Build RAG Chatbot with Llamaindex, Faiss, Jamba Large, and Cohere embed-multilingual-v2.0

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.
- Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want 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 Large: Jamba Large is a robust language model built for high-performance tasks requiring deep contextual understanding. It offers strong capabilities in generating complex responses and handling intricate queries, making it ideal for advanced applications like virtual assistants, content creation, and conversational AI in enterprise solutions.
Cohere embed-multilingual-v2.0: A multilingual embedding model designed to convert text in over 100 languages into high-dimensional vectors. It excels in capturing semantic relationships across diverse languages, enabling robust cross-lingual search, content recommendation, and multilingual NLP applications. Ideal for global enterprises needing scalable, language-agnostic text analysis and retrieval solutions.

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 Large

%pip install llama-index-llms-ai21

from llama_index.llms.ai21 import AI21

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

Step 3: Install and Set Up Cohere embed-multilingual-v2.0

%pip install llama-index-embeddings-cohere

from llama_index.embeddings.cohere import CohereEmbedding

embed_model = CohereEmbedding(
    api_key=cohere_api_key,
    model_name="embed-multilingual-v2.0",
)

Step 4: Install and Set Up Faiss

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

from llama_index.core import (
    SimpleDirectoryReader,
    load_index_from_storage,
    VectorStoreIndex,
    StorageContext,
)
from llama_index.vector_stores.faiss import FaissVectorStore

vector_store = FaissVectorStore(faiss_index=faiss_index)

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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

Jamba Large optimization tips

Optimizing Jamba Large in a RAG setup involves utilizing its advanced capabilities for complex queries and multi-turn conversations. Leverage fine-tuning to improve domain-specific understanding, ensuring that the model can generate more relevant and accurate results. Optimize your retrieval pipeline by using well-structured, concise document chunks that maintain context without overwhelming the model’s input size. Reduce inference time by batching queries when possible and using efficient hardware acceleration, such as GPUs. Experiment with dynamic prompt adjustments to improve response quality, and ensure that document reranking is utilized for highly relevant results during retrieval.

Cohere embed-multilingual-v2.0 optimization tips

To optimize Cohere embed-multilingual-v2.0 in RAG, preprocess text by normalizing languages (lowercasing, removing diacritics) and chunking documents into 512-token segments for compatibility. Use domain-specific fine-tuning via Cohere’s API to align embeddings with specialized vocabularies. Cache frequently accessed embeddings to reduce latency and costs. Batch embedding requests for bulk processing. Align query language with document language for improved retrieval accuracy, and apply L2 normalization before similarity calculations. Monitor retrieval hit rates to refine chunking strategies and fine-tuning datasets iteratively.

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 making it through this tutorial! You've taken a big step in understanding how to harness the power of modern AI by integrating LlamaIndex, Faiss, Jamba Large, and the Cohere embed-multilingual-v2.0 model to build a robust Retrieval-Augmented Generation (RAG) system. By now, you should have a solid grasp of how each component plays a crucial role in creating an efficient RAG pipeline. LlamaIndex serves as a fantastic framework for structuring and managing your data, while Faiss allows you to perform lightning-fast searches within your vector database. Plus, with the Jamba Large LLM at your disposal, you're ready to generate high-quality responses that truly enhance the user experience. Let’s not forget the embedding capabilities of Cohere, which can process multilingual input, opening up a world of possibilities for your projects!

As you dove into the details, I hope you picked up some valuable optimization tips that can help enhance the performance of your RAG system. And don’t overlook the handy cost calculator we provided – it’s a great tool to evaluate your RAG resource expenditures, ensuring you stay within budget while pushing the boundaries of innovation. Now that you’re equipped with this amazing knowledge, the sky's the limit! Go ahead and start building, optimizing, and personalizing your own RAG applications. Remember, every great project begins with a single step, and the possibilities are endless. You’re on your way to creating something extraordinary – let’s see 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

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