feat: solution for 'Untitled Task'

requirements.txt

@@ -1 +1,2 @@
-langgraph\nlangchain\nopenai\npython-dotenv\n
+langchain==0.2.0
+openai==1.30.0

src/main.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+"""
+Stream-режим AI-агента с использованием LangChain.
+"""
+
+import os
+from langchain import OpenAI
+from langchain.agents import create_agent, AgentType
+from langchain.tools import Tool
+
+# Define a simple echo tool
+def echo(text: str) -> str:
+    """Echoes the input text."""
+    return text
+
+echo_tool = Tool(
+    name="Echo",
+    func=echo,
+    description="Echoes the input text."
+)
+
+# Create the LLM
+llm = OpenAI(temperature=0)
+
+# Create the agent with the echo tool
+agent = create_agent(
+    llm=llm,
+    tools=[echo_tool],
+    agent_type=AgentType.OPENAI_FUNCTIONS,
+    verbose=False,
+)
+
+def format_message(message) -> str:
+    """
+    Formats a message for printing.
+    If the message contains content, returns it.
+    Otherwise, formats the tool call.
+    """
+    if getattr(message, "content", None):
+        return message.content
+    # For tool calls
+    tool_call = message.tool_calls[0]
+    return f"{tool_call['name']}({tool_call['args']})"
+
+def main():
+    user_input = input("Введите сообщение: ")
+    # Start streaming
+    stream = agent.stream(
+        {"messages": [{"role": "user", "content": user_input}]},
+        stream_mode=["messages", "updates"]
+    )
+
+    step = 1
+    for chunk_type, chunk_data in stream:
+        if chunk_type == "messages":
+            # chunk_data is a tuple (message, meta)
+            message, meta = chunk_data
+            # Insert separator when step changes
+            if meta.get("langgraph_step") != step:
+                step = meta.get("langgraph_step")
+                print("\n --- --- --- \n")
+            # Print the message content without newline
+            if getattr(message, "content", None):
+                print(message.content, end="", flush=True)
+        elif chunk_type == "updates":
+            # chunk_data contains information about completed steps
+            if chunk_data.get("model"):
+                last_message = chunk_data["model"]["messages"][-1]
+                formatted = format_message(last_message)
+                print(formatted, end="", flush=True)
+
+    # Final newline after streaming is done
+    print()
+
+if __name__ == "__main__":
+    main()