Build RAG Chatbot with LangChain, Zilliz Cloud, Mistral AI Mistral Nemo, and Amazon Titan Text Embeddings v2

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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It offers a free tier supporting up to 1 million vectors.
Mistral AI Mistral Nemo: This model is designed for high-performance natural language processing, emphasizing interpretability and adaptability. It excels in tasks involving text generation, dialogue systems, and content creation. Ideal for industries like marketing and entertainment, Mistral Nemo delivers rich, coherent narratives while allowing for fine-tuning to specific domain requirements.
Amazon Titan Text Embeddings v2: This model generates high-quality text embeddings, enabling nuanced semantic understanding and similarity comparisons. It boasts enhanced performance and scalability, making it suitable for tasks such as information retrieval, recommendation systems, and sentiment analysis. Ideal for applications needing robust and efficient language representation at scale.

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 Mistral AI Mistral Nemo

pip install -qU "langchain[mistralai]"

import getpass
import os

if not os.environ.get("MISTRAL_API_KEY"):
  os.environ["MISTRAL_API_KEY"] = getpass.getpass("Enter API key for Mistral AI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("open-mistral-nemo", model_provider="mistralai")

Step 3: Install and Set Up Amazon Titan Text Embeddings v2

pip install -qU langchain-aws

from langchain_aws import BedrockEmbeddings

embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0")

Step 4: Install and Set Up Zilliz Cloud

pip install -qU langchain-milvus

from langchain_milvus import Zilliz

vector_store = Zilliz(
    embedding_function=embeddings,
    connection_args={
        "uri": ZILLIZ_CLOUD_URI,
        "token": ZILLIZ_CLOUD_TOKEN,
    },
)

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

Mistral AI Mistral Nemo optimization tips

Mistral Nemo is designed for domain-specific knowledge retrieval and response generation, making retrieval efficiency a top priority. Optimize performance by fine-tuning retrieval pipelines to ensure domain-relevant embeddings are used for similarity searches. Use a ranking layer to prioritize the most contextually relevant passages before passing them to the model, reducing token waste. Adjust temperature settings (0.1–0.3) to maintain consistent, structured responses. In latency-sensitive applications, use token streaming to provide incremental responses rather than waiting for full completion. If running Nemo in a production environment, consider distributed inference techniques to manage large-scale workloads effectively. Monitor retrieval accuracy continuously and fine-tune embeddings periodically to keep responses aligned with domain-specific updates.

Amazon Titan Text Embeddings v2 optimization tips

Amazon Titan Text Embeddings v2 is a scalable model that performs well in large-scale text retrieval tasks. Optimize retrieval by preprocessing input text to remove noise and focus on high-value content, which can improve the efficiency of embedding generation. Use vector compression techniques like quantization or dimensionality reduction to reduce memory and storage costs without significantly impacting retrieval accuracy. When querying, implement hybrid search strategies combining dense vector search and traditional keyword-based search to improve retrieval speed and relevance. For large-scale applications, batch text processing to reduce API calls and enhance throughput. Cache high-demand embeddings to minimize redundant processing and speed up query response times. Regularly update and retrain the embedding model to maintain accuracy with fresh data.

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?

As we wrap up this exciting tutorial, let's take a moment to celebrate everything you’ve just learned about building a cutting-edge RAG system! You’ve journeyed through an impressive landscape where each component plays a vital role in creating a robust and intelligent application. The framework, LangChain, seamlessly integrates all the elements, ensuring that everything works in perfect harmony. You’ve seen firsthand how the Zilliz Cloud vector database enables lightning-fast searches, allowing you to retrieve the most relevant information in the blink of an eye. With the Mistral AI LLM, you’ve unlocked the power of conversational intelligence, enabling your application to interact with users in a way that feels natural and engaging.

And let's not forget about the embedding model, specifically the Amazon Titan Text Embeddings v2, which generates rich semantic representations to enhance your application's understanding of context and meaning. We also shared some optimization tips to ensure your RAG system runs as smoothly as possible and introduced a handy cost calculator to keep your project within budget.

Now it’s your turn! The tools and knowledge you’ve gained here are just the beginning. Dive in, start building, and let your creativity flow. Imagine the innovative applications you can create and the real-world problems you can solve with your newfound skills. So, roll up your sleeves, optimize, and innovate. The world of RAG applications is waiting for you, and it’s time to make your mark!

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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