Build RAG Chatbot with LangChain, Faiss, OpenAI GPT-4o mini, and OpenAI text-embedding-3-large

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.)
OpenAI GPT-4o mini: This model is a compact version of the advanced GPT-4 architecture, designed for efficiency and lower resource consumption. It delivers strong performance in natural language understanding and generation tasks, making it ideal for chatbot implementations, content creation, and interactive applications where responsiveness and cost-effectiveness are crucial.
text-embedding-3-large: OpenAI's text embedding model, generating embeddings with 1536 dimensions, designed for tasks like semantic search and similarity matching.

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 OpenAI GPT-4o mini

pip install -qU "langchain[openai]"

import getpass
import os

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

from langchain.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

Step 3: Install and Set Up OpenAI text-embedding-3-large

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

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.

OpenAI GPT-4o mini optimization tips

GPT-4o mini is a smaller and more cost-effective variant of GPT-4o, making it ideal for low-latency RAG applications. Optimize retrieval by limiting the number of retrieved documents to only the most relevant, ensuring the model does not get overloaded with unnecessary context. Structure prompts effectively by using bullet points or numbered lists to enhance clarity and focus. Keep temperature at 0.1–0.2 for factual responses, adjusting top-p and top-k values to refine output precision. Implement caching for frequently accessed queries to minimize API calls and reduce costs. For high-throughput applications, batch process multiple queries to optimize API efficiency. Stream responses to improve interactivity in real-time applications. If using GPT-4o mini in a multi-model pipeline, consider using it for simpler tasks while reserving larger models for complex reasoning.

OpenAI text-embedding-3-large optimization tips

OpenAI text-embedding-3-large is a high-capacity embedding model designed for precise and rich semantic representation, making it ideal for RAG systems with complex document retrieval needs. Optimize efficiency by preprocessing and normalizing text to reduce noise before embedding generation. Use dimensionality reduction techniques, such as PCA, if storage or computational limits become a concern. When querying, leverage HNSW-based approximate nearest neighbor (ANN) search to accelerate retrieval while maintaining accuracy. Batch process embedding requests to reduce latency and optimize resource utilization. Implement re-ranking models to further refine top results based on query context. Regularly update the embedding store with newly ingested data to maintain retrieval relevance.

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? Congratulations on completing this tutorial! You’ve just taken significant steps toward mastering the integration of a cutting-edge Retrieval-Augmented Generation (RAG) system. By exploring the combined power of LangChain as your foundational framework, you’ve seen how it elegantly ties all components together. The vector database, specifically using Faiss, empowers you to perform lightning-fast searches, ensuring that your application retrieves relevant information quickly and efficiently. With OpenAI's GPT-4o mini leading the charge in conversational intelligence, you can now develop systems that interact naturally and engagingly with users. And let's not forget the embedding model, OpenAI’s text-embedding-3-large, which gives your application the ability to generate rich semantic representations for deeper understanding and context.

Throughout the tutorial, we’ve also shared optimization tips and even provided you with a free cost calculator to help you make informed decisions as you scale your projects. The knowledge you've gained here is just the beginning! Imagine the limitless possibilities that await as you dive deeper into building, optimizing, and innovating your own RAG applications. So, roll up your sleeves, fire up your coding environment, and start creating! The future is yours to shape, and we can’t wait to see what you come up with. Happy coding!

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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