Build RAG Chatbot with LangChain, LangChain vector store, AWS Bedrock Claude 3 Haiku, 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.
LangChain in-memory vector store: an in-memory, ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. It is intended for demos and does not yet support ids or deletion. (If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand offers a free tier supporting up to 1 million vectors.)
AWS Bedrock Claude 3 Haiku: This model harnesses the power of Claude 3 within AWS's Bedrock service, facilitating scalable AI solutions. It excels in natural language understanding and generation tasks, making it ideal for customer support, content creation, and conversational agents. Its integration with cloud infrastructure ensures flexibility and ease of deployment for enterprises.
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 AWS Bedrock Claude 3 Haiku

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-haiku-20240307-v1:0", model_provider="bedrock_converse")

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 LangChain vector store

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(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.

LangChain in-memory vector store optimization tips

LangChain in-memory vector store is just an ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. It has very limited features and is only intended for demos. If you plan to build a functional or even production-level solution, 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.)

AWS Bedrock Claude 3 Haiku optimization tips

Claude 3 Haiku on AWS Bedrock is designed for speed and efficiency, making it ideal for real-time RAG applications. Optimize retrieval by prioritizing high-precision embeddings and filtering out low-relevance documents before passing them to the model. Keep prompts structured with clear formatting to help the model focus on the most critical context. Use temperature settings between 0.1 and 0.3 for factual accuracy and adjust top-p to balance diversity. For latency-sensitive applications, leverage AWS Bedrock’s scaling capabilities to ensure smooth performance under heavy loads. Implement caching for frequently accessed queries to reduce API calls and improve response times. If used in a multi-model setup, reserve Haiku for fast preliminary filtering or summarization before escalating complex queries to larger models.

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 now, you’ve seen how the magic of LangChain acts as the glue that ties everything together, orchestrating a seamless RAG pipeline where each component shines in its role. You learned how LangChain’s vector store becomes the backbone of your system, efficiently storing and retrieving context-rich data, while Ollama’s snowflake-arctic-embed model transforms text into meaningful embeddings, capturing semantic nuances that make your AI’s understanding feel almost human. Pairing this with AWS Bedrock’s Claude 3 Haiku—a powerhouse LLM—you saw firsthand how dynamic, context-aware responses come to life, blending retrieved knowledge with creative generation. Together, these tools form a symphony of capabilities, turning raw data into actionable insights and natural conversations. And let’s not forget those optimization tricks! From fine-tuning chunk sizes to balancing speed and accuracy, you’re now equipped to tweak your system for peak performance—and the free RAG cost calculator you explored ensures you can scale smartly without breaking the bank.

But this is just the beginning. Imagine the possibilities: building chatbots that truly understand niche domains, crafting research assistants that synthesize information in seconds, or even prototyping tools that bridge gaps in knowledge access. You’ve got the blueprint—now it’s time to experiment, iterate, and innovate. Tinker with different datasets, play with hybrid retrieval strategies, or layer in domain-specific fine-tuning. The tools are in your hands, and the future of intelligent applications is yours to shape. So go ahead—fire up your IDE, spin up those models, and start building. Your next breakthrough is waiting, and the world needs what you’ll create. Let’s get 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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