Build RAG Chatbot with LangChain, Faiss, OpenAI GPT-4, and Ollama snowflake-arctic-embed

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-4: GPT-4 is OpenAI's advanced language model, designed for comprehensive understanding and context-aware text generation. It excels in creative writing, complex problem-solving, and nuanced conversation, making it suitable for applications in content creation, tutoring, and interactive AI. Its robust capabilities enable it to handle a wide range of topics with depth and coherence.
Ollama Snowflake-Arctic-Embed: This model specializes in generating high-dimensional embeddings for structured and unstructured data, leveraging advanced deep learning techniques. Its strengths include efficient handling of large datasets and producing contextual representations, making it ideal for applications in recommendation systems, semantic search, and personalized content delivery.

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-4

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-4", model_provider="openai")

Step 3: Install and Set Up Ollama snowflake-arctic-embed

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="snowflake-arctic-embed")

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-4 optimization tips

GPT-4 is a powerful model for RAG applications, but optimizing retrieval and prompt efficiency is key to reducing costs and improving response quality. Use embedding-based retrieval to ensure highly relevant context is included while avoiding unnecessary token usage. Structure prompts concisely, presenting retrieved documents in a clear, ranked format to guide the model’s focus. Fine-tune temperature (0.1–0.3) for fact-based tasks and adjust top-p and top-k sampling to control response variability. Implement caching for frequently queried information to reduce redundant API calls and improve latency. If using GPT-4 in high-traffic applications, batch requests to minimize overhead and optimize throughput. Leverage OpenAI’s function-calling capabilities to structure responses programmatically and avoid excessive hallucinations. Use response streaming for real-time applications to improve perceived performance while maintaining efficient token usage.

Ollama snowflake-arctic-embed optimization tips

To optimize the Ollama snowflake-arctic-embed component in your Retrieval-Augmented Generation setup, ensure that you fine-tune the embedding model on domain-specific data to improve relevance and accuracy. Utilize a caching mechanism for frequently accessed embeddings to reduce computation time. Experiment with different embedding dimensions to balance performance and resource usage, and implement vector quantization techniques to save memory space without significantly impacting quality. Additionally, regularly monitor the performance metrics and adjust your hyperparameters accordingly to achieve the best outcomes in your retrieval tasks.

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?

By diving into this tutorial, you’ve unlocked the power of combining cutting-edge tools to build a sophisticated RAG system from scratch! You learned how LangChain acts as the glue, seamlessly orchestrating the entire pipeline—connecting your data sources, managing interactions with the vector database, and coordinating the flow between embeddings and the LLM. Faiss stepped in as your vector database hero, enabling lightning-fast similarity searches to retrieve the most relevant context for your queries. Then, Ollama’s Snowflake-Arctic-Embed model transformed raw text into rich numerical representations, ensuring your data is primed for retrieval. Finally, OpenAI’s GPT-4 took center stage, synthesizing retrieved information into coherent, human-like responses that feel almost magical. Along the way, you discovered optimization tricks like chunking strategies and indexing best practices to boost efficiency, and even got a sneak peek at tools like the free RAG cost calculator to keep your projects budget-friendly and scalable.

Now you’re armed with the knowledge to build intelligent systems that don’t just answer questions—they understand context, adapt to user needs, and deliver real value. Whether you’re creating chatbots, research assistants, or custom knowledge bases, you’ve seen firsthand how these components work in harmony to turn raw data into actionable insights. The best part? This is just the beginning. Experiment with different models, fine-tune your retrieval logic, and let your creativity run wild. The world of RAG is evolving rapidly, and you’re now equipped to ride the wave. So fire up your IDE, tweak those parameters, and start building something amazing—your next breakthrough is waiting! 🚀

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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