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