Build RAG Chatbot with LangChain, Zilliz Cloud, AWS Bedrock Claude 3 Opus, and voyage-code-3

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 Opus: This model from the Claude 3 family focuses on high-performance natural language understanding and generation. It’s designed for applications that require nuanced text creation, detailed analysis, and advanced conversational capabilities. Ideal for enterprises looking to enhance customer interactions, content creation, and complex query handling while optimizing scalability.
Voyage Code 3: This model is designed for advanced code generation and understanding, offering robust capabilities in programming assistance and automated coding tasks. Its strength lies in contextual comprehension and precise syntax generation, making it ideal for developers seeking to enhance productivity through efficient coding solutions and debugging support.

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 Opus

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-opus-20240229-v1:0", model_provider="bedrock_converse")

Step 3: Install and Set Up voyage-code-3

pip install -qU langchain-voyageai

import getpass
import os

if not os.environ.get("VOYAGE_API_KEY"):
  os.environ["VOYAGE_API_KEY"] = getpass.getpass("Enter API key for Voyage AI: ")

from langchain-voyageai import VoyageAIEmbeddings

embeddings = VoyageAIEmbeddings(model="voyage-code-3")

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 Opus optimization tips

Claude 3 Opus on AWS Bedrock is a high-capacity model suitable for complex RAG applications requiring deep reasoning. Optimize retrieval by using multi-step ranking strategies, ensuring only the most relevant documents are included in context. Keep prompts concise but comprehensive, structuring retrieved information in a logical order to guide the model effectively. Use temperature settings between 0.1 and 0.2 for fact-based tasks and slightly higher values for more creative responses. To manage API costs and latency, implement response caching and query batching for high-traffic applications. Leverage AWS Bedrock’s auto-scaling features to handle fluctuating workloads without compromising response time. If Opus is part of a multi-tiered system, use it selectively for high-value queries requiring deep analysis while offloading simpler tasks to smaller models.

voyage-code-3 optimization tips

voyage-code-3 is designed for code generation and understanding, making structured input crucial for optimal RAG performance. Improve retrieval by using high-quality code embeddings and semantic search techniques to surface the most relevant code snippets. Keep prompts well-organized, clearly specifying requirements, constraints, and relevant context. Adjust temperature settings (0.1–0.3) to control creativity versus precision in generated code. Implement response caching for frequently accessed functions and common coding patterns to reduce processing time. Utilize batch processing and token-efficient formatting to optimize resource usage. If integrating multiple models, use voyage-code-3 for generating complex code solutions while reserving smaller models for lightweight code completions and explanations.

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?

What a journey it’s been! In this tutorial, you've not only learned how to integrate some of the most cutting-edge components—LangChain, Zilliz Cloud, AWS Bedrock Claude 3 Opus, and an embedding model—but you've also witnessed firsthand how they can work together to create a robust RAG (Retrieval-Augmentation Generation) system. Each piece plays a pivotal role in this ecosystem. The LangChain framework ties everything together seamlessly, offering you a flexible foundation for building complex applications. With Zilliz Cloud’s advanced vector database, you now have the ability to conduct lightning-fast searches, drastically improving the retrieval process. Meanwhile, AWS Bedrock Claude 3 Opus allows for sophisticated conversational interactions, powering your application with impressive conversational intelligence. And let’s not forget the embedding model, which generates rich semantic representations to ensure your system understands the nuances of language better than ever!

You’ve also explored optimization tips to make your system even more efficient and have access to a handy free cost calculator to help you plan your resources wisely. Now it's your turn! Dive into this exciting world, unleash your creativity, and start building, optimizing, and innovating your own RAG applications. The possibilities are endless, and the skills you've gained will empower you to tackle new challenges and discover innovative solutions. Let's get to building something amazing together—your journey in AI has just begun!

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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