The Engineer's Guide to OpenRAG: Seamless Integration of OpenSearch and Retrieval-Augmented Generation

2026-03-27

Here is a breakdown of why this matters and how you can get it running.

In a typical RAG setup, you're juggling three massive tasks

Parsing
Converting messy PDFs/docs into clean text (handled here by Docling).

Storage/Search
Indexing that text so you can find it later (handled by OpenSearch).

Orchestration
Connecting the AI to the data (handled by Langflow).

By bundling these, OpenRAG removes the "integration tax"—you don't have to spend days figuring out how to make Version A of one tool talk to Version B of another.

Since this is a containerized world, the easiest way to deploy OpenRAG is via Docker Compose. This ensures all three services (Langflow, Docling, and OpenSearch) wake up in the same network and can see each other immediately.

First, clone the repository and spin up the environment

git clone https://github.com/langflow-ai/openrag.git
cd openrag
docker-compose up -d

Once the containers are healthy

Langflow Interface
Usually at http://localhost:7860

OpenSearch
Running in the background at http://localhost:9200

In Langflow (the heart of OpenRAG), you create "flows." Here is how you would conceptually represent a document ingestion script using their components.

While you'll mostly use the drag-and-drop UI, you can interact with your OpenRAG backend using the Langflow Python SDK

from langflow.load import run_flow_from_json

# This sends a query to your pre-configured OpenRAG pipeline
results = run_flow_from_json(
    flow="My_OpenRAG_Pipeline.json",
    input_value="What are the key features of OpenRAG?",
    fallback_to_env_vars=True # Automatically uses your API keys
)

print(results[0].outputs[0].results)

Docling Power
Docling is fantastic at understanding complex document layouts (like tables or multi-column PDFs) that usually break simpler parsers.

Visual Debugging
Instead of digging through logs to see why a prompt failed, you can see exactly which "node" in Langflow dropped the ball.

OpenSearch Scalability
Using OpenSearch means that if your project grows from 10 documents to 10 million, your infrastructure is already built to handle it.

OpenRAG essentially turns a complex infrastructure project into a manageable software deployment. It’s perfect for prototyping internal tools where security and data privacy are top priorities!