Build RAG Chatbot with LangChain, Milvus, NVIDA Llama 3 70B Instruct, and OpenAI text-embedding-3-small

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAI’s GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation. A RAG pipeline usually consists of four basic components: a vector database, an embedding model, an LLM, and a framework.

Key Components We'll Use for This RAG Chatbot

This tutorial shows you how to build a simple RAG chatbot in Python using the following components:

LangChain: An open-source framework that helps you orchestrate the interaction between LLMs, vector stores, embedding models, etc, making it easier to integrate a RAG pipeline.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
NVIDIA Llama 3 70B Instruct: This powerful model is designed for instruction-following tasks, leveraging its 70 billion parameters to deliver high-quality responses. It excels in generating detailed answers and engaging in complex dialogues, making it ideal for educational tools, customer support, and interactive applications that require nuanced understanding and guidance.
OpenAI text-embedding-3-small: This model specializes in generating high-quality text embeddings for various NLP tasks, offering a balance between performance and computational efficiency. Its strengths lie in semantic understanding and similarity comparisons, making it ideal for applications in search, recommendation systems, and clustering tasks where quick processing is essential.

By the end of this tutorial, you’ll have a functional chatbot capable of answering questions based on a custom knowledge base.

Note: Since we may use proprietary models in our tutorials, make sure you have the required API key beforehand.

Step 1: Install and Set Up LangChain

%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph

Step 2: Install and Set Up NVIDA Llama 3 70B Instruct

pip install -qU "langchain-nvidia-ai-endpoints"

import getpass
import os

if not os.environ.get("NVIDIA_API_KEY"):
  os.environ["NVIDIA_API_KEY"] = getpass.getpass("Enter API key for NVIDIA: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("meta/llama3-70b-instruct", model_provider="nvidia")

Step 3: Install and Set Up OpenAI text-embedding-3-small

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

Step 4: Install and Set Up Milvus

pip install -qU langchain-milvus

from langchain_milvus import Milvus

vector_store = Milvus(embedding_function=embeddings)

Step 5: Build a RAG Chatbot

Now that you’ve set up all components, let’s start to build a simple chatbot. We’ll use the Milvus introduction doc as a private knowledge base. You can replace it with your own dataset to customize your RAG chatbot.

import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict

# Load and chunk contents of the blog
loader = WebBaseLoader(
    web_paths=("https://milvus.io/docs/overview.md",),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(
            class_=("doc-style doc-post-content")
        )
    ),
)

docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)

# Index chunks
_ = vector_store.add_documents(documents=all_splits)

# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")


# Define state for application
class State(TypedDict):
    question: str
    context: List[Document]
    answer: str


# Define application steps
def retrieve(state: State):
    retrieved_docs = vector_store.similarity_search(state["question"])
    return {"context": retrieved_docs}


def generate(state: State):
    docs_content = "\n\n".join(doc.page_content for doc in state["context"])
    messages = prompt.invoke({"question": state["question"], "context": docs_content})
    response = llm.invoke(messages)
    return {"answer": response.content}


# Compile application and test
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()

Test the Chatbot

Yeah! You've built your own chatbot. Let's ask the chatbot a question.

response = graph.invoke({"question": "What data types does Milvus support?"})
print(response["answer"])

Example Output

Milvus supports various data types including sparse vectors, binary vectors, JSON, and arrays. Additionally, it handles common numerical and character types, making it versatile for different data modeling needs. This allows users to manage unstructured or multi-modal data efficiently.

Optimization Tips

As you build your RAG system, optimization is key to ensuring peak performance and efficiency. While setting up the components is an essential first step, fine-tuning each one will help you create a solution that works even better and scales seamlessly. In this section, we’ll share some practical tips for optimizing all these components, giving you the edge to build smarter, faster, and more responsive RAG applications.

