wehub-resource-sync
80 lines
2.0 KiB
Python
80 lines
2.0 KiB
Python
# server.py
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from mcp.server.fastmcp import FastMCP
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from rag_code import *
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# Create an MCP server
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mcp = FastMCP("MCP-RAG-app",
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host="127.0.0.1",
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port=8080,
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timeout=30)
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@mcp.tool()
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def machine_learning_faq_retrieval_tool(query: str) -> str:
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"""Retrieve the most relevant documents from the machine learning
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FAQ collection. Use this tool when the user asks about ML.
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Input:
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query: str -> The user query to retrieve the most relevant documents
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Output:
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context: str -> most relevant documents retrieved from a vector DB
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"""
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# check type of text
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if not isinstance(query, str):
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raise ValueError("query must be a string")
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retriever = Retriever(QdrantVDB("ml_faq_collection"), EmbedData())
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response = retriever.search(query)
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return response
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@mcp.tool()
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def firecrawl_web_search_tool(query: str) -> list[str]:
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"""
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Search for information on a given topic using Firecrawl.
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Use this tool when the user asks about a specific topic or question
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that is not related to general machine learning.
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Input:
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query: str -> The user query to search for information
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Output:
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context: list[str] -> list of most relevant web search results
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"""
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# check type of text
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if not isinstance(query, str):
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raise ValueError("query must be a string")
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import requests
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import os
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from dotenv import load_dotenv
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load_dotenv()
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url = "https://api.firecrawl.dev/v1/search"
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payload = {
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"query": query,
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"limit": 10,
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"lang": "en",
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"country": "us",
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"timeout": 60000,
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"ignoreInvalidURLs": False,
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}
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headers = {
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"Authorization": f"Bearer {os.getenv('FIRECRAWL_API_KEY')}",
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"Content-Type": "application/json"
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}
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response = requests.request("POST", url, json=payload, headers=headers)
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return response.text
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if __name__ == "__main__":
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print("Starting MCP server at http://127.0.0.1:8080 on port 8080")
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mcp.run()
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