Build a RAG with Milvus and Unstructured

Unstructured provides a platform and tools to ingest and process unstructured documents for Retrieval Augmented Generation (RAG) and model fine-tuning. It offers both a no-code UI platform and serverless API services, allowing users to process data on Unstructured-hosted compute resources.

In this tutorial, we will use Unstructured to ingest PDF documents and then use Milvus to build a RAG pipeline.

Preparation

Dependencies and Environment

$ pip install -qU "unstructured[pdf]" pymilvus openai

import os

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

Prepare Milvus and OpenAI clients

You can use the Milvus client to create a Milvus collection and insert data into it.

from pymilvus import MilvusClient, DataType

# Initialize Milvus client
milvus_client = MilvusClient(uri="./milvus_demo.db")

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

collection_name = "my_rag_collection"

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

Prepare a OpenAI client to generate embeddings and generate responses.

from openai import OpenAI

openai_client = OpenAI()


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
[0.009889289736747742, -0.005578675772994757, 0.00683477520942688, -0.03805781528353691, -0.01824733428657055, -0.04121600463986397, -0.007636285852640867, 0.03225184231996536, 0.018949154764413834, 9.352207416668534e-05]

Create Milvus Collection

We will create a collection with the following schema:

• id: the primary key, which is a unique identifier for each document.
• vector: the embedding of the document.
• text: the text content of the document.
• metadata: the metadata of the document.

Then we build an AUTOINDEX index on the vector field. And then create the collection.

# Create schema
schema = milvus_client.create_schema(auto_id=False, enable_dynamic_field=False)
# Add fields to schema
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=embedding_dim)
schema.add_field(field_name="text", datatype=DataType.VARCHAR, max_length=65535)
schema.add_field(field_name="metadata", datatype=DataType.JSON)
index_params = MilvusClient.prepare_index_params()
index_params.add_index(
    field_name="vector",
    metric_type="COSINE",
    index_type="AUTOINDEX",
)
milvus_client.create_collection(
    collection_name=collection_name,
    schema=schema,
    index_params=index_params,
    consistency_level="Strong",
)

milvus_client.load_collection(collection_name=collection_name)

Load data from Unstructured

Unstructured provides a flexible and powerful ingestion pipeline to process various file types, including PDF, HTML, and more. We will partition and chunk a local PDF file. And then load the data into Milvus.

import warnings
from unstructured.partition.auto import partition

warnings.filterwarnings("ignore")

elements = partition(
    filename="./pdf_files/WhatisMilvus.pdf",
    strategy="hi_res",
    chunking_strategy="by_title",
)  # Replace with the path to your PDF file

Let’s examine the partitioned elements from the PDF file. Each element represents a chunk of content extracted by Unstructured’s partitioning process.

for element in elements:
    print(element)
    break

What is Milvus?

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.

Insert data into Milvus.

data = []
for i, element in enumerate(elements):
    data.append(
        {
            "id": i,
            "vector": emb_text(element.text),
            "text": element.text,
            "metadata": element.metadata.to_dict(),
        }
    )
milvus_client.insert(collection_name=collection_name, data=data)

{'insert_count': 29, '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], 'cost': 0}

Retrieve and Generate Response

Define a function to retrieve relevant documents from Milvus.

def retrieve_documents(question, top_k=3):
    search_res = milvus_client.search(
        collection_name=collection_name,
        data=[emb_text(question)],
        limit=top_k,
        # search_params={"metric_type": "IP", "params": {}},
        output_fields=["text"],
    )
    return [(res["entity"]["text"], res["distance"]) for res in search_res[0]]

Define a function to generate a response using the retrieved documents in the RAG pipeline.

def generate_rag_response(question):
    retrieved_docs = retrieve_documents(question)
    context = "\n".join([f"Text: {doc[0]}\n" for doc in retrieved_docs])
    system_prompt = (
        "You are an AI assistant. Provide answers based on the given context."
    )
    user_prompt = f"""
    Use the following pieces of information to answer the question. If the information is not in the context, say you don't know.
    
    Context:
    {context}
    
    Question: {question}
    """
    response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ],
    )
    return response.choices[0].message.content

Let’s test the RAG pipeline with a sample question.

question = "What is the Advanced Search Algorithms in Milvus?"
answer = generate_rag_response(question)
print(f"Question: {question}")
print(f"Answer: {answer}")

Question: What is the Advanced Search Algorithms in Milvus?
Answer: The Advanced Search Algorithms in Milvus include a wide range of in-memory and on-disk indexing/search algorithms such as IVF, HNSW, and DiskANN. These algorithms have been deeply optimized, and Milvus delivers 30%-70% better performance compared to popular implementations like FAISS and HNSWLib.