Build RAG Chatbot with Llamaindex, OpenSearch, DeepSeek V3, and OpenAI text-embedding-3-large
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.)
- DeepSeek-V3: DeepSeek-V3 is a cutting-edge, open-weight large language model (LLM) with 685 billion parameters, excelling in code generation, mathematical reasoning, and long-context understanding (up to 128K tokens). It adopts the MIT license, enabling free modification, distribution, and commercial use.
- OpenAI text-embedding-3-large: A state-of-the-art embedding model designed to convert text into high-dimensional vectors, capturing deep semantic relationships. Renowned for its accuracy, scalability, and ability to handle long contexts (up to 8192 tokens), it excels in semantic search, retrieval-augmented generation (RAG), recommendation systems, and multilingual NLP tasks requiring nuanced language understanding.
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 DeepSeek V3
%pip install llama-index-llms-deepseek
from llama_index.llms.deepseek import DeepSeek
# you can also set DEEPSEEK_API_KEY in your environment variables
llm = DeepSeek(model="deepseek-chat", api_key="you_api_key")
# You might also want to set deepseek as your default llm
# from llama_index.core import Settings
# Settings.llm = llm
Step 3: Install and Set Up OpenAI text-embedding-3-large
%pip install llama-index-embeddings-openai
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding(
model="text-embedding-3-large",
)
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.
DeepSeek V3 optimization tips
DeepSeek V3 benefits from optimized retrieval and structured prompting to generate high-quality responses in RAG workflows. Improve retrieval accuracy by using domain-specific embeddings and reranking retrieved documents for relevance. Implement hierarchical chunking to structure long-form documents while keeping inputs within token limits. Use prompt tuning to guide the model’s reasoning and reduce hallucinations. Enable caching for frequently accessed knowledge to minimize latency and API costs. Experiment with retrieval augmentation techniques, such as query expansion, to improve recall. Monitor system performance and refine retrieval parameters continuously to maintain an efficient and cost-effective setup.
OpenAI text-embedding-3-large optimization tips
Optimize OpenAI text-embedding-3-large in RAG by adjusting the dimensions
parameter to balance accuracy and efficiency—lower values reduce latency and cost while retaining semantic relevance. Batch embedding requests to maximize throughput, preprocess text to remove noise (e.g., truncate to 8191 tokens, normalize whitespace), and cache frequent queries. Use cosine similarity for retrieval alignment, validate embeddings with domain-specific benchmarks, and fine-tune hybrid search strategies (e.g., combining sparse/dense vectors) to improve recall. Monitor API rate limits and leverage asynchronous calls for scalability.
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
What Have You Learned?
Congratulations on diving into the exciting world of Retrieval-Augmented Generation (RAG) systems! In this tutorial, you’ve explored how to effectively integrate key components like LlamaIndex, OpenSearch, DeepSeek V3, and the OpenAI text-embedding-3-large model into a powerful RAG pipeline. You've learned how LlamaIndex can streamline your data retrieval, ensuring that you have quick access to relevant information. OpenSearch has equipped you with robust search capabilities, making it a breeze to find the content you need. And let’s not forget DeepSeek V3 and the embeddings model, which together enhance your application’s ability to generate contextually rich and accurate responses. With every interaction, you are tapping into the potential of advanced AI, and it’s fantastic to see how each element contributes uniquely to your RAG system.
Moreover, the tutorial was packed with practical tips to optimize your setup, ensuring efficiency and harnessing the full capabilities of each component. You also received access to a free RAG cost calculator—what a fantastic resource to keep track of your tool usage and expenses! With this newfound knowledge, the next step is all yours. Dive into the world of RAG applications, experiment with what you’ve learned, and let your creativity soar. Build, optimize, and innovate your own unique solutions, and remember, every great project starts with a single step. Your journey into the realms of AI awaits—so let’s get to 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
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! 💖
- Introduction to RAG
- Key Components We'll Use for This RAG Chatbot
- Step 1: Install and Set Up Llamaindex
- Step 2: Install and Set Up DeepSeek V3
- Step 3: Install and Set Up OpenAI text-embedding-3-large
- Step 4: Install and Set Up OpenSearch
- Step 5: Build a RAG Chatbot
- Optimization Tips
- RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds
- What Have You Learned?
- Further Resources
- We'd Love to Hear What You Think!
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