feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'

src/main.py

@@ -1,23 +1,101 @@
+#!/usr/bin/env python3
 """
-Entry point for running the LangGraph example.
+Graph Reflection and Refinement Demo with LangChain LLM Integration.
+
+This script demonstrates how to integrate LangChain LLMs (OpenAI or Ollama)
+into a simple graph-related prompt. It loads configuration from environment
+variables, selects an appropriate LLM, and runs a prompt chain that
+explains the concept of graph reflection and refinement.
+
+Requirements:
+- langchain
+- langchain-openai
+- langchain-ollama
+- python-dotenv
+- openai
 """
 
-from src.graph import build_graph
-from src.utils import format_state
+import os
+from pathlib import Path
 
-def main():
-    # Build the graph
-    graph = build_graph()
+# Load environment variables from a .env file if present
+try:
+    from dotenv import load_dotenv
 
-    # Create a simple state with a question
-    state = {"question": "What is the capital of France?"}
+    load_dotenv()
+except ImportError:
+    # dotenv is optional; if not installed, environment variables must be set manually
+    pass
 
-    # Run the graph
-    result = graph.invoke(state)
+# Import LangChain components
+try:
+    from langchain import PromptTemplate, LLMChain
+    from langchain_openai import OpenAI
+    from langchain_ollama import Ollama
+except ImportError as exc:
+    raise ImportError(
+        "Required LangChain packages are missing. "
+        "Please install them via 'pip install -r requirements.txt'."
+    ) from exc
+
+
+def get_llm() -> "BaseLLM":
+    """
+    Instantiate an LLM based on available environment variables.
+
+    Returns:
+        An instance of a LangChain LLM (OpenAI or Ollama).
+
+    Raises:
+        RuntimeError: If neither OpenAI nor Ollama configuration is found.
+    """
+    # Prefer OpenAI if API key is available
+    openai_key = os.getenv("OPENAI_API_KEY")
+    if openai_key:
+        return OpenAI(
+            model_name=os.getenv("OPENAI_MODEL", "gpt-3.5-turbo"),
+            temperature=float(os.getenv("OPENAI_TEMPERATURE", "0.7")),
+            openai_api_key=openai_key,
+        )
+
+    # Fallback to Ollama if host is configured
+    ollama_host = os.getenv("OLLAMA_HOST")
+    if ollama_host:
+        return Ollama(
+            model=os.getenv("OLLAMA_MODEL", "llama2"),
+            temperature=float(os.getenv("OLLAMA_TEMPERATURE", "0.7")),
+            base_url=ollama_host,
+        )
+
+    raise RuntimeError(
+        "No LLM configuration found. Set either OPENAI_API_KEY or OLLAMA_HOST "
+        "in your environment."
+    )
+
+
+def main() -> None:
+    """
+    Main entry point: builds a prompt chain and prints the LLM response.
+    """
+    llm = get_llm()
+
+    # Simple prompt template explaining graph reflection and refinement
+    prompt = PromptTemplate(
+        input_variables=[],
+        template=(
+            "You are an expert in graph theory. "
+            "Explain the concepts of graph reflection and graph refinement "
+            "in simple, concise terms suitable for a beginner."
+        ),
+    )
+
+    chain = LLMChain(llm=llm, prompt=prompt)
+
+    # Run the chain and print the result
+    response = chain.run()
+    print("\n=== LLM Response ===\n")
+    print(response)
 
-    # Print the final state
-    print("Final state:")
-    print(format_state(result))
 
 if __name__ == "__main__":
     main()