Build RAG Chatbot with LangChain, Faiss, Google Vertex AI Claude 3.7 Sonnet, and IBM all-minilm-l6-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.)
Google Vertex AI Claude 3.7 Sonnet: Google Vertex AI Claude 3.7 Sonnet: Google’s Vertex AI integrates Claude 3.7 Sonnet, bringing advanced NLP capabilities to the Google Cloud ecosystem. It supports the creation and deployment of secure, customized language models with high precision in natural language understanding and generation. Ideal for enterprises looking to leverage AI for chatbots, document processing, and AI-assisted customer interactions with a focus on scalability and cloud-native solutions.
IBM all-minilm-l6-v2: This model is a compact, efficient transformer-based language representation model optimized for tasks requiring fast inferencing. It excels in natural language understanding tasks such as sentiment analysis and information retrieval, making it ideal for applications in chatbots, search engines, and data annotation.

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 Google Vertex AI Claude 3.7 Sonnet

pip install -qU "langchain[google-vertexai]"

# Ensure your VertexAI credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-7-sonnet@20250219", model_provider="google_vertexai")

Step 3: Install and Set Up IBM all-minilm-l6-v2

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="sentence-transformers/all-minilm-l6-v2",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

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.

Google Vertex AI Claude 3.7 Sonnet Optimization Tips

In a Retrieval-Augmented Generation (RAG) setup with Google Vertex AI Claude 3.7 Sonnet, focus on fine-tuning the model to your domain-specific data for enhanced response accuracy. Use Vertex AI’s integrated tools to scale document retrieval, ensuring that your knowledge base is well-structured and efficiently indexed. Adjust retrieval parameters such as embedding vectors and similarity thresholds to improve the relevance of documents pulled into the generation process. Monitor response times and reduce latency by optimizing batch processing and utilizing Google Cloud’s low-latency storage. Additionally, regularly test and adjust hyperparameters like temperature and top-p to balance response creativity with factual correctness.

IBM all-minilm-l6-v2 optimization tips

To optimize the performance of IBM all-minilm-l6-v2 in a Retrieval-Augmented Generation (RAG) setup, consider implementing streamlined query preprocessing to remove stop words and normalize text, ensuring that input queries are concise and relevant. Layering caching strategies on frequently retrieved results can significantly reduce latency, while fine-tuning the model with domain-specific data enhances relevance and accuracy. Additionally, experiment with batch processing during inference to leverage parallelization, and monitor and adjust hyperparameters like learning rates and maximum token counts to refine model responses. Lastly, ensure that your retrieval system is seamlessly integrated with the generation process to maintain context and coherence in generated outputs.

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 magic of building a powerful RAG system from the ground up! You’ve seen how LangChain acts as the glue, seamlessly connecting every component in your pipeline. Its framework orchestrates the flow of data, making it easy to plug in tools like Faiss—a lightning-fast vector database that stores and retrieves embeddings with incredible efficiency. Speaking of embeddings, you learned how IBM’s all-minilm-l6-v2 model transforms text into compact numerical representations, capturing the essence of your data in a way machines understand. Then, Google Vertex AI’s Claude 3.7 Sonnet steps in as the brainy LLM, synthesizing retrieved information into human-like responses that feel natural and relevant. Together, these pieces form a dynamic system that answers questions intelligently while staying grounded in your custom data sources. You even picked up pro tips for optimizing costs and performance, like tweaking chunk sizes and leveraging Faiss’s indexing tricks—plus that handy free RAG cost calculator to keep your projects budget-friendly!

Now, imagine what you can build next! Whether you’re creating chatbots that truly understand your business, crafting AI research assistants, or designing tools to democratize knowledge, you’ve got the skills to bring ideas to life. The tutorial showed you the blueprint, but the real adventure starts now. Experiment with different datasets, fine-tune retrieval strategies, or swap in other models to see how they shine. Remember, every tweak and iteration gets you closer to smarter, faster, and more creative AI solutions. So fire up your code editor, embrace the trial-and-error joy of building, and let your curiosity lead the way. The future of intelligent applications is yours to shape—go make something awesome! 🚀

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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