Build RAG Chatbot with LangChain, OpenSearch, AWS Bedrock Claude 3.5 Haiku, and Cohere embed-multilingual-light-v3.0

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.
OpenSearch: An open-source search and analytics suite derived from Elasticsearch. It offers robust full-text search and real-time analytics, with vector search available as an add-on for similarity-based queries, extending its capabilities to handle high-dimensional data. Since it is just a vector search add-on rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer 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.)
AWS Bedrock Claude 3.5 Haiku: An advanced model from Anthropic, Claude 3.5 is designed for scalable natural language processing within AWS Bedrock. It offers enhanced reasoning and contextual understanding, making it well-suited for applications such as conversational agents, content generation, and data analysis, delivering both speed and accuracy in enterprise settings.
Cohere embed-multilingual-light-v3.0: This model is designed for efficient multilingual text embedding, enabling robust semantic search and similarity tasks across various languages. It excels in scenarios requiring rapid comprehension and matching of diverse linguistic datasets, making it ideal for global applications in content recommendation, categorization, and cross-language information retrieval.

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 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-3-5-haiku-20241022-v1:0", model_provider="bedrock_converse")

Step 3: Install and Set Up Cohere embed-multilingual-light-v3.0

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-multilingual-light-v3.0")

Step 4: Install and Set Up OpenSearch

pip install --upgrade --quiet  opensearch-py langchain-community

from langchain_community.vectorstores import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
    "http://localhost:9200",
    "embeddings",
    embedding_function
)

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.

OpenSearch optimization tips

To optimize OpenSearch in a Retrieval-Augmented Generation (RAG) setup, fine-tune indexing by enabling efficient mappings and reducing unnecessary stored fields. Use HNSW for vector search to speed up similarity queries while balancing recall and latency with appropriate ef_search and ef_construction values. Leverage shard and replica settings to distribute load effectively, and enable caching for frequent queries. Optimize text-based retrieval with BM25 tuning and custom analyzers for better relevance. Regularly monitor cluster health, index size, and query performance using OpenSearch Dashboards and adjust configurations accordingly.

AWS Bedrock Claude 3.5 Haiku optimization tips

Claude 3.5 Haiku on AWS Bedrock is an optimized version of Haiku with improved efficiency and response quality. Enhance performance by using high-precision vector embeddings and filtering irrelevant data before retrieval. Keep prompts brief yet structured to improve clarity and token efficiency. Adjust temperature between 0.1 and 0.3 to balance accuracy and variation. Utilize AWS Bedrock’s low-latency infrastructure to maintain responsiveness in real-time applications. Implement API request batching for large-scale workloads and cache frequent queries to optimize cost and performance. When deploying multiple models, use Haiku for fast responses while reserving larger models for deep analytical tasks.

Cohere embed-multilingual-light-v3.0 optimization tips

Cohere embed-multilingual-light-v3.0 is optimized for faster and more efficient multilingual embeddings. Reduce input data size by filtering out non-essential elements and focusing on key phrases or terms that provide the most value in cross-lingual search scenarios. Implement approximate nearest neighbor search methods, such as FAISS, to ensure that retrieval remains fast and accurate even in large datasets. Use vector compression techniques to save on storage space while maintaining retrieval quality. Employ caching strategies to store commonly used embeddings and reduce unnecessary recomputation. Optimize search performance by organizing embeddings into a hierarchical structure based on languages or topics, enabling quicker retrieval from specific language groups.

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 backbone of your pipeline, seamlessly orchestrating workflows between components. OpenSearch stepped in as your high-performance vector database, handling the storage and lightning-fast retrieval of embedded knowledge. The Cohere embed-multilingual-light-v3.0 model became your multilingual superhero, transforming text into rich numerical representations that capture meaning across languages. Finally, AWS Bedrock’s Claude 3.5 Haiku brought everything to life, generating human-like responses that feel natural and context-aware. Together, these tools form a dynamic quartet: ingesting data, retrieving relevant context, and synthesizing answers with precision.

But this tutorial didn’t just stop at the basics—you also picked up pro tips for optimizing your RAG system! From tweaking chunk sizes for better retrieval to balancing cost and performance with Claude’s scalable pricing, you’re now equipped to fine-tune every layer. And let’s not forget the free RAG cost calculator—a game-changer for budgeting your projects without surprises. Imagine what’s next: building multilingual chatbots, automating research tools, or creating AI assistants that feel like trusted teammates. The foundation is set, the tools are in your hands, and the possibilities are endless. So go ahead—experiment, iterate, and innovate. Your first RAG application is just a few lines of code away. Let’s turn those ideas into reality! 🚀

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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