Build RAG Chatbot with LangChain, Zilliz Cloud, AWS Bedrock Claude 3.5 Sonnet, and Ollama nomic-embed-text

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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It offers a free tier supporting up to 1 million vectors.
AWS Bedrock Claude 3.5 Sonnet: This model combines the advanced language processing abilities of Claude 3.5 with AWS's scalable infrastructure, offering enhanced performance for complex text generation tasks. Its strengths lie in versatility and resilience, making it ideal for applications in creative writing, customer support automation, and interactive content generation.
Ollama nomic-embed-text: This model specializes in generating high-quality text embeddings, designed to enhance semantic understanding in various NLP tasks. Its strengths lie in contextual representation and scalability, making it suitable for applications like semantic search, recommendation systems, and clustering. Ideal for developers looking to integrate profound text analysis into their projects.

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 AWS Bedrock Claude 3.5 Sonnet

pip install -qU "langchain[aws]"

# Ensure your AWS credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("anthropic.claude-3-5-sonnet-20241022-v2:0", model_provider="bedrock_converse")

Step 3: Install and Set Up Ollama nomic-embed-text

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="nomic-embed-text")

Step 4: Install and Set Up Zilliz Cloud

pip install -qU langchain-milvus

from langchain_milvus import Zilliz

vector_store = Zilliz(
    embedding_function=embeddings,
    connection_args={
        "uri": ZILLIZ_CLOUD_URI,
        "token": ZILLIZ_CLOUD_TOKEN,
    },
)

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

AWS Bedrock Claude 3.5 Sonnet optimization tips

Claude 3.5 Sonnet in AWS Bedrock delivers an improved balance of efficiency and reasoning. Optimize retrieval by incorporating reranking techniques that prioritize the most contextually relevant documents. Keep prompts structured and avoid unnecessary details to prevent context window saturation. Use temperature values between 0.1 and 0.3 for factual responses, fine-tuning top-p and top-k as needed. Cache frequently accessed data to reduce redundant API calls and lower operational costs. Leverage AWS Bedrock’s elastic scaling to handle demand spikes while maintaining performance. If used with Claude 3.5 Opus, delegate more complex reasoning tasks to Opus while Sonnet handles general-purpose queries.

Ollama nomic-embed-text optimization tips

Ollama nomic-embed-text is designed for robust text embedding generation, making it essential to optimize how embeddings are stored and queried in a RAG pipeline. Preprocess input text by stripping unnecessary metadata and normalizing case to maintain consistency. Choose an optimized vector indexing strategy, such as IVF-PQ for balanced speed and accuracy, depending on dataset size. Use approximate nearest neighbor search to accelerate retrieval while maintaining a high recall rate. Cache commonly accessed embeddings to avoid redundant computations. If embeddings are used for long-term retrieval tasks, periodically refresh and retrain on new data to prevent model drift. Optimize database queries to quickly retrieve relevant embeddings and minimize I/O bottlenecks.

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?

Throughout this tutorial, you've embarked on an exciting journey to build a cutting-edge Retrieval-Augmented Generation (RAG) system, and wow, what a toolkit you now have at your fingertips! By seamlessly integrating LangChain as the framework, you've created a cohesive backbone that unites all the components. This powerful framework elegantly orchestrates the synergy between the embedding model and the other technologies, making everything work together like a charm. With Zilliz Cloud providing a robust vector database, you can perform lightning-fast searches that allow your application to retrieve relevant data in the blink of an eye, making your RAG system not just functional, but truly impressive.

On top of that, leveraging AWS Bedrock's Claude 3.5 Sonnet for conversational intelligence transforms your interactions, allowing you to engage users in a way that's not only efficient but also friendly and relatable. With the nomic-embed-text model generating rich semantic representations, you've unlocked a new level of understanding for your application, paving the way for smarter and more intuitive responses.

Don't forget the optimization tips you picked up along the way, and remember to use the free cost calculator to manage your resources effectively. Now it’s your turn to take the reins! Dive in, experiment, iterate, and innovate as you build your own RAG applications. The possibilities are limitless, and who knows the amazing solutions you’ll create? So, roll up your sleeves and let your creativity soar! You've got this!

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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