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

langchain_agent.py

@@ -0,0 +1,46 @@
+import os
+from langchain_ollama import Ollama
+from langchain.agents import Tool, initialize_agent, AgentType
+from src.vector_store_utils import search_course_docs
+from src.mcp_utils import fetch_course_meta
+
+def chroma_search_tool(collection) -> Tool:
+    def _search(query: str) -> str:
+        results = search_course_docs(collection, query, k=3)
+        if not results:
+            return "No relevant documents found.\nSource: chroma"
+        return "\n\n".join(
+            [f"Source {i+1}:\n{res['page_content']}" for i, res in enumerate(results)]
+        ) + "\nSource: chroma"
+    return Tool(
+        name="Chroma Search",
+        func=_search,
+        description="Search the FAQ stored in Chroma. Use this for general course questions."
+    )
+
+def mcp_meta_tool() -> Tool:
+    def _meta(query: str) -> str:
+        return fetch_course_meta(query) + "\nSource: mcp_meta"
+    return Tool(
+        name="MCP Metadata",
+        func=_meta,
+        description="Fetch metadata about the course from the MCP service."
+    )
+
+def create_agent(collection):
+    tools = [chroma_search_tool(collection), mcp_meta_tool()]
+    llm = Ollama(model="llama3")
+    system_prompt = (
+        "You are a helpful assistant for a course. "
+        "Use the 'Chroma Search' tool for general FAQ questions. "
+        "Use the 'MCP Metadata' tool for questions about course schedule, modules, or lessons. "
+        "Always include a source tag in your answer: 'Source: chroma' or 'Source: mcp_meta'."
+    )
+    agent = initialize_agent(
+        tools,
+        llm,
+        agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
+        verbose=False,
+        agent_kwargs={"system_message": system_prompt},
+    )
+    return agent