Build RAG Chatbot with Haystack, OpenSearch, Google Vertex AI Gemini 2.0 Pro, and STACKIT e5-mistral-7b-instruct
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.)
- Google Vertex AI Gemini 2.0 Pro: A multimodal AI model optimized for complex enterprise tasks, offering advanced reasoning, high accuracy, and scalability. It excels in processing text, code, and structured data, with seamless integration into Google Cloud. Ideal for data analysis, content generation, and automation in regulated industries, leveraging robust security and cloud-native deployment.
- STACKIT e5-mistral-7b-instruct: A 7B-parameter language model optimized for instruction-based tasks, delivering efficient, context-aware responses. Excels in natural language understanding, scalability, and low-latency performance. Ideal for enterprise automation, customer support, technical documentation, and generating structured outputs from complex prompts. Combines precision with adaptability for business-critical AI applications.
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 Google Vertex AI Gemini 2.0 Pro
Using theVertexAIGeminiGenerator
with Haystack requires authentication using Google Cloud Application Default Credentials (ADCs). This means your application must be set up with credentials that allow it to access Google Cloud services. If you're not sure how to configure ADCs, check the official Google documentation for setup instructions.
It's important to use a Google Cloud account that has the right permissions to access a project with Google Vertex AI endpoints. Without proper access, the generator won’t work as expected.
To find your project ID, you can either look it up in the Google Cloud Console under the resource manager or run the following command in your terminal.
Now let's install and set up this model.
pip install google-vertex-haystack
from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator
generator = VertexAIGeminiGenerator(model="gemini-2.0-pro-exp-02-05")
Step 3: Install and Set Up STACKIT e5-mistral-7b-instruct
pip install stackit-haystack
from haystack_integrations.components.embedders.stackit import STACKITTextEmbedder
from haystack_integrations.components.embedders.stackit import STACKITDocumentEmbedder
text_embedder = STACKITTextEmbedder(model="intfloat/e5-mistral-7b-instruct")
document_embedder = STACKITDocumentEmbedder(model="intfloat/e5-mistral-7b-instruct")
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 = STACKITTextEmbedder(model="intfloat/e5-mistral-7b-instruct")
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.
Google Vertex AI Gemini 2.0 Pro optimization tips
To enhance performance in RAG, fine-tune Gemini 2.0 Pro’s context window by truncating irrelevant sections of retrieved documents, prioritize concise chunks (500-800 tokens) to balance detail and processing speed. Use structured prompts with explicit instructions (e.g., “Answer using ONLY the context below”) to reduce hallucinations. Enable low-temperature sampling (0.1–0.3) for factual accuracy, and leverage batch inference for parallel processing. Regularly validate outputs against retrieval quality, and use Vertex AI’s monitoring tools to track latency, token usage, and error rates for iterative tuning.
STACKIT e5-mistral-7b-instruct optimization tips
To optimize STACKIT e5-mistral-7b-instruct in RAG, ensure input context is well-structured with clear document chunks (≤512 tokens) and metadata for precise retrieval. Use dynamic temperature and top-p sampling to balance creativity and relevance. Fine-tune retrieval thresholds to minimize irrelevant context injection. Batch process queries for GPU efficiency, and enable FlashAttention for faster inference. Precompute embeddings for static data to reduce latency. Regularly evaluate retrieval accuracy and model outputs via metrics like Hit Rate and ROUGE, adjusting prompts and chunk sizes 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
What Have You Learned?
Wow, you’ve just navigated the exciting journey of building a RAG system from scratch! By diving into this tutorial, you’ve learned how to weave together four powerful tools—Haystack as your orchestration framework, OpenSearch as your lightning-fast vector database, Google Vertex AI Gemini 2.0 Pro as your creative LLM, and STACKIT e5-mistral-7b-instruct as your embedding model—to create a pipeline that can retrieve and generate answers intelligently. You saw how Haystack acts as the glue, seamlessly connecting data ingestion, embedding generation, and query handling, while OpenSearch turbocharges retrieval with its vector search capabilities. The magic truly came alive as Gemini transformed retrieved snippets into human-like responses, all powered by Mistral’s embeddings that turn text into meaningful numerical representations. Plus, you picked up pro tips like optimizing chunk sizes for accuracy and speed, and even discovered a free RAG cost calculator to estimate expenses before scaling—tools that’ll save you time and headaches down the road.
But this is just the beginning! You’re now equipped to experiment, tweak, and innovate. Imagine customizing this pipeline for niche domains, enhancing it with hybrid search, or fine-tuning models for even sharper results. The possibilities are endless, and the skills you’ve gained are your launchpad. So go ahead—build that dream RAG app, optimize it fearlessly, and let your creativity run wild. The future of intelligent systems is in your hands, and there’s no better time to start shaping it. 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
We'd Love to Hear What You Think!
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- Introduction to RAG
- Key Components We'll Use for This RAG Chatbot
- Step 1: Install and Set Up Haystack
- Step 2: Install and Set Up Google Vertex AI Gemini 2.0 Pro
- Step 3: Install and Set Up STACKIT e5-mistral-7b-instruct
- Step 4: Install and Set Up OpenSearch
- Step 5: Build a RAG Chatbot
- Optimization Tips
- RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds
- What Have You Learned?
- Further Resources
- We'd Love to Hear What You Think!
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