feat: solution for 'Агент с RAG-памятью'

src/main.py

@@ -0,0 +1,53 @@
+import os
+from dotenv import load_dotenv
+from langchain_ollama import Ollama
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain.vectorstores import Qdrant
+from qdrant_client import QdrantClient
+from src.agent import RAGAgent
+from src.chunk_document import chunk_document
+
+def main() -> None:
+    # Load environment variables if any
+    load_dotenv()
+
+    # Initialize LLM and embeddings
+    llm = Ollama(model="llama3")
+    embeddings = OllamaEmbeddings(model="llama3")
+
+    # Connect to Qdrant (assumes Qdrant is running locally on port 6333)
+    qdrant_client = QdrantClient(host="localhost", port=6333)
+    vector_store = Qdrant(
+        client=qdrant_client,
+        collection_name="rag_collection",
+        embeddings=embeddings,
+    )
+
+    # Create the RAG agent
+    rag_agent = RAGAgent(llm=llm, vector_store=vector_store, chunk_document_func=chunk_document)
+
+    # Example documents to add to the vector store
+    sample_docs = [
+        "LangChain is a framework for developing applications powered by language models.",
+        "Qdrant is a vector database that can store embeddings and perform similarity search.",
+        "Ollama provides a lightweight interface to run LLMs locally.",
+    ]
+    rag_agent.add_documents(sample_docs)
+
+    # Build the agent executor
+    agent_executor = rag_agent.create_agent()
+
+    print("RAG Agent is ready. Type your question (or 'exit' to quit).")
+    while True:
+        user_input = input(">>> ")
+        if user_input.lower() in {"exit", "quit"}:
+            print("Goodbye!")
+            break
+        try:
+            response = agent_executor.invoke({"input": user_input})
+            print(response["output"])
+        except Exception as e:
+            print(f"Error: {e}")
+
+if __name__ == "__main__":
+    main()