Build RAG Chatbot with Llamaindex, Pgvector, Anthropic Claude 3 Sonnet, and AmazonBedrock titan-embed-text-v1

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:

Llamaindex: a data framework that connects large language models (LLMs) with various data sources, enabling efficient retrieval-augmented generation (RAG). It helps structure, index, and query private or external data, optimizing LLM applications for search, chatbots, and analytics.
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.)
Anthropic Claude 3 Sonnet: A versatile AI model optimized for complex reasoning, multilingual tasks, and processing long-context inputs. It balances high performance with cost-efficiency, ideal for enterprise-scale applications like data analysis, real-time customer support, content creation, and research tasks requiring accuracy and scalability across diverse industries.
AmazonBedrock Titan-Embed-Text-v1: A high-performance embedding model designed to convert text into dense vector representations, enabling semantic search, clustering, and retrieval tasks. Strengths include scalability, multilingual support, and robust accuracy. Ideal for enterprise applications like recommendation systems, document similarity analysis, and AI-driven search engines within AWS environments.

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 Llamaindex

pip install llama-index

Step 2: Install and Set Up Anthropic Claude 3 Sonnet

%pip install llama-index-llms-anthropic

from llama_index.llms.anthropic import Anthropic

# To customize your API key, do this
# otherwise it will lookup ANTHROPIC_API_KEY from your env variable
# llm = Anthropic(api_key="")
llm = Anthropic(model="claude-3-sonnet-20240229")

Step 3: Install and Set Up AmazonBedrock titan-embed-text-v1

%pip install llama-index-embeddings-bedrock

from llama_index.embeddings.bedrock import BedrockEmbedding

ebed_model = BedrockEmbedding(model_name="amazon.titan-embed-text-v1")

Step 4: Install and Set Up Pgvector

%pip install llama-index-vector-stores-postgres

from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.postgres import PGVectorStore

vector_store = PGVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=url.password,
    port=url.port,
    user=url.username,
    table_name="your_table_name",
    embed_dim=1536,  # openai embedding dimension
    hnsw_kwargs={
        "hnsw_m": 16,
        "hnsw_ef_construction": 64,
        "hnsw_ef_search": 40,
        "hnsw_dist_method": "vector_cosine_ops",
    },
)

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 requests

from llama_index.core import SimpleDirectoryReader

# load documents
url = 'https://raw.githubusercontent.com/milvus-io/milvus-docs/refs/heads/v2.5.x/site/en/about/overview.md'
example_file = 'example_file.md'  # You can replace it with your own file paths.
response = requests.get(url)
with open(example_file, 'wb') as f:
    f.write(response.content)
documents = SimpleDirectoryReader(
    input_files=[example_file]
).load_data()

print("Document ID:", documents[0].doc_id)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)

query_engine = index.as_query_engine(llm=llm)
res = query_engine.query("What is Milvus?")  # You can replace it with your own question.
print(res)

Example output

Milvus is a high-performance, highly scalable vector database designed to operate efficiently across various environments, from personal laptops to large-scale distributed systems. It is available as both open-source software and a cloud service. Milvus excels in managing unstructured data by converting it into numerical vectors through embeddings, which facilitates fast and scalable searches and analytics. The database supports a wide range of data types and offers robust data modeling capabilities, allowing users to organize their data effectively. Additionally, Milvus provides multiple deployment options, including a lightweight version for quick prototyping and a distributed version for handling massive data scales.

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.

LlamaIndex optimization tips

To optimize LlamaIndex for a Retrieval-Augmented Generation (RAG) setup, structure your data efficiently using hierarchical indices like tree-based or keyword-table indices for faster retrieval. Use embeddings that align with your use case to improve search relevance. Fine-tune chunk sizes to balance context length and retrieval precision. Enable caching for frequently accessed queries to enhance performance. Optimize metadata filtering to reduce unnecessary search space and improve speed. If using vector databases, ensure indexing strategies align with your query patterns. Implement async processing to handle large-scale document ingestion efficiently. Regularly monitor query performance and adjust indexing parameters as needed for optimal results.

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.

Anthropic Claude 3 Sonnet optimization tips

To optimize Claude 3 Sonnet in RAG workflows, refine retrieval chunk sizes to balance context relevance and token efficiency—aim for 500-800 token chunks with 15% overlap. Use structured prompts with XML tags or markdown to separate instructions from retrieved content, explicitly directing Claude to ground responses in provided sources. Lower temperature (0.2-0.4) improves factual consistency, while adding validation steps like “Verify this answer is fully supported by the context” reduces hallucinations. Prioritize system prompts to define response formats and enforce source citation. Test top-p (0.7-0.9) and max tokens to control output breadth without truncation.

AmazonBedrock titan-embed-text-v1 optimization tips

To optimize titan-embed-text-v1 in a RAG setup, preprocess inputs by removing redundant whitespace and truncating excessively long texts to fit its 8K-token limit. Use batch embedding requests to reduce latency and costs. Fine-tune chunking strategies to balance context retention (e.g., 512-token segments) and avoid fragmentation. Normalize embeddings to improve retrieval accuracy. Leverage metadata filtering to refine retrieved results. Test newer model versions for performance gains. Cache frequent or repeated queries to minimize redundant computations. Monitor embedding quality via cosine similarity thresholds and adjust retrieval thresholds dynamically.

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?

Wow, what an incredible journey you’ve just been on! This tutorial has taken you through the exciting world of building a Retrieval-Augmented Generation (RAG) system, combining the robust capabilities of LlamaIndex, Pgvector, Anthropic Claude 3 Sonnet, and Amazon Bedrock's titan-embed-text-v1. You’ve learned how these components work seamlessly together to enhance your applications, offering you a powerful pipeline that incorporates a framework for efficient data management, a vector database for smooth retrieval, a large language model for generating contextual responses, and an advanced embedding model for accurate understanding of text. Each piece plays an essential role, creating a harmonious system designed to make your data interact more intelligently and effectively.

But that’s not all! Throughout this tutorial, you’ve also uncovered optimization tips that will fine-tune your RAG system for maximum performance and utilized a free RAG cost calculator to help you evaluate the feasibility and expenses of your innovative ideas. It’s thrilling to think about what you can build, optimize, and improve upon in your own projects. The potential is truly limitless! So, take this newfound knowledge and start creating your own RAG applications—experiment, innovate, and make your vision a reality. Your journey doesn’t stop here; get out there and unleash your creativity!

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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