Build RAG Chatbot with Haystack, OpenSearch, Amazon Bedrock Claude 3 Opus, 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 Opus: A state-of-the-art multimodal AI model designed for complex reasoning, advanced data analysis, and high-stakes decision-making. Its strengths include deep contextual understanding, exceptional accuracy in processing text and images, and scalability for enterprise needs. Ideal for strategic planning, technical research, and sophisticated content generation requiring nuanced, ethical, and reliable outputs.
- 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 Opus
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-opus-20240229-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 Opus optimization tips
To optimize Claude 3 Opus in RAG, focus on efficient retrieval and context management. Use smaller, semantically dense document chunks (200-400 tokens) to improve relevance and reduce noise. Implement metadata filtering during retrieval to prioritize high-quality sources. Fine-tune prompts with explicit instructions to leverage Claude’s reasoning, such as asking for step-by-step analysis or citing retrieved passages. Adjust temperature and top-p settings to balance creativity and accuracy. Cache frequent queries to reduce latency and costs, and monitor token usage via Amazon Bedrock’s metrics to optimize chunk sizes or batch processing. Regularly validate outputs against ground-truth data to refine retrieval thresholds and prompt engineering.
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?
Wow, look how far you’ve come! By diving into this tutorial, you’ve unlocked the power to build a sophisticated RAG system from the ground up using cutting-edge tools. You learned how Haystack acts as the backbone, seamlessly orchestrating your pipeline to connect the dots between retrieval and generation. OpenSearch stepped in as your trusty vector database, storing and retrieving embeddings at lightning speed, while the HuggingFace all-MiniLM-L12-v1 model transformed text into rich numerical representations, making semantic search possible. Then came the star of the show: Amazon Bedrock’s Claude 3 Opus, which turned retrieved context into human-like, nuanced answers, proving why it’s a top-tier LLM. Together, these pieces formed a cohesive RAG pipeline that’s not just functional but intelligent—a system that understands queries, fetches relevant data, and crafts responses that feel almost magical. You even explored optimization tricks like tweaking chunk sizes, adjusting metadata filters, and balancing speed with accuracy to make your system lean and efficient. And let’s not forget the free RAG cost calculator you discovered—a handy tool to budget your experiments without surprises!
But this is just the beginning. You’ve now got the blueprint to create RAG applications that solve real problems, whether for research, customer support, or creative projects. The tools are yours to mix and match; imagine adding custom datasets, experimenting with different embedding models, or fine-tuning prompts to make Claude shine even brighter. The sky’s the limit! So what’s next? Start building. Tinker, optimize, and let your curiosity lead the way. Every tweak you make, every line of code you write, brings you closer to something groundbreaking. The future of AI-powered applications is in your hands—go shape it! 🚀
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 Opus
- 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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