Build RAG Chatbot with Llamaindex, OpenSearch, Jamba Mini, and Ollama paraphrase-multilingual

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.)
Jamba Mini: Jamba Mini is a lightweight AI model designed for real-time text generation and natural language understanding in resource-constrained environments. With a balance of efficiency and accuracy, it is ideal for mobile apps, edge devices, and interactive assistants that require quick, low-latency responses.
Ollama Paraphrase-Multilingual: A versatile AI model designed to rephrase and restructure text across multiple languages while preserving meaning. Strengths include multilingual adaptability, context retention, and semantic accuracy. Ideal for translation enhancement, cross-lingual content generation, global customer support, and academic or technical writing requiring nuanced paraphrasing in diverse linguistic contexts.

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 Jamba Mini

%pip install llama-index-llms-ai21

from llama_index.llms.ai21 import AI21

llm = AI21(
    model="jamba-mini", api_key=api_key, max_tokens=100, temperature=0.5
)

Step 3: Install and Set Up Ollama paraphrase-multilingual

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="paraphrase-multilingual",
)

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.

Jamba Mini optimization tips

To optimize Jamba Mini in a Retrieval-Augmented Generation (RAG) setup, focus on fine-tuning the model to handle domain-specific language and user intents. Preprocess inputs effectively by normalizing text and removing noise, ensuring relevant context is fed into the model. Utilize caching for frequent queries to reduce latency, and experiment with prompt engineering to guide the model’s responses toward greater accuracy. Monitor system performance and adjust batch sizes to maintain efficiency, while minimizing resource consumption. You can also optimize memory management by using lightweight data structures and ensuring fast retrieval from the knowledge base.

Ollama paraphrase-multilingual optimization tips

To optimize Ollama paraphrase-multilingual in a RAG setup, preprocess input text to remove noise and standardize formats (e.g., lowercasing, punctuation normalization). Use smaller temperature values (e.g., 0.3) for deterministic outputs and adjust max_length to balance context retention and brevity. Batch processing parallelizes paraphrasing for efficiency. Cache frequent or repetitive queries to reduce redundant computations. Validate outputs with metrics like BLEU or semantic similarity scores. For multilingual use, explicitly specify language codes in prompts to avoid ambiguity. Fine-tune on domain-specific data if available, and leverage GPU acceleration for faster inference.

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 a journey we've been on together! After diving into this tutorial, you should now have a solid understanding of how to integrate a framework like LlamaIndex with a vector database such as OpenSearch, harnessing the powerful capabilities of an LLM like Jamba Mini and an embedding model from Ollama. You've seen how these components work harmoniously to construct a Retrieval-Augmented Generation (RAG) pipeline that not only enhances the way you manage data but also revolutionizes the way you generate content. Isn't it amazing how these tools can help you retrieve relevant information quickly and generate contextually rich responses? You’ve equipped yourself with knowledge about the strengths of each component and how they synergize to create a robust application that's tailored to your needs.

Along the way, we also explored some nifty optimization tips that can help streamline your RAG system for even better performance. And don't forget about that invaluable free RAG cost calculator — it’s a fantastic tool to help you evaluate and optimize your project's expenses as it scales! Now that you have this hands-on experience and insights, it's time to put your skills into action. Get out there and start building, optimizing, and innovating your own RAG applications! The possibilities are endless, and with your newfound knowledge, you are well on your way to making a significant impact. Let your creativity flow and watch what you can achieve!

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

We'd Love to Hear What You Think!

We’d love to hear your thoughts! 🌟 Leave your questions or comments below or join our vibrant Milvus Discord community to share your experiences, ask questions, or connect with thousands of AI enthusiasts. Your journey matters to us!

If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