Build RAG Chatbot with LangChain, Milvus, Groq Qwen2.5 32B Instruct, and IBM multilingual-e5-large

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.
Groq Qwen2.5 32B Instruct: Groq Qwen2.5 is a large-scale AI language model designed for instruction-following tasks. With 32 billion parameters, it excels in generating coherent, contextually relevant responses and understanding complex queries. Ideal for applications in customer service, content creation, and educational tools, it enhances user interactions through its robust and adaptable capabilities.
IBM multilingual-e5-large: This advanced AI model excels in natural language processing across multiple languages. Designed for tasks such as text generation, translation, and sentiment analysis, it exhibits strong contextual understanding and fluency. Ideal for global enterprises seeking to enhance customer interaction and automate multilingual communication.

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 Groq Qwen2.5 32B Instruct

pip install -qU "langchain[groq]"

import getpass
import os

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

from langchain.chat_models import init_chat_model

llm = init_chat_model("qwen-2.5-32b", model_provider="groq")

Step 3: Install and Set Up IBM multilingual-e5-large

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="intfloat/multilingual-e5-large",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

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.

Groq Qwen2.5 32B Instruct optimization tips

To optimize the Groq Qwen2.5 32B Instruct model in a Retrieval-Augmented Generation (RAG) setup, consider implementing mixed precision training to reduce memory usage and enhance throughput. Fine-tune hyperparameters such as learning rate and batch size based on your dataset to improve performance. Utilize efficient indexing methods for retrieval components to speed up query responses. Additionally, cache frequent queries and responses to minimize redundant computations. Regularly assess model performance on validation data to identify any degradation over time, allowing for timely retraining or adjustments. Lastly, leverage data augmentation techniques to enrich your training dataset, which can help the model generalize better across unseen queries.

IBM multilingual-e5-large optimization tips

To optimize the IBM multilingual-e5-large model for use in a Retrieval-Augmented Generation (RAG) setup, consider fine-tuning the model on domain-specific data to improve relevance and coherence during generation. Maintain balanced retrieval by leveraging diverse and high-quality datasets to enhance the quality of context provided to the model. Implement caching mechanisms for commonly requested queries to minimize latency and improve response times. Monitor prompt design closely, ensuring they are concise and context-rich to guide the model effectively. Lastly, integrate user feedback to iteratively refine the retrieval and generation processes, thereby enhancing overall performance and user satisfaction.

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?

Wow, you’ve just unlocked the power to build a sophisticated RAG system from the ground up! By diving into this tutorial, you’ve learned how to seamlessly integrate four powerhouse components: LangChain as the orchestration framework, Milvus as the lightning-fast vector database, Groq’s Qwen2.5 32B Instruct as the high-performance LLM, and IBM’s multilingual-e5-large as the versatile embedding model. LangChain tied everything together, acting as the glue that streamlined data flow and pipeline logic, while Milvus handled vector storage and retrieval with jaw-dropping speed, ensuring your system scales effortlessly. Groq’s Qwen2.5 32B Instruct blew minds with its rapid, coherent text generation, and IBM’s multilingual-e5-large embeddings broke language barriers, enabling your RAG pipeline to understand and process queries across multiple languages. You even discovered optimization tricks like chunking strategies and hybrid search configurations to fine-tune performance—plus, that free RAG cost calculator you explored is a game-changer for balancing efficiency and budget!

But this isn’t just about building a tool—it’s about opening doors to innovation. You now have the skills to create AI applications that answer complex questions, translate knowledge across languages, and adapt to real-world data dynamically. Imagine enhancing customer support bots, powering multilingual research tools, or crafting personalized content engines—the possibilities are endless. And with the tips you’ve learned, you’re equipped to optimize every layer of your RAG system for speed, accuracy, and cost-effectiveness. So what’s next? Grab your code editor, experiment fearlessly, and start turning your ideas into reality. The future of intelligent applications is in your hands—build something amazing, share it with the world, and keep pushing the boundaries of what’s possible. Let’s go! 🚀

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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