Build RAG Chatbot with LangChain, Faiss, Mistral AI Mistral Small, 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.
Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want a much more scalable solution or hate to manage your own infrastructure, 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.)
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.
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 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 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 Faiss

pip install -qU langchain-community

from langchain_community.vectorstores import FAISS

vector_store = FAISS(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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

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.

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?

What have you learned? Throughout this tutorial, you've taken some amazing steps into the heart of building a cutting-edge Retrieval-Augmented Generation (RAG) system! By leveraging a dynamic framework like LangChain, you’ve seen firsthand how it seamlessly ties all the components together, ensuring a fluid workflow that keeps your applications efficient and effective. The integration of a vector database, specifically Faiss, empowers fast and powerful searches, allowing you to retrieve relevant information in the blink of an eye, enhancing the overall performance of your RAG applications.

And let’s not forget about the magic brought by the LLMs, such as Mistral AI's Mistral Small! You've harnessed their conversational intelligence to create engaging and intuitive interactions that can truly respond to user needs. Meanwhile, the embedding model, like Amazon’s Titan Text Embeddings v2, has enabled you to generate rich semantic representations, giving depth to the data you work with and allowing for nuanced understanding and responses.

With optimization tips and even a handy cost calculator in your toolkit, you now have the knowledge to not just build, but refine and scale your projects. So don’t stop here! Dive in and start innovating your very own RAG applications. The possibilities are endless, and the only limit is your imagination. Get coding, experimenting, and let your creativity shine!

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! 💖