Build RAG Chatbot with LangChain, pgvector, AWS Bedrock Claude 3 Opus, and Amazon Titan Text Embeddings 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 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.
Amazon Titan Text Embeddings v2: This model generates high-quality text embeddings, enabling nuanced semantic understanding and similarity comparisons. It boasts enhanced performance and scalability, making it suitable for tasks such as information retrieval, recommendation systems, and sentiment analysis. Ideal for applications needing robust and efficient language representation at scale.

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 Amazon Titan Text Embeddings v2

pip install -qU langchain-aws

from langchain_aws import BedrockEmbeddings

embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0")

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 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.

Amazon Titan Text Embeddings v2 optimization tips

Amazon Titan Text Embeddings v2 is a scalable model that performs well in large-scale text retrieval tasks. Optimize retrieval by preprocessing input text to remove noise and focus on high-value content, which can improve the efficiency of embedding generation. Use vector compression techniques like quantization or dimensionality reduction to reduce memory and storage costs without significantly impacting retrieval accuracy. When querying, implement hybrid search strategies combining dense vector search and traditional keyword-based search to improve retrieval speed and relevance. For large-scale applications, batch text processing to reduce API calls and enhance throughput. Cache high-demand embeddings to minimize redundant processing and speed up query response times. Regularly update and retrain the embedding model to maintain accuracy with fresh data.

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 magic of combining cutting-edge tools to build a Retrieval-Augmented Generation (RAG) system from the ground up! You’ve seen how LangChain acts as the glue, orchestrating workflows to seamlessly connect your data pipeline with powerful language models. With pgvector, you’ve harnessed the simplicity of PostgreSQL to store and query vector embeddings at scale, turning unstructured data into searchable knowledge. The integration of AWS Bedrock’s Claude 3 Opus showcased the raw power of modern LLMs—transforming retrieved context into precise, human-like responses—while Amazon Titan Text Embeddings v2 turned your text into rich numerical representations, ensuring your system understands semantic relationships deeply. Together, these tools form a dynamic pipeline where retrieval and generation work hand-in-hand to deliver accurate, context-aware answers, whether you’re building chatbots, research assistants, or intelligent search engines.

But this tutorial didn’t stop at the basics! You also picked up pro tips for optimizing performance, like balancing chunk sizes for embeddings and tuning retrieval parameters to reduce latency. And with the free RAG cost calculator, you’re now equipped to estimate expenses upfront, making smart choices about scalability and resource allocation. The best part? You’ve seen firsthand how accessible advanced AI workflows can be when using the right tools. Now it’s your turn to take these concepts further—experiment with different datasets, fine-tune models for niche domains, or even integrate multimedia for multimodal RAG. The future of intelligent applications is in your hands. Go build something groundbreaking, iterate fearlessly, and let your creativity shape what’s next in AI. The possibilities are endless, and you’re ready to seize them! 🚀

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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