Build RAG with Milvus and Docling

Docling streamlines document parsing and understanding across diverse formats for AI applications. With advanced PDF comprehension and unified document representation, Docling makes unstructured document data ready for downstream workflows.

In this tutorial, we’ll show you how to build a Retrieval-Augmented Generation (RAG) pipeline using Milvus and Docling. The pipeline integrates Docling for document parsing, Milvus for vector storage, and OpenAI for generating insightful, context-aware responses.

Preparation

Dependencies and Environment

To start, install the required dependencies by running the following command:

$ pip install --upgrade pymilvus docling openai

Setting Up API Keys

We will use OpenAI as the LLM in this example. You should prepare the OPENAI_API_KEY as an environment variable.

import os

os.environ["OPENAI_API_KEY"] = "sk-***********"

Prepare the LLM and Embedding Model

We initialize the OpenAI client to prepare the embedding model.

from openai import OpenAI

openai_client = OpenAI()

Define a function to generate text embeddings using OpenAI client. We use the text-embedding-3-small model as an example.

def emb_text(text):
    return (
        openai_client.embeddings.create(input=text, model="text-embedding-3-small")
        .data[0]
        .embedding
    )

Generate a test embedding and print its dimension and first few elements.

test_embedding = emb_text("This is a test")
embedding_dim = len(test_embedding)
print(embedding_dim)
print(test_embedding[:10])

1536
[0.00988506618887186, -0.005540902726352215, 0.0068014683201909065, -0.03810417652130127, -0.018254263326525688, -0.041231658309698105, -0.007651153020560741, 0.03220026567578316, 0.01892443746328354, 0.00010708322952268645]

Process Data Using Docling

Docling can parse various document formats into a unified representation (Docling Document), which can then be exported to different output formats. For a full list of supported input and output formats, please refer to the official documentation.

In this tutorial, we will use a Markdown file (source) as the input. We will process the document using a HierarchicalChunker provided by Docling to generate structured, hierarchical chunks suitable for downstream RAG tasks.

from docling.document_converter import DocumentConverter
from docling_core.transforms.chunker import HierarchicalChunker

converter = DocumentConverter()
chunker = HierarchicalChunker()

# Convert the input file to Docling Document
source = "https://milvus.io/docs/overview.md"
doc = converter.convert(source).document

# Perform hierarchical chunking
texts = [chunk.text for chunk in chunker.chunk(doc)]

for i, text in enumerate(texts[:5]):
    print(f"Chunk {i+1}:\n{text}\n{'-'*50}")

Chunk 1:
Milvus is a high-performance, highly scalable vector database that runs efficiently across a wide range of environments, from a laptop to large-scale distributed systems. It is available as both open-source software and a cloud service.
--------------------------------------------------
Chunk 2:
Milvus is an open-source project under LF AI & Data Foundation distributed under the Apache 2.0 license. Most contributors are experts from the high-performance computing (HPC) community, specializing in building large-scale systems and optimizing hardware-aware code. Core contributors include professionals from Zilliz, ARM, NVIDIA, AMD, Intel, Meta, IBM, Salesforce, Alibaba, and Microsoft.
--------------------------------------------------
Chunk 3:
Unstructured data, such as text, images, and audio, varies in format and carries rich underlying semantics, making it challenging to analyze. To manage this complexity, embeddings are used to convert unstructured data into numerical vectors that capture its essential characteristics. These vectors are then stored in a vector database, enabling fast and scalable searches and analytics.
--------------------------------------------------
Chunk 4:
Milvus offers robust data modeling capabilities, enabling you to organize your unstructured or multi-modal data into structured collections. It supports a wide range of data types for different attribute modeling, including common numerical and character types, various vector types, arrays, sets, and JSON, saving you from the effort of maintaining multiple database systems.
--------------------------------------------------
Chunk 5:
Untructured data, embeddings, and Milvus
--------------------------------------------------

Load Data into Milvus

Create the collection

from pymilvus import MilvusClient

milvus_client = MilvusClient(uri="./milvus_demo.db")
collection_name = "my_rag_collection"

Check if the collection already exists and drop it if it does.

if milvus_client.has_collection(collection_name):
    milvus_client.drop_collection(collection_name)

Create a new collection with specified parameters.

