From 4894cc1dcfb4acd18215b0dc542e1311acf318bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=A0=D0=BE=D0=BC?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BE=D0=B2?= <kirilinka320@gmail.com>
Date: Thu, 4 Jun 2026 22:57:18 +0000
Subject: [PATCH] Add src/agent.py

---
 src/agent.py | 54 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 src/agent.py

diff --git a/src/agent.py b/src/agent.py
new file mode 100644
index 0000000..c4939bb
--- /dev/null
+++ b/src/agent.py
@@ -0,0 +1,54 @@
+"""Agent implementation using LangChain tool‑calling.
+
+The agent is built with the modern LangChain interface. It uses a system prompt that
+encourages the assistant to search the knowledge base before answering.
+"""
+
+from typing import List
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_ollama import ChatOllama
+
+from .tools import search_knowledge_base, add_to_knowledge_base
+
+# Create the LLM that will power the agent. We use Ollama's local llama3 model.
+llm = ChatOllama(model="llama3", temperature=0.7)
+
+# Register tools
+TOOLS: List = [search_knowledge_base, add_to_knowledge_base]
+
+# Prompt template that tells the agent to use the tools when needed.
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a helpful assistant. Use the provided tools to answer the user."),
+    ("placeholder", "{chat_history}"),
+    ("human", "{input}"),
+    ("placeholder", "{agent_scratchpad}"),
+])
+
+# Create the tool‑calling agent
+agent = create_tool_calling_agent(llm, TOOLS, prompt)
+
+# Wrap it in an executor that will handle the conversation loop
+agent_executor = AgentExecutor(agent=agent, tools=TOOLS, verbose=True)
+
+# Expose a simple function that can be called from a CLI or other entry point
+
+def run_agent(messages: List[dict]):
+    """Run the agent on a list of chat messages.
+
+    Parameters
+    ----------
+    messages: List[dict]
+        Each message is a dict with keys ``role`` ("user" or "assistant") and ``content``.
+
+    Returns
+    -------
+    str
+        The assistant's reply.
+    """
+    # Convert the list of messages into the format expected by the executor
+    chat_history = [(msg["role"], msg["content"]) for msg in messages]
+    input_text = messages[-1]["content"] if messages else ""
+    result = agent_executor.invoke({"input": input_text, "chat_history": chat_history})
+    return result["output"]
\ No newline at end of file