Build RAG Chatbot with Haystack, Zilliz Cloud, OpenAI GPT-4o, and Cohere embed-english-light-v3.0

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:

Haystack: An open-source Python framework designed for building production-ready NLP applications, particularly question answering and semantic search systems. Haystack excels at retrieving information from large document collections through its modular architecture that combines retrieval and reader components. Ideal for developers creating search applications, chatbots, and knowledge management systems that require efficient document processing and accurate information extraction from unstructured text.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It offers a free tier supporting up to 1 million vectors.
OpenAI GPT-4o: A multimodal AI model optimized for advanced natural language processing, image understanding, and audio analysis. It excels in real-time interactions, offering high accuracy, contextual awareness, and seamless integration across diverse inputs. Ideal for dynamic applications like interactive customer support, content generation, multilingual translation, and complex data synthesis in industries requiring rapid, adaptive AI solutions.
Cohere embed-english-light-v3.0: A lightweight, efficient embedding model designed to convert English text into high-dimensional vector representations. Excelling in speed and scalability, it balances accuracy with low computational demands, making it ideal for semantic search, text clustering, and retrieval-augmented applications 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 Haystack

import os
import requests

from haystack import Pipeline
from haystack.components.converters import MarkdownToDocument

from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter

Step 2: Install and Set Up OpenAI GPT-4o

To use OpenAI models, you need to get an OpenAI API key. The Haystack integration with OpenAI models uses an OPENAI_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key:

generator = OpenAIGenerator(api_key=Secret.from_token("<your-api-key>"), model="gpt-4o-mini")

Then, the generator component needs a prompt to operate, but you can pass any text generation parameters valid for the openai.ChatCompletion.create method directly to this component using the generation_kwargs parameter, both at initialization and to run() method. For more details on the parameters supported by the OpenAI API, refer to the OpenAI documentation.

Now let's install and set up OpenAI models.

from haystack.components.generators import OpenAIGenerator

generator = OpenAIGenerator(model="gpt-4o", api_key=Secret.from_token("<your-api-key>"))

Step 3: Install and Set Up Cohere embed-english-light-v3.0

To start using this integration with Haystack, install it with:

pip install cohere-haystack

from haystack import Document
from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder
from haystack_integrations.components.embedders.cohere.text_embedder import CohereTextEmbedder

text_embedder = CohereTextEmbedder(model="embed-english-light-v3.0")
document_embedder = CohereDocumentEmbedder(model="embed-english-light-v3.0")

Step 4: Install and Set Up Zilliz Cloud

pip install --upgrade pymilvus milvus-haystack

from milvus_haystack import MilvusDocumentStore
from milvus_haystack.milvus_embedding_retriever import MilvusEmbeddingRetriever

document_store = MilvusDocumentStore(connection_args={"uri": ZILLIZ_CLOUD_URI, "token": ZILLIZ_CLOUD_TOKEN}, drop_old=True,)
retriever = MilvusEmbeddingRetriever(document_store=document_store, top_k=3)

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 your own dataset to customize your RAG chatbot.

url = 'https://raw.githubusercontent.com/milvus-io/milvus-docs/refs/heads/v2.5.x/site/en/about/overview.md'
example_file = 'example_file.md'
response = requests.get(url)
with open(example_file, 'wb') as f:
    f.write(response.content)
file_paths = [example_file]  # You can replace it with your own file paths.

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("converter", MarkdownToDocument())
indexing_pipeline.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2))
indexing_pipeline.add_component("embedder", document_embedder)
indexing_pipeline.add_component("writer", DocumentWriter(document_store))
indexing_pipeline.connect("converter", "splitter")
indexing_pipeline.connect("splitter", "embedder")
indexing_pipeline.connect("embedder", "writer")
indexing_pipeline.run({"converter": {"sources": file_paths}})

# print("Number of documents:", document_store.count_documents())
  
question = "What is Milvus?"  # You can replace it with your own question.

retrieval_pipeline = Pipeline()
retrieval_pipeline.add_component("embedder", text_embedder)
retrieval_pipeline.add_component("retriever", retriever)
retrieval_pipeline.connect("embedder", "retriever")

retrieval_results = retrieval_pipeline.run({"embedder": {"text": question}})