If we don’t specify any field information, Milvus will automatically create a default id field for primary key, and a vector field to store the vector data. A reserved JSON field is used to store non-schema-defined fields and their values.

milvus_client.create_collection(
    collection_name=collection_name,
    dimension=embedding_dim,
    metric_type="IP",  # Inner product distance
    consistency_level="Strong",  # Supported values are (`"Strong"`, `"Session"`, `"Bounded"`, `"Eventually"`). See https://milvus.io/docs/consistency.md#Consistency-Level for more details.
)

Insert data

from tqdm import tqdm

data = []

for i, chunk in enumerate(tqdm(texts, desc="Processing chunks")):
    embedding = emb_text(chunk)
    data.append({"id": i, "vector": embedding, "text": chunk})

milvus_client.insert(collection_name=collection_name, data=data)

Processing chunks: 100%|██████████| 36/36 [00:18<00:00,  1.96it/s]





{'insert_count': 36, 'ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35], 'cost': 0}

Build RAG

Retrieve data for a query

Let’s specify a query question about the website we just scraped.

question = (
    "What are the three deployment modes of Milvus, and what are their differences?"
)

Search for the question in the collection and retrieve the semantic top-3 matches.

search_res = milvus_client.search(
    collection_name=collection_name,
    data=[emb_text(question)],
    limit=3,
    search_params={"metric_type": "IP", "params": {}},
    output_fields=["text"],
)

Let’s take a look at the search results of the query

import json

retrieved_lines_with_distances = [
    (res["entity"]["text"], res["distance"]) for res in search_res[0]
]
print(json.dumps(retrieved_lines_with_distances, indent=4))

[
    [
        "Milvus offers three deployment modes, covering a wide range of data scales\u2014from local prototyping in Jupyter Notebooks to massive Kubernetes clusters managing tens of billions of vectors:",
        0.6503741145133972
    ],
    [
        "Milvus Lite is a Python library that can be easily integrated into your applications. As a lightweight version of Milvus, it\u2019s ideal for quick prototyping in Jupyter Notebooks or running on edge devices with limited resources. Learn more.\nMilvus Standalone is a single-machine server deployment, with all components bundled into a single Docker image for convenient deployment. Learn more.\nMilvus Distributed can be deployed on Kubernetes clusters, featuring a cloud-native architecture designed for billion-scale or even larger scenarios. This architecture ensures redundancy in critical components. Learn more.",
        0.6281254291534424
    ],
    [
        "What is Milvus?\nUnstructured Data, Embeddings, and Milvus\nWhat Makes Milvus so Fast\uff1f\nWhat Makes Milvus so Scalable\nTypes of Searches Supported by Milvus\nComprehensive Feature Set",
        0.6117545962333679
    ]
]

Use LLM to get a RAG response

Convert the retrieved documents into a string format.

context = "\n".join(
    [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]
)

Define system and user prompts for the Lanage Model. This prompt is assembled with the retrieved documents from Milvus.

SYSTEM_PROMPT = """
Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.
"""
USER_PROMPT = f"""
Use the following pieces of information enclosed in <context> tags to provide an answer to the question enclosed in <question> tags.
<context>
{context}
</context>
<question>
{question}
</question>
"""

Use OpenAI ChatGPT to generate a response based on the prompts.

response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": USER_PROMPT},
    ],
)
print(response.choices[0].message.content)

The three deployment modes of Milvus are Milvus Lite, Milvus Standalone, and Milvus Distributed. 

1. **Milvus Lite**: This is a Python library designed for easy integration into applications. It is lightweight and ideal for quick prototyping in Jupyter Notebooks or for use on edge devices with limited resources.

2. **Milvus Standalone**: This deployment mode involves a single-machine server with all components bundled into a single Docker image for convenient deployment.

3. **Milvus Distributed**: This mode can be deployed on Kubernetes clusters and is built for larger-scale scenarios, including managing billions of vectors. It features a cloud-native architecture that ensures redundancy in critical components, making it suited for extensive scalability.