Build RAG Chatbot with Haystack, Milvus, Mistral Large, and HuggingFace all-MiniLM-L12-v1

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.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
Mistral Large: A state-of-the-art language model optimized for advanced reasoning, multilingual tasks, and high-stakes decision-making. It excels in code generation, complex analysis, and cross-lingual understanding, offering scalability, efficiency, and high accuracy for enterprise solutions, AI-driven research, and global customer interaction platforms.
HuggingFace all-MiniLM-L12-v1: A compact sentence embedding model designed to convert text into dense vector representations for semantic understanding. It balances speed and efficiency with strong performance in tasks like semantic search, text clustering, and retrieval-augmented generation. Ideal for applications requiring low-latency inference or resource-constrained environments while maintaining robust semantic analysis capabilities.

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 Large

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='mistral-large-latest')

Step 3: Install and Set Up HuggingFace all-MiniLM-L12-v1

Haystack'sHuggingFaceAPITextEmbedder can be used to embed strings with different Hugging Face APIs:

The component uses a HF_API_TOKEN environment variable by default. Otherwise, you can pass a Hugging Face API token at initialization with token – see code examples below. The token is needed:

If you use the Serverless Inference API, or
If you use Inference Endpoints.

Here, in this tutorial, we'll use the Free Serverless Inference API. Let's install and set up the model.

To use this API, you need a free Hugging Face token. The Embedder expects the model in api_params.

from haystack.components.embedders import HuggingFaceAPITextEmbedder
from haystack.utils import Secret
from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.dataclasses import Document

text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
                                           api_params={"model": "sentence-transformers/all-MiniLM-L12-v1"},
                                           token=Secret.from_token("<your-api-key>"))
document_embedder = HuggingFaceAPIDocumentEmbedder(api_type="serverless_inference_api",
                                              api_params={"model": "sentence-transformers/all-MiniLM-L12-v1"},
                                              token=Secret.from_token("<your-api-key>"))

Step 4: Install and Set Up Milvus

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": "./milvus.db"}, 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 = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
                                           api_params={"model": "sentence-transformers/all-MiniLM-L12-v1"},
                                           token=Secret.from_token("<your-api-key>"))

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.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

Mistral Large optimization tips

To enhance Mistral Large’s performance in RAG systems, prioritize efficient context handling by truncating or summarizing retrieved documents to fit its token limit while retaining key information. Fine-tune prompts to explicitly guide the model to reference retrieved content, using phrases like “based on the provided context.” Adjust temperature settings (lower for factuality, higher for creativity) and max token limits to balance output quality and length. Implement caching for frequent queries, and use parallel processing to speed up document retrieval. Regularly evaluate retrieval relevance scores to ensure high-quality inputs, and experiment with chunk sizes/overlaps during indexing to optimize context granularity.

HuggingFace all-MiniLM-L12-v1 optimization tips

To optimize the all-MiniLM-L12-v1 model in a RAG setup: preprocess input data by cleaning and normalizing text (lowercasing, removing special characters) to improve embedding quality. Use batch inference for embedding generation to maximize GPU utilization. Fine-tune the model on domain-specific data via contrastive learning to enhance retrieval relevance. Reduce vector dimensionality via PCA if storage or latency is critical. Cache frequently accessed embeddings to minimize recomputation. Quantize the model with Hugging Face’s transformers library for faster inference with minimal accuracy loss. Regularly benchmark performance against your retrieval metrics (e.g., recall@k) to validate optimizations.

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 fully functional RAG system from scratch! You learned how Haystack acts as the flexible framework orchestrating your pipeline, seamlessly connecting your data sources, retrieval logic, and generative AI. With Milvus as your vector database, you saw how lightning-fast similarity searches enable precise retrieval of relevant context, even from massive datasets. The HuggingFace all-MiniLM-L12-v1 embedding model transformed your raw text into meaningful numerical representations, bridging the gap between human language and machine understanding. And Mistral Large stepped in as the powerhouse LLM, generating insightful, coherent responses by synthesizing retrieved context with its vast knowledge. Together, these tools create a dynamic RAG workflow where retrieval meets generation, turning information overload into actionable answers.

But you didn’t stop there! The tutorial also equipped you with pro tips—like optimizing chunking strategies for better embeddings and using metadata filtering in Milvus to refine results. You even discovered how to use a free RAG cost calculator to estimate expenses and fine-tune your system’s efficiency. Now that you’ve seen the magic of integrating these components, imagine what’s next! Whether you’re building a smarter chatbot, a research assistant, or a knowledge hub, you’ve got the toolkit to innovate. So go ahead—experiment, tweak, and scale. The world of RAG is yours to explore, and every iteration brings you closer to creating something truly transformative. Ready, set, build—your next breakthrough is just a pipeline away! 🚀

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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