wehub-resource-sync
56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import os
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from dotenv import load_dotenv
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from groundx import GroundX, Document
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from mcp.server.fastmcp import FastMCP
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load_dotenv()
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mcp = FastMCP("eyelevel-rag")
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client = GroundX(api_key=os.getenv("GROUNDX_API_KEY"))
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@mcp.tool()
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def search_doc_for_rag_context(query: str) -> str:
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"""
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Searches and retrieves relevant context from a knowledge base,
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based on the user's query.
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Args:
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query: The search query supplied by the user.
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Returns:
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str: Relevant text content that can be used by the LLM to answer the query.
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"""
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response = client.search.content(
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id=17221,
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query=query,
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n=10,
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)
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return response.search.text
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@mcp.tool()
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def ingest_documents(local_file_path: str) -> str:
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"""
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Ingest documents from a local file into the knowledge base.
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Args:
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local_file_path: The path to the local file containing the documents to ingest.
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Returns:
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str: A message indicating the documents have been ingested.
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"""
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file_name = os.path.basename(local_file_path)
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client.ingest(
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documents=[
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Document(
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bucket_id=17279,
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file_name=file_name,
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file_path=local_file_path,
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file_type="pdf",
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search_data=dict(
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key = "value",
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),
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)
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]
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)
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return f"""Ingested {file_name} into the knowledge base.
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It should be available in a few minutes"""
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if __name__ == "__main__":
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mcp.run(transport="stdio") |