Build RAG Chatbot with Haystack, Zilliz Cloud, Mixtral 8x7B, and OpenAI text-embedding-ada-002

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.
Mixtral 8x7B: A sparse mixture-of-experts (MoE) model with eight 7B parameter networks, designed for efficient, high-performance NLP tasks. Excels in text generation, reasoning, and multilingual support while minimizing computational costs. Ideal for scalable enterprise applications, real-time chatbots, and multi-task environments requiring optimized resource utilization and versatile AI capabilities.
OpenAI text-embedding-ada-002: A state-of-the-art embedding model designed to convert text into high-dimensional vectors, capturing semantic meaning for tasks like search, clustering, and recommendations. Renowned for efficiency, scalability, and cost-effectiveness, it excels in natural language processing applications, particularly where understanding contextual relationships and similarity across large datasets is critical.

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 Mixtral 8x7B

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='open-mixtral-8x7b')

Step 3: Install and Set Up OpenAI text-embedding-ada-002

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})

document_embedder = OpenAIDocumentEmbedder(meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Now let's install and set up the model.

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder
from haystack.components.embedders import OpenAITextEmbedder

text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-ada-002")
document_embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-ada-002")

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 = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-ada-002")

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.

Mixtral 8x7B optimization tips

To optimize Mixtral 8x7B in RAG, prioritize efficient context retrieval by fine-tuning chunk size and overlap for balanced relevance and latency. Use sparse attention configurations to reduce computational overhead, and enable tensor parallelism to leverage its mixture-of-experts architecture. Quantize the model to 4-bit precision (e.g., via GPTQ) for faster inference with minimal accuracy loss. Pre-filter retrieved documents to remove noise, and cache frequent query embeddings. Adjust temperature (0.2-0.5) and max tokens to balance creativity and focus. Profile expert routing to ensure balanced workload distribution across GPU resources.

OpenAI text-embedding-ada-002 optimization tips

To optimize text-embedding-ada-002 in RAG, ensure input text is clean and concise—remove irrelevant content, truncate long documents to the 8191-token limit, and normalize casing/punctuation. Batch embedding requests to reduce latency and costs. Use cosine similarity for relevance scoring, as embeddings are normalized. Cache frequent or static embeddings to avoid reprocessing. Experiment with chunk sizes (256-512 tokens) to balance context retention and granularity. Monitor embedding quality via downstream task performance and adjust preprocessing or retrieval thresholds as needed.

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 power of combining cutting-edge tools to build a RAG system from the ground up! You saw how Haystack acts as the brain of your pipeline, orchestrating workflows to connect your data sources, process queries, and generate intelligent responses. Zilliz Cloud stepped in as your supercharged vector database, storing and retrieving embeddings at lightning speed, ensuring your system handles even the most complex datasets effortlessly. Then came Mixtral 8x7B, the open-source LLM powerhouse, which transformed retrieved context into human-like answers, balancing creativity with precision. And let’s not forget OpenAI’s text-embedding-ada-002, the magic behind turning text into meaningful numerical representations—making sure your system understands the semantics of your data, not just keywords. Along the way, you picked up pro tips for optimizing performance, like tuning chunk sizes and balancing latency with accuracy, plus you got hands-on with that nifty free RAG cost calculator to keep your projects budget-friendly without sacrificing quality.

Now you’re equipped to build, tweak, and scale RAG systems that feel almost human! Whether you’re creating chatbots, enhancing search engines, or automating knowledge bases, you’ve got the toolkit to make it happen. The best part? This is just the beginning. Experiment with different models, fine-tune your retrieval strategies, and explore hybrid approaches to push your applications further. Remember, every line of code you write brings us closer to smarter, more intuitive AI. So go ahead—fire up your IDE, iterate fearlessly, and let your creativity run wild. The future of intelligent systems is in your hands, and you’re more than ready to shape it. 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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