Build RAG Chatbot with LangChain, Milvus, Mistral AI Pixtral Large, and Cohere embed-english-light-v3.0

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.
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.
Mistral AI Pixtral Large: This advanced model is engineered for high-quality image and text generation. With its robust architecture, it excels in tasks such as creative content generation, image enhancement, and multimedia integration. Ideal for applications in marketing, design, and entertainment, Pixtral Large delivers impressive results while maintaining a user-friendly experience.
Cohere embed-english-light-v3.0: This model specializes in generating high-quality embeddings for English text, optimized for efficiency and low resource usage. It excels in tasks such as semantic search, clustering, and recommendation systems. Ideal for developers needing fast and scalable text representation capabilities in resource-constrained environments.

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 Large

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-large-latest", model_provider="mistralai")

Step 3: Install and Set Up Cohere embed-english-light-v3.0

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-light-v3.0")

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.

Mistral AI Pixtral Large optimization tips

Pixtral Large is a high-performance multimodal model optimized for advanced reasoning across text and image inputs. Optimize retrieval by ensuring efficient document and image indexing, leveraging hybrid search to find the most relevant multimodal content. Use structured context formatting to provide clear relationships between retrieved text and images, preventing ambiguity in model responses. Adjust inference settings by dynamically controlling retrieval depth—fetching broader context for complex multimodal queries and focusing on narrower, high-precision retrieval for simpler ones. Optimize token efficiency by filtering out redundant data before passing it to the model, ensuring inference speed remains high. Use caching for frequently requested multimodal queries to reduce processing overhead. If deploying Pixtral Large in production, distribute inference across multiple nodes to handle large-scale workloads effectively while maintaining latency targets.

Cohere embed-english-light-v3.0 optimization tips

Cohere embed-english-light-v3.0 is optimized for lighter, faster embeddings in English language tasks. To maximize efficiency, preprocess input text by stripping out unnecessary words and standardizing formatting, which reduces computational load and speeds up embedding generation. Leverage vector compression techniques to reduce storage costs and improve retrieval performance. When managing large volumes of data, implement efficient search strategies like HNSW or product quantization to reduce time spent querying. Batch embedding operations to process multiple texts at once, minimizing the overhead from frequent API calls. Periodically update the embeddings to capture any changes in the dataset, ensuring freshness and accuracy in search results while optimizing storage.

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 an incredible journey you've just embarked on! Throughout this tutorial, you've seen how the integration of a robust framework like LangChain, a powerful vector database like Milvus, a cutting-edge LLM like Mistral AI Pixtral Large, and a rich embedding model such as Cohere's embed-english-light-v3.0 can come together to form a truly revolutionary RAG (Retrieval-Augmented Generation) system. Each component brings something unique to the table: LangChain serves as the mastermind that harmonizes the interactions between the models; Milvus enables lightning-fast searches, ensuring that your queries are met with the speed they demand; the Mistral AI model leverages the capabilities of conversational intelligence, enabling dynamic and fluid dialogues; and the embedding model creates profound semantic representations that enhance understanding and relevance.

And don't forget the invaluable optimization tips we shared, plus that handy free cost calculator! These tools are fantastic for ensuring that your applications not only perform well but also stay financially viable. You've laid down a solid foundation, and the sky's the limit! So why wait? Start building, optimizing, and innovating your own RAG applications today! Dive into the vast possibilities ahead, experiment, and let your creativity lead the way. The future is bright, and your journey is just beginning—let's go make some magic 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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