Build RAG Chatbot with LangChain, Zilliz Cloud, Mistral AI Pixtral, and Cohere embed-english-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 Pixtral: Pixtral is a cutting-edge image generation model designed for high-quality visual content creation. With a focus on artistic style transfer and detail accuracy, it excels in transforming text prompts into vibrant images. Ideal for applications in design, marketing, and creative fields, Pixtral enhances workflows with its versatility and aesthetic precision.
Cohere embed-english-v2.0: This model specializes in generating high-quality embeddings for English text, enhancing semantic understanding in various applications. Its strengths lie in capturing nuanced meanings and context, making it ideal for tasks like semantic search, recommendation systems, and clustering. Efficient and scalable, it's perfect for developers looking to integrate advanced text analysis into their applications.

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 Pixtral

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("pixtral-12b-2409", model_provider="mistralai")

Step 3: Install and Set Up Cohere embed-english-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-english-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 Pixtral optimization tips

Pixtral is optimized for multimodal RAG applications, requiring careful management of both textual and visual data retrieval. Improve retrieval efficiency by using specialized embeddings for different modalities—vector search for text and CLIP-based embeddings for images. Implement a multimodal ranking system to prioritize the most contextually relevant passages and images. Optimize model performance by structuring input prompts effectively, ensuring text and visual information are well-integrated without unnecessary repetition. Fine-tune temperature settings based on response requirements—lower values (0.1–0.2) for accuracy-driven applications, higher values for creative outputs. If deploying at scale, use parallel inference for handling large multimodal datasets efficiently. Streamline inference by leveraging batching and caching strategies, especially when handling frequently queried images and text pairs.

Cohere embed-english-v2.0 optimization tips

Cohere embed-english-v2.0 is a robust English-language embedding model, ideal for fast and accurate semantic retrieval in RAG systems. To optimize performance, preprocess input text by removing redundant information and normalizing formatting. Apply techniques like stemming or lemmatization to reduce the dimensionality of the input without losing meaning. Use high-performance indexing algorithms like HNSW for efficient vector search. When working with large-scale systems, batch embeddings for faster processing and minimize API latency. Consider implementing dimensionality reduction techniques to compress embeddings and reduce storage costs while retaining sufficient accuracy. For large document collections, periodically refresh the embeddings to ensure that they stay relevant to evolving 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?

Wow, what a journey we’ve just taken together into the fascinating world of Retrieval-Augmented Generation (RAG) systems! Throughout this tutorial, we unpacked how to effectively integrate a framework like LangChain with a powerful vector database from Zilliz Cloud, coupled with the remarkable capabilities of Mistral AI's Pixtral LLM and the clever embedding model from Cohere. Each component plays a crucial role: the framework elegantly synchronizes everything, allowing seamless interaction between different parts of your system. The vector database supercharges your application with blazing-fast search capabilities, making it a breeze to access and retrieve information when you need it most.

Meanwhile, the LLM adds that touch of conversational intelligence, transforming your RAG application into a lively, engaging entity that can understand and respond to complex queries. And let’s not forget about the embedding model! It generates rich semantic representations to ensure that your application truly comprehends the nuances of language, making responses not just accurate but deeply relevant. Plus, we’ve wrapped up with some handy optimization tips, and you even have a free cost calculator to help you maximize efficiency and budget.

So, what are you waiting for? Dive in! It’s time to start building, optimizing, and innovating your very own RAG applications. The possibilities are endless, and I can’t wait to see what incredible solutions you come up with. Remember, every great creator started where you are right now – so go out there and make your ideas come to life!

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

We'd Love to Hear What You Think!

We’d love to hear your thoughts! 🌟 Leave your questions or comments below or join our vibrant Milvus Discord community to share your experiences, ask questions, or connect with thousands of AI enthusiasts. Your journey matters to us!

If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