Build RAG Chatbot with Haystack, OpenSearch, OpenAI GPT-o3-mini, and Mistral Embed

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.
OpenSearch: An open-source search and analytics suite derived from Elasticsearch. It offers robust full-text search and real-time analytics, with vector search available as an add-on for similarity-based queries, extending its capabilities to handle high-dimensional data. Since it is just a vector search add-on rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer 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.)
OpenAI GPT-o3-mini: A lightweight, efficient language model optimized for rapid text generation and comprehension. Designed to balance performance with resource efficiency, it excels in applications requiring quick responses and lower computational costs, such as mobile apps, customer service chatbots, and real-time content moderation. Ideal for developers seeking scalable AI solutions with minimal infrastructure demands.
Mistral Embed: A high-performance embedding model designed to convert text into dense vector representations, capturing semantic meaning for tasks like retrieval, clustering, and similarity analysis. It excels in efficiency, multilingual support, and scalability, making it ideal for semantic search engines, multilingual content organization, and large-scale data processing applications requiring rapid, context-aware text analysis.

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-o3-mini

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="o3-mini", api_key=Secret.from_token("<your-api-key>"))

Step 3: Install and Set Up Mistral Embed

pip install mistral-haystack

from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
from haystack import Document
from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder

text_embedder = MistralTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="mistral-embed")
document_embedder = MistralDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="mistral-embed")

Step 4: Install and Set Up OpenSearch

If you have Docker set up, we recommend pulling the Docker image and running it.

docker pull opensearchproject/opensearch:2.11.0
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" opensearchproject/opensearch:2.11.0

Once you have a running OpenSearch instance, install the opensearch-haystack integration:

pip install opensearch-haystack

from haystack_integrations.components.retrievers.opensearch  import OpenSearchEmbeddingRetriever
from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore

document_store = OpenSearchDocumentStore(hosts="http://localhost:9200", use_ssl=True,
verify_certs=False, http_auth=("admin", "admin"))
retriever = OpenSearchEmbeddingRetriever(document_store=document_store)

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 = OpenSearchEmbeddingRetriever(document_store=document_store) 
 text_embedder = MistralTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="mistral-embed")

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.

OpenSearch optimization tips

To optimize OpenSearch in a Retrieval-Augmented Generation (RAG) setup, fine-tune indexing by enabling efficient mappings and reducing unnecessary stored fields. Use HNSW for vector search to speed up similarity queries while balancing recall and latency with appropriate ef_search and ef_construction values. Leverage shard and replica settings to distribute load effectively, and enable caching for frequent queries. Optimize text-based retrieval with BM25 tuning and custom analyzers for better relevance. Regularly monitor cluster health, index size, and query performance using OpenSearch Dashboards and adjust configurations accordingly.

OpenAI GPT-3-mini optimization tips

Optimize OpenAI GPT-3-mini in RAG by chunking input data into smaller, semantically coherent segments to reduce token waste and improve retrieval relevance. Use structured prompts with explicit instructions (e.g., "Answer based on: [context]") to guide outputs. Fine-tune temperature (0.2-0.5 for precision) and max tokens to balance brevity and completeness. Cache frequent queries to reduce latency and costs. Preprocess retrieved documents to remove redundancy and align with query intent. Monitor outputs via metrics like BLEU or ROUGE and iterate based on user feedback.

Mistral Embed optimization tips

To optimize Mistral Embed in a RAG setup, preprocess text by removing redundant whitespace, special characters, and normalizing casing to reduce embedding noise. Use batch processing for bulk embeddings to leverage GPU parallelism. Fine-tune Mistral Embed on domain-specific data if retrieval accuracy is low. Reduce input sequence length via truncation or sliding windows for long documents. Cache frequent queries to save compute. Test different pooling strategies (mean, max) for sentence-level embeddings and normalize outputs to improve similarity scoring consistency.

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 RAG system from the ground up! You now understand how to weave together four powerful tools: Haystack as the flexible framework orchestrating your pipeline, OpenSearch as the lightning-fast vector database storing and retrieving knowledge, OpenAI’s GPT-3 (or alternatives like Mistral) as the creative LLM generating human-like responses, and Mistral Embed as the embedding model transforming text into rich numerical representations. You’ve seen firsthand how these components collaborate—Haystack seamlessly connects them, OpenSearch retrieves context with precision, Mistral Embed encodes meaning, and the LLM crafts answers that feel both informed and natural. Along the way, you learned optimization tricks like chunking strategies for cleaner data ingestion, hybrid search techniques to balance speed and accuracy, and metadata filtering to refine results. Plus, that free RAG cost calculator? It’s your secret weapon for balancing performance and budget, ensuring your projects stay scalable without breaking the bank.

But this isn’t just about following steps—it’s about sparking innovation! You’ve got the blueprint to build smarter applications, whether that’s chatbots that answer like experts, search engines that read minds, or tools that turn data chaos into clarity. The best part? You’re now equipped to experiment, tweak, and push boundaries. Maybe you’ll fine-tune embeddings for niche domains, optimize OpenSearch indexes for blazing speed, or layer in your own custom logic. The possibilities are endless, and the world of generative AI is yours to explore. So go ahead—fire up your IDE, play with the code, and watch your ideas come to life. Every line you write, every tweak you make, brings you closer to building something groundbreaking. Ready, set, RAG! 🚀

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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