Build RAG Chatbot with LangChain, LangChain vector store, Groq Qwen2.5 32B Instruct, and Azure text-embedding-3-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.
LangChain in-memory vector store: an in-memory, ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. It is intended for demos and does not yet support ids or deletion. (If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand 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.
Azure text-embedding-3-large: This powerful AI model specializes in generating high-quality text embeddings for natural language processing tasks. With its advanced understanding of context, it excels in applications like semantic search, recommendation systems, and clustering. Ideal for developers seeking to enhance text analysis and retrieval in complex datasets while ensuring robust accuracy and performance.

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 Azure text-embedding-3-large

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import AzureOpenAIEmbeddings

embeddings = AzureOpenAIEmbeddings(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

Step 4: Install and Set Up LangChain vector store

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(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.

LangChain in-memory vector store optimization tips

LangChain in-memory vector store is just an ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. It has very limited features and is only intended for demos. If you plan to build a functional or even production-level solution, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)

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.

Azure text-embedding-3-large optimization tips

Azure text-embedding-3-large is a powerful model for generating high-quality text embeddings. Optimize efficiency by preprocessing input text to remove redundant content and ensure the embeddings focus on the most important concepts. Leverage approximate nearest neighbor (ANN) search algorithms like FAISS or HNSW for fast retrieval across large datasets. When dealing with high-throughput applications, implement caching strategies to store frequently accessed embeddings, reducing API call overhead. Use multi-threading or parallel processing to handle batch requests and reduce latency in large-scale systems. Consider dimensionality reduction techniques, such as PCA or quantization, to reduce storage requirements and improve retrieval speed. Regularly update embeddings to reflect new data and ensure your system’s search results are accurate.

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?

By diving into this tutorial, you’ve unlocked the magic of building a RAG system from the ground up! You’ve seen how LangChain acts as the glue that ties everything together, orchestrating the flow of data between components with elegance and flexibility. The LangChain vector store stepped in as your trusty knowledge vault, efficiently storing and retrieving embeddings generated by Azure’s powerful text-embedding-3-large model, which transformed raw text into rich, semantic vectors. Then came the star of the show—Groq’s Qwen2.5 32B Instruct model—delivering lightning-fast, context-aware responses that feel almost human. Together, these tools created a seamless pipeline: ingesting data, embedding it, retrieving relevant context, and generating answers that feel both accurate and natural. Along the way, you picked up pro tips for optimizing performance, like tweaking chunk sizes and refining prompts, and even discovered a free RAG cost calculator to keep your projects budget-friendly.

Now that you’ve seen how these pieces fit together, the world of intelligent applications is yours to explore! Imagine building chatbots that understand nuance, creating research assistants that synthesize information instantly, or crafting tools that democratize access to knowledge. The tutorial gave you the blueprint—now it’s time to experiment, iterate, and make it your own. Whether you’re fine-tuning embeddings, experimenting with hybrid search strategies, or scaling up with Groq’s speed, every tweak brings you closer to something groundbreaking. So fire up your IDE, embrace the trial-and-error joy, and start building. The future of AI-powered solutions isn’t just ahead—it’s in your hands. Let’s go make it happen! 🚀

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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