empty.
+4. Build a RetrievalQA chain using the Ollama LLM.
+5. Execute a sample query and print the answer.
 
-## Project Structure
+## Extending
 
-```
-src/
-├── index.py          # Main implementation
-tests/
-├── test_agent.py     # Unit tests
-README.md
-requirements.txt
-```
+- Replace the sample documents with your own corpus.
+- Adjust the `chain_type` in `src/agent.py` if you need a different retrieval strategy.
+- Use environment variables or a `.env` file to store sensitive information like `QDRANT_API_KEY`.
 
 ## License
 
-MIT License
+MIT License
+---