Build RAG Chatbot with LangChain, pgvector, AWS Bedrock Claude 3.5 Haiku, and NVIDIA llama-3.2-nv-embedqa-1b-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.
Pgvector: an open-source extension for PostgreSQL that enables efficient storage and querying of high-dimensional vector data, essential for machine learning and AI applications. Designed to handle embeddings, it supports fast approximate nearest neighbor (ANN) searches using algorithms like HNSW and IVFFlat. Since it is just a vector search add-on to traditional search 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.
NVIDIA llama-3.2-nv-embedqa-1b-v2: This AI model is designed for advanced question-answering tasks, leveraging NVIDIA's LLaMA architecture. It excels in embedding-based question retrieval and provides high accuracy in understanding context. Ideal for knowledge-intensive applications, it enhances customer support, educational tools, and research assistance.

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 NVIDIA llama-3.2-nv-embedqa-1b-v2

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

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

from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings

embeddings = NVIDIAEmbeddings(model="nvidia/llama-3.2-nv-embedqa-1b-v2")

Step 4: Install and Set Up pgvector

pip install -qU langchain-postgres

from langchain_postgres import PGVector

vector_store = PGVector(
    embeddings=embeddings,
    collection_name="my_docs",
    connection="postgresql+psycopg://...",
)

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.

pgvector optimization tips

To optimize pgvector in a Retrieval-Augmented Generation (RAG) setup, consider indexing your vectors using GiST or IVFFlat to significantly speed up search queries and improve retrieval performance. Make sure to leverage parallelization for query execution, allowing multiple queries to be processed simultaneously, especially for large datasets. Optimize memory usage by tuning the vector storage size and using compressed embeddings where possible. To further enhance query speed, implement pre-filtering techniques to narrow down search space before querying. Regularly rebuild indexes to ensure they are up to date with any new data. Fine-tune vectorization models to reduce dimensionality without sacrificing accuracy, thus improving both storage efficiency and retrieval times. Finally, manage resource allocation carefully, utilizing horizontal scaling for larger datasets and offloading intensive operations to dedicated processing units to maintain responsiveness during high-traffic periods.

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.

NVIDIA llama-3.2-nv-embedqa-1b-v2 optimization tips

To optimize the performance of the NVIDIA llama-3.2-nv-embedqa-1b-v2 in a Retrieval-Augmented Generation setup, consider employing mixed precision training to enhance computational efficiency while maintaining model accuracy. Utilize efficient indexing and retrieval methods, such as FAISS, to quickly access relevant documents, minimizing response time. Tune the hyperparameters, especially the learning rate and batch size, based on validation metrics to improve convergence speed. Implement caching strategies to store frequently accessed data and results for faster retrieval. Regularly profile the model to identify bottlenecks and make necessary adjustments. Finally, leverage NVIDIA’s TensorRT for optimized inference, ensuring that your setup benefits from accelerated performance on compatible hardware.

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 robust RAG system from scratch! You learned how LangChain acts as the glue, orchestrating the entire pipeline with its flexible framework—connecting data sources, managing prompts, and seamlessly integrating components. The pgvector database became your go-to for storing and querying vector embeddings at scale, leveraging PostgreSQL’s reliability to handle similarity searches with lightning speed. With AWS Bedrock’s Claude 3.5 Haiku, you tapped into a lightweight yet incredibly capable LLM that generates crisp, context-aware answers, while NVIDIA’s blazing-fast llama-3.2-nv-embedqa-1b-v2 embedding model transformed raw text into rich numerical representations, ensuring your RAG system understands nuances in your data. Together, these tools transformed static data into dynamic, intelligent responses, empowering you to build applications that feel almost magical in their ability to retrieve and generate insights.

But this isn’t just about stitching tools together—you also picked up pro tips for optimization, like tuning chunk sizes for embeddings or balancing cost and performance with Claude’s API. The free RAG cost calculator you explored is a game-changer, helping you forecast expenses and scale smarter. Now, imagine what’s next! You’re not just following steps; you’re equipped to innovate—whether enhancing chatbots, building personalized search engines, or crafting domain-specific AI assistants. The foundation is yours to build upon. So fire up your IDE, experiment with new datasets, tweak those parameters, and watch your ideas come alive. The future of intelligent applications is in your hands, and you’ve got everything you need to shape it. Let’s go build something amazing! 🚀

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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