Build RAG Chatbot with LangChain, Zilliz Cloud, Mistral AI Mistral Small, and Cohere embed-multilingual-v2.0

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 Small: This lightweight transformer model offers competitive performance with a reduced memory footprint, making it suitable for resource-constrained environments. It excels in tasks like text generation and classification, providing efficiency without sacrificing quality. Ideal for applications needing quick responses and low latency, such as chatbots and real-time analytics.
Cohere embed-multilingual-v2.0: This model specializes in generating high-quality multilingual embeddings, enabling effective cross-lingual understanding and retrieval. Its strengths lie in capturing semantic relationships in diverse languages, making it suitable for applications such as multilingual search, recommendation systems, and global content analysis where language diversity is a critical factor.

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 Small

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("mistral-small-latest", model_provider="mistralai")

Step 3: Install and Set Up Cohere embed-multilingual-v2.0

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-multilingual-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 Small optimization tips

Mistral Small is a compact, efficient model best suited for low-latency and cost-effective RAG applications. Optimize token usage by ensuring retrieval pipelines return highly targeted and concise context, reducing unnecessary model computation. Use lightweight prompt compression techniques to streamline input formatting and avoid redundant details. Adjust temperature to 0.1–0.2 for factual consistency while keeping sampling techniques minimal to prevent response variability. For real-time applications, implement caching of common queries to further improve speed. If deploying at scale, leverage quantized versions of the model (e.g., 4-bit or 8-bit precision) to reduce memory footprint. Use batch inference techniques to maximize throughput while minimizing API call overhead.

Cohere embed-multilingual-v2.0 optimization tips

Cohere embed-multilingual-v2.0 supports a variety of languages, making it ideal for cross-lingual RAG setups. To optimize efficiency, preprocess text to remove language-specific noise and handle encoding issues, ensuring clean input for embedding generation. Implement efficient ANN algorithms, like FAISS with hierarchical indexing, to support fast retrieval across multilingual datasets. Compress embeddings using techniques such as product quantization or HNSW to optimize storage and speed. Use language detection models to route queries to the appropriate language-specific embeddings, minimizing unnecessary computation. Batch embedding operations and take advantage of parallel processing to handle large amounts of multilingual data efficiently. Regularly update embeddings to ensure the model reflects any language shifts or evolving trends.

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 in this thrilling journey of building your own cutting-edge RAG system? You’ve delved into the intricate dance of technology, exploring how the specialized framework, like LangChain, elegantly orchestrates all the elements of your RAG application. This framework acts as the backbone, seamlessly connecting the dots between each powerful tool you’ve integrated.

The vector database, powered by Zilliz Cloud, has shown you how it enables super-fast searches that can handle vast amounts of data with incredible efficiency. You’ve experienced firsthand how quick access to relevant information can transform your applications into lightning-fast, user-friendly experiences.

Moreover, the LLM, particularly from Mistral AI’s Mistral Small, has demonstrated the power of conversational intelligence, enabling your system to engage with users in an intuitive and meaningful way. Coupled with the embedding model, such as Cohere's embed-multilingual-v2.0, you learned how to transform raw data into rich semantic representations, turning your application into a smart, context-aware powerhouse that truly understands the nuances of language.

Plus, let's not forget the handy optimization tips and the free cost calculator we introduced to enhance your efficiency and budgeting! Now, it's time for you to take all this knowledge and start building, optimizing, and innovating your very own RAG applications. The opportunities are boundless, and you’re equipped with the right tools and insights to bring your vision to life! So, roll up your sleeves, dive into your next project, and let your creativity shine. The future of intelligent applications is at your fingertips!

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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