Build RAG Chatbot with Haystack, Milvus, Amazon Bedrock Claude 3.7 Sonnet, and Ollama snowflake-arctic-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.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
Amazon Bedrock Claude 3.7 Sonnet: A versatile multimodal AI model designed for enterprise applications, excelling in complex reasoning, code generation, and natural language tasks. Known for its balance of speed, accuracy, and cost-efficiency, it is ideal for data analysis, content creation, and scalable automation within AWS-integrated environments.
Ollama Snowflake-Arctic-Embed: A high-performance embedding model optimized for semantic understanding and retrieval tasks. It excels in generating dense vector representations for text, offering robust accuracy and scalability. Ideal for enterprise applications like semantic search, recommendation systems, and data clustering, particularly in environments leveraging Snowflake’s data ecosystem for seamless integration and large-scale analytics.

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 3.7 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-3-7-sonnet-20250219-v1:0")

Step 3: Install and Set Up Ollama snowflake-arctic-embed

pip install ollama-haystack

Make sure that you have a running Ollama model (either through a docker container, or locally hosted). No other configuration is necessary as Ollama has the embedding API built in.

from haystack import Document
from haystack_integrations.components.embedders.ollama import OllamaDocumentEmbedder
from haystack_integrations.components.embedders.ollama import OllamaTextEmbedder

text_embedder = OllamaTextEmbedder(model="snowflake-arctic-embed")
document_embedder = OllamaDocumentEmbedder(model="snowflake-arctic-embed")

Step 4: Install and Set Up Milvus

pip install --upgrade pymilvus milvus-haystack

from milvus_haystack import MilvusDocumentStore
from milvus_haystack.milvus_embedding_retriever import MilvusEmbeddingRetriever

document_store = MilvusDocumentStore(connection_args={"uri": "./milvus.db"}, drop_old=True,)
retriever = MilvusEmbeddingRetriever(document_store=document_store, top_k=3)

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 = MilvusEmbeddingRetriever(document_store=document_store, top_k=3) 
 text_embedder = OllamaTextEmbedder(model="snowflake-arctic-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.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

AmazonBedrock Claude 3.7 Sonnet optimization tips

Optimize Claude 3.7 Sonnet in RAG by preprocessing data to remove noise, using semantic chunking (512-1024 tokens) with overlap for context continuity. Adjust temperature (0.3–0.6 balances creativity) and max_tokens to control response length. Structure prompts with clear instructions and retrieval context placement. Cache frequent queries and embeddings to reduce latency. Apply metadata filters during retrieval to improve relevance. Monitor precision/recall via built-in metrics and tweak retrieval thresholds. Use batch inference for cost efficiency and parallelize document processing. Test varying chunk sizes, top-k retrievals, and system prompts to balance accuracy, speed, and operational costs.

Ollama Snowflake-Arctic-Embed optimization tips

To optimize Ollama Snowflake-Arctic-Embed in a RAG setup, ensure input text is cleanly chunked (e.g., 256-512 tokens) to align with its context window. Use batch processing for embeddings to reduce latency, and leverage hardware acceleration (e.g., CUDA for GPUs). Fine-tune with domain-specific data to improve retrieval relevance. Quantize the model for faster inference with minimal accuracy loss. Cache frequently accessed embeddings, and experiment with dimensionality reduction techniques like PCA if storage or speed constraints exist. Regularly validate embedding quality using similarity benchmarks.

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 modern RAG system from the ground up! You learned how to seamlessly integrate Haystack as the orchestration framework, Milvus as your lightning-fast vector database, Amazon Bedrock’s Claude 3 Sonnet as the reasoning powerhouse, and Ollama’s Snowflake-Arctic-Embed model to transform text into rich, searchable embeddings. Each component plays a critical role: Haystack acts as the glue, Milvus stores and retrieves vectors efficiently, Claude 3 generates human-like responses, and Snowflake-Arctic-Embed ensures your data is semantically mapped for precision. Together, they form a pipeline that supercharges applications—from chatbots that really understand context to search engines that feel almost psychic. The tutorial even threw in pro tips like optimizing chunk sizes and tweaking similarity thresholds to squeeze every drop of performance from your system, plus a free RAG cost calculator to keep your budget in check while experimenting.

Now that you’ve seen how these pieces fit together, the real magic begins—it’s your turn to innovate! Whether you’re refining workflows, experimenting with hybrid search strategies, or scaling up for enterprise use cases, you’ve got the tools to build smarter, faster, and more intuitive AI solutions. Remember, every tweak you make could unlock a new level of user experience. So fire up your IDE, play with those parameters, and let your creativity run wild. The future of intelligent applications is in your hands—go break things, learn wildly, and build something unforgettable. The RAG revolution is just getting started, and you’re already leading the charge! 🚀

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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