Build RAG Chatbot with Llamaindex, OpenSearch, Databricks DBRX, and Ollama granite-embedding

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.)
Databricks DBRX: DBRX is a transformer-based decoder-only large language model (LLM) that was trained using next-token prediction. It uses a fine-grained mixture-of-experts (MoE) architecture with 132B total parameters of which 36B parameters are active on any input. It was pre-trained on 12T tokens of text and code data.
Ollama Granite-Embedding: A high-performance embedding model designed for semantic understanding and retrieval tasks. It excels at generating dense vector representations for text, enabling robust similarity search, clustering, and retrieval-augmented generation (RAG). Ideal for enterprise applications requiring scalable, privacy-preserving semantic analysis in on-premises or edge 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 Databricks DBRX

% pip install llama-index-llms-databricks

from llama_index.llms.databricks import Databricks

llm = Databricks(
    model="databricks-dbrx-instruct",
    api_key="your_api_key",
    api_base="https://[your-work-space].cloud.databricks.com/serving-endpoints/",
)

Step 3: Install and Set Up Ollama granite-embedding

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="granite-embedding",
)

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.

Databricks DBRX optimization tips

To optimize Databricks DBRX in a Retrieval-Augmented Generation (RAG) setup, focus on efficiently integrating DBRX with your vector store for seamless retrieval and real-time document processing. Leverage the platform’s scalability by partitioning large datasets and using optimized cluster configurations to handle intensive tasks. Ensure your embeddings are stored in the most efficient format, and use indexing techniques like HNSW or IVF for faster retrieval. Optimize Spark jobs by fine-tuning resource allocation, minimizing shuffle operations, and utilizing caching to reduce query latency. Finally, monitor and adjust the job execution plan to avoid unnecessary overhead, ensuring optimal performance while maintaining cost efficiency.

Ollama Granite-Embedding optimization tips

To optimize Ollama Granite-Embedding in RAG, ensure input text is cleanly chunked (avoid truncation by splitting documents into 512-token segments). Fine-tune embedding parameters like temperature and batch size for speed-quality balance. Use hardware acceleration (e.g., CUDA) and quantize the model for faster inference. Normalize embeddings to improve similarity calculations. Regularly evaluate retrieval accuracy with benchmarks like NDCG or recall@k. Cache frequent queries to reduce redundant computations, and pre-filter low-relevance documents using metadata to lighten embedding workloads.

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?

Congratulations on reaching the end of this tutorial! You've just unlocked the secrets of building a powerful Retrieval-Augmented Generation (RAG) system by integrating essential components like LlamaIndex, OpenSearch, Databricks DBRX, and the Ollama granite-embedding model. You've learned how to leverage the unique capabilities of each piece: LlamaIndex for efficient data structuring, OpenSearch for conducting high-speed searches, Databricks DBRX for streamlined database management, and the embedding model to create rich, high-dimensional representations of your data. With these tools in your arsenal, you're now equipped to develop applications that not only access vast amounts of information but also generate insightful responses that can effectively enhance user interactions.

Beyond merely assembling these components, this tutorial also covered optimization tips to ensure your RAG system runs at peak efficiency. And let’s not forget about the free RAG cost calculator; it’s an incredible resource for budgeting your project! The possibilities for innovation are endless, whether you're aiming to develop customer support bots, advanced data analysis tools, or interactive content generators. So, take this momentum and run with it! Dive into building, optimizing, and shaping your own RAG applications. Remember, the world of AI is ripe for exploration, and the only limit is your creativity. Happy building!

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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