# for doc in retrieval_results["retriever"]["documents"]:
#     print(doc.content)
#     print("-" * 10)

from haystack.utils import Secret
from haystack.components.builders import PromptBuilder

retriever = MilvusEmbeddingRetriever(document_store=document_store, top_k=3) 
 text_embedder = CohereTextEmbedder(model="embed-english-light-v3.0")

prompt_template = """Answer the following query based on the provided context. If the context does
                     not include an answer, reply with 'I don't know'.\n
                     Query: {{query}}
                     Documents:
                     {% for doc in documents %}
                        {{ doc.content }}
                     {% endfor %}
                     Answer: 
                  """

rag_pipeline = Pipeline()
rag_pipeline.add_component("text_embedder", text_embedder)
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", PromptBuilder(template=prompt_template))
rag_pipeline.add_component("generator", generator)
rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "generator")

results = rag_pipeline.run({"text_embedder": {"text": question}, "prompt_builder": {"query": question},})
print('RAG answer:\n', results["generator"]["replies"][0])

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.

Haystack optimization tips

To optimize Haystack in a RAG setup, ensure you use an efficient retriever like FAISS or Milvus for scalable and fast similarity searches. Fine-tune your document store settings, such as indexing strategies and storage backends, to balance speed and accuracy. Use batch processing for embedding generation to reduce latency and optimize API calls. Leverage Haystack's pipeline caching to avoid redundant computations, especially for frequently queried documents. Tune your reader model by selecting a lightweight yet accurate transformer-based model like DistilBERT to speed up response times. Implement query rewriting or filtering techniques to enhance retrieval quality, ensuring the most relevant documents are retrieved for generation. Finally, monitor system performance with Haystack’s built-in evaluation tools to iteratively refine your setup based on real-world query performance.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

OpenAI GPT-4o optimization tips

To optimize OpenAI GPT-4o in a RAG setup, structure inputs with concise, context-rich prompts and preprocess retrieved documents to remove noise. Use metadata filtering to prioritize relevant sources and limit context length to avoid token waste. Fine-tune retrieval models to align with GPT-4o’s strengths, employ chunking with overlap for continuity, and experiment with temperature and max_token settings for balanced creativity and focus. Cache frequent queries and implement rate limiting to manage costs. Regularly evaluate outputs to refine retrieval and generation alignment.

Cohere embed-english-light-v3.0 optimization tips

To optimize Cohere embed-english-light-v3.0 in RAG, ensure input text is clean and concise by removing redundant whitespace, special characters, or irrelevant metadata. Use batch processing for embeddings to reduce API calls and latency. Align chunk sizes with the model’s 512-token limit, splitting longer texts into coherent segments. Cache frequent or static embeddings to save costs. Fine-tune retrieval scoring (e.g., cosine similarity) to match your data distribution, and pre-filter low-relevance documents using metadata to reduce computational overhead. Regularly validate embedding quality against domain-specific benchmarks.

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?

By diving into this tutorial, you’ve unlocked the essentials of building a powerful RAG system from the ground up! You learned how Haystack acts as the flexible framework that stitches everything together, making it easy to orchestrate workflows and manage interactions between components. Zilliz Cloud stepped in as your high-performance vector database, handling the heavy lifting of storing and retrieving embeddings at scale with speed and precision. The Cohere embed-english-light-v3.0 model transformed your raw text into rich, semantic vectors, capturing meaning in a way that supercharges search accuracy. And OpenAI’s GPT-4o? It became the brain of your application, synthesizing retrieved data into coherent, context-aware responses that feel almost human. Together, these tools created a seamless pipeline where data flows from ingestion to insight, proving how RAG can turn static information into dynamic, actionable knowledge!

But you didn’t just stop at the basics—you explored optimization tricks like chunking strategies and metadata filtering to boost efficiency, and even discovered how to use a free RAG cost calculator to budget your projects smartly. Now, imagine what’s next! Whether you’re building chatbots, research tools, or enterprise solutions, you’ve got the skills to tailor RAG systems to your needs. The future of AI-powered applications is in your hands. So go ahead—experiment, tweak, and innovate. Your ideas deserve to come alive, and with these tools, you’re ready to make them shine. Let’s build something amazing! 🚀

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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