LangChain optimization tips

To optimize LangChain, focus on minimizing redundant operations in your workflow by structuring your chains and agents efficiently. Use caching to avoid repeated computations, speeding up your system, and experiment with modular design to ensure that components like models or databases can be easily swapped out. This will provide both flexibility and efficiency, allowing you to quickly scale your system without unnecessary delays or complications.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

NVIDIA Llama 3 70B Instruct optimization tips

NVIDIA Llama 3 70B Instruct is a high-precision model ideal for RAG applications requiring in-depth reasoning and analysis. Optimize retrieval by using hierarchical document ranking to filter only the most relevant passages, reducing unnecessary token consumption. Structure prompts with a clear separation between user queries, retrieved context, and system instructions for optimal performance. Keep temperature between 0.1 and 0.3 for factual consistency while fine-tuning top-k and top-p for better control over response diversity. Leverage NVIDIA’s TensorRT-LLM and optimized inference stacks to accelerate model execution and reduce latency. Use batching strategies to improve throughput for high-volume workloads. If deploying multiple models, assign Llama 3 70B to handle complex queries requiring deep contextual understanding while reserving lighter models for faster, low-complexity tasks.

OpenAI text-embedding-3-small optimization tips

OpenAI text-embedding-3-small offers a lightweight alternative with faster processing speeds, making it well-suited for real-time RAG applications. Improve retrieval performance by fine-tuning similarity thresholds to minimize false positives in ANN searches. Keep input text concise and remove redundant information before embedding to maximize embedding efficiency. Utilize caching for frequently queried text embeddings to reduce unnecessary recomputation. When scaling, distribute embedding generation across multiple workers to optimize throughput. Apply post-processing filters, such as cosine similarity cutoffs, to refine search results. Consider hybrid search (dense + keyword-based) for improved retrieval when dealing with varied query types.

By implementing these tips across your components, you'll be able to enhance the performance and functionality of your RAG system, ensuring it’s optimized for both speed and accuracy. Keep testing, iterating, and refining your setup to stay ahead in the ever-evolving world of AI development.

RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds

Estimating the cost of a Retrieval-Augmented Generation (RAG) pipeline involves analyzing expenses across vector storage, compute resources, and API usage. Key cost drivers include vector database queries, embedding generation, and LLM inference.

RAG Cost Calculator is a free tool that quickly estimates the cost of building a RAG pipeline, including chunking, embedding, vector storage/search, and LLM generation. It also helps you identify cost-saving opportunities and achieve up to 10x cost reduction on vector databases with the serverless option.

Calculate your RAG cost now.

Calculate your RAG cost

What Have You Learned?

What have you learned? Wow, what a journey this tutorial has been! You've unlocked the magic of creating an advanced Retrieval-Augmented Generation (RAG) system by successfully integrating four powerful components: a robust framework with LangChain, a blazing-fast vector database with Milvus, an intelligent conversational model powered by the NVIDIA Llama 3 70B, and a rich embedding model using OpenAI's text-embedding-3-small! Isn’t that impressive?

By harnessing LangChain, you've seen how this framework elegantly ties all the pieces together, enabling seamless communication between components while providing the structure needed for optimal performance. Milvus, with its capability to store and retrieve high-dimensional vectors at lightning speed, ensures your searches are not just fast but also incredibly efficient. And let’s not forget about the conversational magic that NVIDIA Llama 3 brings, allowing your applications to understand and interact in a way that feels truly natural! The embedding model rounds it all out, enabling your RAG system to generate rich and nuanced semantic representations, setting the stage for exceptional user experiences.

This tutorial also provided you with optimization tips and even introduced a handy free cost calculator to streamline your journey. The sky’s the limit here! So, what are you waiting for? Dive in, start building, optimizing, and innovating your own RAG applications. Your creativity and ingenuity can lead to groundbreaking advances that make a real impact. Go for it!

Further Resources

🌟 In addition to this RAG tutorial, unleash your full potential with these incredible resources to level up your RAG skills.

How to Build a Multimodal RAG | Documentation
How to Enhance the Performance of Your RAG Pipeline
Graph RAG with Milvus | Documentation
How to Evaluate RAG Applications - Zilliz Learn
Generative AI Resource Hub | Zilliz

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