Grounding email replies in verified company documentation
Generic LLM auto-responders often invent policies or quote incorrect pricing. Retrieval-Augmented Generation (RAG) solves this by retrieving relevant documentation snippets before generating the email response.
With LlamaIndex, developers can index Markdown docs, API references, and product manuals to ground every generated draft in factual context.
Full RAG Ingestion and Email Dispatch Blueprint
Here is a complete Python pipeline using LlamaIndex VectorStoreIndex and FunctionTool to answer incoming support emails.
PYTHON
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.tools import FunctionTool
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
import requests
import os
# 1. Load documentation into vector index
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=3)
# 2. Define email dispatch tool
def dispatch_support_reply(recipient: str, subject: str, response_body: str) -> str:
"""Sends a verified customer support reply email."""
res = requests.post(
"https://api.sadasend.com/v1/emails",
headers={"Authorization": f"Bearer {os.getenv('SADASEND_API_KEY')}"},
json={"to": recipient, "subject": subject, "text": response_body}
)
return "Dispatched" if res.status_code == 200 else f"Error: {res.text}"
email_tool = FunctionTool.from_defaults(fn=dispatch_support_reply)
# 3. Create RAG-powered agent
llm = OpenAI(model="gpt-4o")
agent = ReActAgent.from_tools([email_tool], llm=llm, verbose=True)
# Process customer inquiry
inquiry = "Customer Alex (alex@client.io) asks: 'How do I configure DMARC reporting on SadaSend?'"
agent.chat(f"Search our docs to answer: {inquiry}, then send the reply email.")Deliverability and compliance safeguards
- Include source attribution in drafted emails so human reviewers can verify claims quickly.
- Set a confidence score threshold: If vector search similarity is below 0.75, route the ticket to human support rather than auto-sending.
- Enforce strict per-key rate limits (e.g. max 50 emails/hour) on the agent credentials.
Early Access
Building AI agents that send email?
Join the SadaSend early access waitlist to get scoped API keys, recipient allowlists, and Model Context Protocol (MCP) servers upon launch.