feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'
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+36
-11
@@ -1,14 +1,39 @@
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from langgraph.graph import StateGraph
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from src.nodes import generate_response
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from typing import Dict, Any
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"""
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Graph definition for the LangGraph workflow.
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def build_graph() -> StateGraph:
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The graph consists of two nodes:
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1. ReflectNode – generates a reflection of the user input.
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2. RewriteNode – rewrites the reflection into a more formal style.
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The graph starts at the reflect node, then proceeds to the rewrite node,
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and finishes with the rewritten output.
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"""
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from langgraph.graph import StateGraph
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from src.nodes.reflect import ReflectNode
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from src.nodes.rewrite import RewriteNode
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def build_graph():
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"""
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Builds a simple StateGraph with a single node that echoes user input.
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Build and compile the LangGraph graph.
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Returns
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-------
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langgraph.graph.Graph
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The compiled graph ready for invocation.
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"""
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graph = StateGraph()
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# Add the echo node
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graph.add_node("echo", generate_response)
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# Set the entry point to the echo node
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graph.set_entry_point("echo")
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return graph
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builder = StateGraph()
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builder.add_node("reflect", ReflectNode.run)
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builder.add_node("rewrite", RewriteNode.run)
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# Entry point is the reflect node
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builder.set_entry_point("reflect")
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# Define the flow: reflect -> rewrite
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builder.add_edge("reflect", "rewrite")
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# Finish at the rewrite node
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builder.set_finish("rewrite")
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return builder.compile()
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+9
-91
@@ -1,100 +1,18 @@
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#!/usr/bin/env python3
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"""
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Graph Reflection and Refinement Demo with LangChain LLM Integration.
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Entry point for running the LangGraph workflow.
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This script demonstrates how to integrate LangChain LLMs (OpenAI or Ollama)
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into a simple graph-related prompt. It loads configuration from environment
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variables, selects an appropriate LLM, and runs a prompt chain that
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explains the concept of graph reflection and refinement.
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Requirements:
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- langchain
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- langchain-openai
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- langchain-ollama
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- python-dotenv
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- openai
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This script demonstrates how to invoke the graph with a sample input.
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"""
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import os
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from pathlib import Path
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# Load environment variables from a .env file if present
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try:
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from dotenv import load_dotenv
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load_dotenv()
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except ImportError:
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# dotenv is optional; if not installed, environment variables must be set manually
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pass
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# Import LangChain components
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try:
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from langchain import PromptTemplate, LLMChain
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from langchain_openai import OpenAI
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from langchain_ollama import Ollama
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except ImportError as exc:
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raise ImportError(
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"Required LangChain packages are missing. "
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"Please install them via 'pip install -r requirements.txt'."
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) from exc
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from src.graph import build_graph
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def get_llm() -> "BaseLLM":
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"""
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Instantiate an LLM based on available environment variables.
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Returns:
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An instance of a LangChain LLM (OpenAI or Ollama).
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Raises:
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RuntimeError: If neither OpenAI nor Ollama configuration is found.
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"""
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# Prefer OpenAI if API key is available
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openai_key = os.getenv("OPENAI_API_KEY")
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if openai_key:
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return OpenAI(
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model_name=os.getenv("OPENAI_MODEL", "gpt-3.5-turbo"),
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temperature=float(os.getenv("OPENAI_TEMPERATURE", "0.7")),
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openai_api_key=openai_key,
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)
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# Fallback to Ollama if host is configured
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ollama_host = os.getenv("OLLAMA_HOST")
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if ollama_host:
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return Ollama(
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model=os.getenv("OLLAMA_MODEL", "llama2"),
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temperature=float(os.getenv("OLLAMA_TEMPERATURE", "0.7")),
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base_url=ollama_host,
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)
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raise RuntimeError(
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"No LLM configuration found. Set either OPENAI_API_KEY or OLLAMA_HOST "
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"in your environment."
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)
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def main() -> None:
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"""
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Main entry point: builds a prompt chain and prints the LLM response.
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"""
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llm = get_llm()
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# Simple prompt template explaining graph reflection and refinement
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prompt = PromptTemplate(
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input_variables=[],
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template=(
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"You are an expert in graph theory. "
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"Explain the concepts of graph reflection and graph refinement "
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"in simple, concise terms suitable for a beginner."
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),
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)
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chain = LLMChain(llm=llm, prompt=prompt)
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# Run the chain and print the result
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response = chain.run()
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print("\n=== LLM Response ===\n")
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print(response)
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def main():
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graph = build_graph()
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# Sample input
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input_state = {"input": "Hello world"}
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result = graph.invoke(input_state)
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print("Graph output:", result)
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if __name__ == "__main__":
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@@ -0,0 +1,40 @@
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"""
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Reflect node for LangGraph.
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This node takes the user input from the state and produces a reflection
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message that acknowledges the input. The output is a dictionary containing
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the key 'reflection'.
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"""
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from langgraph.graph import node
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from typing import Dict, Any
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class ReflectNode:
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"""
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A LangGraph node that performs reflection on the input text.
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"""
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@node
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def run(self, state: Dict[str, Any]) -> Dict[str, str]:
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"""
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Generate a reflection message based on the input.
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Parameters
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----------
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state : dict
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The current state of the graph. Expected to contain an 'input'
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key with the user-provided text.
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Returns
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-------
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dict
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A dictionary with a single key 'reflection' containing the
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reflection message.
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"""
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input_text = state.get("input", "")
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reflection = (
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f"I see that you said: '{input_text}'. "
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"Let's reflect on that."
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)
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return {"reflection": reflection}
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@@ -0,0 +1,38 @@
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"""
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Rewrite node for LangGraph.
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This node takes the reflection produced by the ReflectNode and rewrites
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it to a more formal style. The output is a dictionary containing
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the key 'rewritten'.
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"""
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from langgraph.graph import node
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from typing import Dict, Any
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class RewriteNode:
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"""
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A LangGraph node that rewrites the reflection message.
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"""
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@node
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def run(self, state: Dict[str, Any]) -> Dict[str, str]:
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"""
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Rewrite the reflection message.
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Parameters
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----------
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state : dict
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The current state of the graph. Expected to contain a 'reflection'
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key with the message produced by the ReflectNode.
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Returns
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-------
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dict
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A dictionary with a single key 'rewritten' containing the
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rewritten message.
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"""
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reflection = state.get("reflection", "")
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# Simple rewrite: replace "I see" with "I notice"
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rewritten = reflection.replace("I see", "I notice")
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return {"rewritten": rewritten}
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