Build RAG Chatbot with Haystack, Haystack In-memory store, Amazon Bedrock Claude 3 Sonnet, and OpenAI text-embedding-3-small

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.
Haystack in-memory store: a very simple, in-memory document store with no extra services or dependencies. It is great for experimenting with Haystack, and we do not recommend using it for production. If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand offers a free tier supporting up to 1 million vectors.)
AmazonBedrock Claude 3 Sonnet: A versatile AI model optimized for enterprise workloads, balancing speed and intelligence. Excels in high-accuracy reasoning, multilingual support, and cost-efficiency. Ideal for data analysis, content generation, retrieval-augmented generation (RAG), workflow automation, and multilingual applications. Seamlessly integrates with AWS services for scalable, secure AI solutions.
OpenAI text-embedding-3-small: Designed to generate dense vector representations of text, this model excels in efficiency and cost-effectiveness, optimized for speed and low resource usage. It delivers competitive performance in semantic similarity, retrieval, and clustering tasks, making it ideal for large-scale applications like search engines, recommendation systems, and text classification where balancing accuracy with computational cost is crucial.

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 Sonnet

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-sonnet-20240229-v1:0")

Step 3: Install and Set Up OpenAI text-embedding-3-small

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})

document_embedder = OpenAIDocumentEmbedder(meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Now let's install and set up the model.

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder
from haystack.components.embedders import OpenAITextEmbedder

text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-3-small")
document_embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-3-small")

Step 4: Install and Set Up Haystack In-memory store

from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore()
retriever=InMemoryEmbeddingRetriever(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=InMemoryEmbeddingRetriever(document_store=document_store) 
 text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-3-small")

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.

Haystack in-memory store optimization tips

Haystack in-memory store is just a very simple, in-memory document store with no extra services or dependencies. We recommend that you just experiment it with RAG pipeline within your Haystack framework, and we do not recommend using it for production. If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand offers a free tier supporting up to 1 million vectors

AmazonBedrock Claude 3 Sonnet optimization tips

Optimize Claude 3 Sonnet in RAG by fine-tuning chunking strategies (e.g., 512-1024 tokens) to balance context relevance and processing speed. Use semantic similarity thresholds to filter retrieved documents, reducing noise. Leverage system prompts to enforce output structure (JSON/XML) and constrain responses to retrieved content. Adjust temperature (0.2-0.5) for deterministic yet creative answers. Cache frequent queries and pre-embed common terms to lower latency. Test with hybrid search (keyword + vector) and prioritize high-quality embeddings. Monitor Bedrock’s inference metrics (e.g., latency, error rates) to tweak batch sizes and concurrency limits for cost-performance balance.

OpenAI text-embedding-3-small optimization tips

Optimize input text by truncating or chunking to stay within the 8191-token limit while preserving semantic context. Normalize embeddings to unit vectors to improve cosine similarity accuracy. Batch embedding requests to reduce API calls and latency. Experiment with dimensionality reduction (e.g., 256-dim) to balance performance and storage costs. Preprocess text by removing redundant or noisy content and standardizing formats (lowercase, trimming whitespace). Cache frequent or static embeddings to avoid redundant computations. Monitor retrieval quality via metrics like recall@k and adjust chunking strategies or hybrid retrieval methods if needed. Fine-tune temperature and top-k parameters during generation to align with embedding outputs.

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 building a RAG system from the ground up! You learned how Haystack acts as the backbone of your pipeline, seamlessly connecting components and orchestrating workflows. The Haystack In-memory store became your go-to vector database, handling lightning-fast storage and retrieval of embeddings, proving that even simple tools can pack a punch when integrated smartly. You saw how the OpenAI text-embedding-3-small model transformed raw text into meaningful vectors, turning unstructured data into searchable knowledge. Then, Amazon Bedrock’s Claude 3 Sonnet stepped in as the LLM powerhouse, generating human-like responses by synthesizing retrieved context with creativity and precision. Together, these tools formed a dynamic RAG pipeline that’s both flexible and scalable—ready to tackle everything from customer support bots to research assistants. Plus, you picked up pro tips for optimizing performance, like chunking strategies and hybrid search tweaks, and even discovered a free RAG cost calculator to keep your projects budget-friendly. How cool is that?

Now, armed with this knowledge, you’re not just following steps—you’re shaping the future of intelligent applications! The possibilities are endless: imagine enhancing your RAG system with real-time data, customizing prompts for niche domains, or experimenting with different embedding models and databases. Remember, every tweak you make brings you closer to a solution that’s uniquely yours. This tutorial was just the launchpad. The real magic happens when you start building, iterating, and pushing boundaries. So go ahead—fire up your IDE, play with those code snippets, and let your creativity run wild. The world of AI is evolving fast, and you have the tools to lead the charge. Build boldly, optimize fearlessly, and watch your ideas come to life. The next breakthrough RAG app? It could be yours. Let’s make it happen! 🚀

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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