Build RAG Chatbot with Haystack, Zilliz Cloud, Mistral Ministral 3B, 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:

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.
Mistral Ministral 3B: A compact, high-efficiency language model optimized for fast inference and low-resource environments. With 3 billion parameters, it balances performance and scalability, excelling in text generation, summarization, and question-answering tasks. Ideal for edge computing, real-time applications, and cost-sensitive deployments requiring reliable NLP capabilities without heavy computational demands.
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 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 Mistral Ministral 3B

To use Mistral models, you need first to get a Mistral API key. You can write this key in:

The api_key init parameter using Secret API
The MISTRAL_API_KEY environment variable (recommended)

Now, after you get the API key, let's install the Install the mistral-haystack package.

pip install mistral-haystack

from haystack_integrations.components.generators.mistral import MistralChatGenerator
from haystack.components.generators.utils import print_streaming_chunk
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret

generator = MistralChatGenerator(api_key=Secret.from_env_var("MISTRAL_API_KEY"), streaming_callback=print_streaming_chunk, model='ministral-3b-latest')

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

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-multilingual-v2.0")
document_embedder = CohereDocumentEmbedder(model="embed-multilingual-v2.0")

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-multilingual-v2.0")

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.

Mistral Ministral 3B optimization tips

To optimize Mistral Ministral 3B in RAG, fine-tune it on domain-specific data to improve retrieval relevance and response accuracy. Use 4-bit or 8-bit quantization to reduce memory usage while maintaining performance. Implement dynamic batching during inference to handle multiple queries efficiently. Prune redundant layers or apply LoRA for lightweight adaptation. Cache frequent retrieval outputs to minimize recomputation. Optimize prompt engineering to reduce input token length, and leverage FlashAttention for faster processing. Monitor latency and adjust context window sizes based on use-case requirements to balance speed and coherence.

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?

By diving into this tutorial, you’ve unlocked the magic of building a powerful RAG system from the ground up! You learned how Haystack acts as the backbone of your pipeline, seamlessly connecting components and orchestrating workflows to process data, retrieve information, and generate responses. Zilliz Cloud stepped in as your high-performance vector database, storing embeddings with speed and scalability, ensuring your system handles large datasets effortlessly. The Mistral Ministral 3B model showcased its lightweight yet powerful capabilities, generating human-like answers while keeping computational costs low—perfect for balancing efficiency and quality. And with Cohere’s embed-multilingual-v2.0, you saw how multilingual embeddings break language barriers, enabling your RAG system to understand and retrieve information across diverse languages with ease. Together, these tools transformed raw data into a dynamic, context-aware AI application ready to tackle real-world queries!

But the journey doesn’t stop here! You also picked up pro tips for optimizing your RAG pipeline—like tweaking chunk sizes, refining metadata filtering, and tuning indexing strategies for lightning-fast retrievals. And who could forget the free RAG cost calculator? Now you can estimate expenses and make smart trade-offs between performance and budget. Imagine the possibilities: multilingual chatbots, research assistants, or enterprise-grade knowledge bases—all within your reach. The tools are in your hands, and the roadmap is clear. So why wait? Start experimenting, iterate fearlessly, and let your creativity run wild. Build something bold, optimize it relentlessly, and watch your RAG-powered ideas come to life. The future of intelligent applications is yours to shape—go make it happen! 🚀

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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