Build RAG Chatbot with Haystack, OpenSearch, Amazon Bedrock Claude 3 Haiku, 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.
- 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.)
- AmazonBedrock Claude 3 Haiku: A lightweight, high-speed AI model optimized for rapid, cost-efficient processing of simple to moderately complex tasks. Excels in low-latency applications, offering strong accuracy and scalability for real-time use cases like chatbots, content moderation, data extraction, and high-volume query handling while minimizing operational costs.
- 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 Amazon Bedrock Claude 3 Haiku
Amazon Bedrock is a fully managed service that makes high-performing foundation models from leading AI startups and Amazon available through a unified API. You can choose from various foundation models to find the one best suited for your use case.
To use LLMs on Amazon Bedrock for text generation together with Haystack, you need to initialize an AmazonBedrockGenerator
with the model name, the AWS credentials (AWS_ACCESS_KEY_ID
, AWS_SECRET_ACCESS_KEY
, AWS_DEFAULT_REGION
) should be set as environment variables, be configured as described above or passed as Secret arguments. Note, make sure the region you set supports Amazon Bedrock.
Now, let's start installing and setting up models with Amazon Bedrock.
pip install amazon-bedrock-haystack
from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockGenerator
aws_access_key_id="..."
aws_secret_access_key="..."
aws_region_name="eu-central-1"
generator = AmazonBedrockGenerator(model="anthropic.claude-3-haiku-20240307-v1:0")
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 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 = 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.
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.
AmazonBedrock Claude 3 Haiku optimization tips
To optimize Claude 3 Haiku in RAG, structure prompts with clear context markers (e.g., <context>
and </context>
) to separate retrieved documents from instructions. Use concise, direct queries to leverage its fast response strength, and set max_tokens
to balance output length with latency. Fine-tune temperature (0.2–0.5 for factual precision, higher for creativity) and employ chunking strategies to fit context windows efficiently. Pre-filter irrelevant retrieved data to reduce noise, and test system prompts to enforce output formatting rules. Implement retries with exponential backoff for reliability, and cache frequent queries to minimize repeat processing.
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
What Have You Learned?
By diving into this tutorial, you’ve unlocked the magic of building a RAG system from the ground up! You learned how Haystack acts as the backbone, seamlessly stitching together each component into a powerful pipeline. OpenSearch steps in as your trusty vector database, storing and retrieving embeddings at lightning speed, while Hugging Face’s all-MiniLM-L12-v1 model transforms text into rich, meaningful vectors—turning messy data into structured knowledge. Then there’s Amazon Bedrock’s Claude 3 Haiku, the LLM superstar that takes those retrieved snippets and crafts human-like responses, making your application feel almost conversational. Together, these tools create a symphony of intelligence: data flows in, gets processed into embeddings, is stored for quick access, and finally fuels Claude’s ability to generate precise, context-aware answers. Plus, you picked up pro tips like optimizing chunk sizes for better retrieval and balancing cost with performance, even discovering a free RAG cost calculator to keep your projects budget-friendly!
Now that you’ve seen how these pieces click into place, imagine what’s next! You’re not just following steps—you’re equipped to innovate. Tweak parameters, experiment with different models, or scale your system to handle mountains of data. The tutorial gave you the blueprint, but your creativity will bring it to life. Whether you’re building a customer support bot, a research assistant, or something entirely new, the tools are in your hands. So go ahead—start coding, iterate fearlessly, and watch your ideas take shape. The future of smart applications is yours to build, optimize, and reinvent. Let’s get those keyboards clacking and make 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
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 Amazon Bedrock Claude 3 Haiku
- Step 3: Install and Set Up HuggingFace all-MiniLM-L12-v1
- 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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