Build RAG Chatbot with Llamaindex, OpenSearch, Anthropic Claude 3.5 Sonnet, and Ollama nomic-embed-text

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.
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.)
Anthropic Claude 3.5 Sonnet: An advanced AI model optimized for complex reasoning, multilingual generation, and data analysis. Combines high accuracy with efficiency, ideal for enterprise automation, technical workflows, and customer support requiring deep contextual understanding and scalable deployment across diverse applications.
Ollama nomic-embed-text: A versatile text embedding model optimized for efficient generation of high-dimensional vector representations, excelling in semantic search, clustering, and similarity analysis. Strengths include robust performance on long-context inputs and scalability for local deployment. Ideal for applications requiring accurate document retrieval, recommendation systems, or contextual understanding in resource-constrained 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 3.5 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-3-5-sonnet-latest")

Step 3: Install and Set Up Ollama nomic-embed-text

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="nomic-embed-text",
)

Step 4: Install and Set Up OpenSearch

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

from os import getenv
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.opensearch import (
    OpensearchVectorStore,
    OpensearchVectorClient,
)
from llama_index.core import VectorStoreIndex, StorageContext

# http endpoint for your cluster (opensearch required for vector index usage)
endpoint = getenv("OPENSEARCH_ENDPOINT", "http://localhost:9200")
# index to demonstrate the VectorStore impl
idx = getenv("OPENSEARCH_INDEX", "gpt-index-demo")

# OpensearchVectorClient stores text in this field by default
text_field = "content"
# OpensearchVectorClient stores embeddings in this field by default
embedding_field = "embedding"
# OpensearchVectorClient encapsulates logic for a
# single opensearch index with vector search enabled
client = OpensearchVectorClient(
    endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field
)
# initialize vector store
vector_store = OpensearchVectorStore(client)

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.

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.

Anthropic Claude 3.5 Sonnet optimization tips

Optimize Claude 3.5 Sonnet in RAG systems by refining retrieval quality through semantic chunking (300-500 tokens) with 15% overlap for context continuity. Use structured prompts with clear instructions and document separators (e.g., ``` markers) to distinguish context from queries. Set temperature=0.3 for factual consistency and max_tokens=512 to balance depth/brevity. Implement query-aware context filtering to remove irrelevant passages before generation. Leverage system prompts to enforce output formats and safety guardrails. Monitor latency using streaming mode for real-time applications, and cache frequent query-answer pairs to reduce repeat computations.

Ollama nomic-embed-text optimization tips

To optimize Ollama nomic-embed-text in RAG, ensure input text is preprocessed (normalize casing, remove redundant whitespace) and chunked into coherent segments matching the model’s optimal context window. Use batch inference for bulk embeddings to reduce latency, and leverage GPU acceleration if available. Fine-tune embedding dimensions via dimensionality reduction if retrieval speed is critical. Regularly validate embedding quality with domain-specific benchmarks, and cache frequently queried embeddings to minimize redundant computations. Adjust temperature and similarity thresholds during retrieval to balance precision and recall.

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 exciting journey we've been on together! After diving into this tutorial, you now have a solid understanding of how to integrate a powerful framework like LlamaIndex with a robust vector database such as OpenSearch, alongside cutting-edge technologies like Anthropic Claude 3.5 Sonnet and the Ollama nomic-embed-text model. You've seen how these components come together to create a state-of-the-art Retrieval-Augmented Generation (RAG) system that can boost your applications with intelligent, context-rich responses. Whether you're sourcing information or generating creative content, your new knowledge equips you to harness the full potential of these tools, giving your work a distinct edge!

We also explored some handy optimization tips to fine-tune your RAG pipeline, maximizing performance while keeping costs in check. Remember that incredible free RAG cost calculator we introduced? That’s your new best friend for managing resources smartly as you experiment and innovate! The possibilities are endless, and with your newfound expertise, you’re ready to take the plunge into building and refining your own RAG applications. So, get out there, start creating, and don’t hesitate to push the boundaries of what’s possible. Let your imagination run wild—you’re on the brink of something remarkable!

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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