feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'

src/agent.py

@@ -0,0 +1,46 @@
+import os
+from typing import List, Dict, Any
+
+from langchain_community.llms import Ollama
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.tools import BaseTool
+from langchain.agents import AgentExecutor, create_openai_tools_agent
+from langchain.schema import HumanMessage, SystemMessage
+
+from .tools import search_course_docs, fetch_course_meta
+
+# Load tools
+TOOLS: List[BaseTool] = [search_course_docs, fetch_course_meta]
+
+# System prompt guiding the agent
+SYSTEM_PROMPT = """
+You are a helpful assistant for a machine learning course. Your job is to answer user questions.
+
+- If the question is about course materials, lecture slides, assignments, or any content that can be found in the FAQ documents, use the tool `search_course_docs`.
+- If the question is about course schedule, instructor information, or other metadata, use the tool `fetch_course_meta`.
+- Do not use both tools unless absolutely necessary.
+- In your answer, always include a source tag: `source: chroma` if you used the FAQ tool, or `source: mcp_meta` if you used the metadata tool.
+"""
+
+def build_agent() -> AgentExecutor:
+    """
+    Build and return a LangChain AgentExecutor with the defined tools and system prompt.
+    """
+    llm = Ollama(model="llama3", temperature=0.0)
+
+    # Prompt template
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            SystemMessage(content=SYSTEM_PROMPT),
+            MessagesPlaceholder(variable_name="history"),
+            HumanMessage(content="{input}"),
+        ]
+    )
+
+    # Create the agent
+    agent = create_openai_tools_agent(llm=llm, tools=TOOLS, prompt=prompt)
+
+    # Wrap with AgentExecutor
+    agent_executor = AgentExecutor(agent=agent, tools=TOOLS, verbose=True, handle_parsing_errors=True)
+    return agent_executor