Build RAG Chatbot with Haystack, Milvus, Anthropic Claude 3 Opus, and AmazonBedrock titan-embed-text-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.
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.
Anthropic Claude 3 Opus: A state-of-the-art multimodal AI model designed for complex reasoning, advanced analysis, and nuanced content creation. Its strengths include exceptional contextual understanding, accuracy in technical or specialized domains, and ethical alignment. Ideal for strategic business planning, academic research, and sophisticated AI-driven applications requiring high-level cognitive capabilities.
AmazonBedrock Titan-Embed-Text-v1: A high-performance embedding model designed to convert text into dense vector representations, enabling semantic search, clustering, and retrieval tasks. Strengths include scalability, multilingual support, and robust accuracy. Ideal for enterprise applications like recommendation systems, document similarity analysis, and AI-driven search engines within AWS environments.

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 Anthropic Claude 3 Opus

To use Anthropic models, you need an Anthropic API key. You can provide this key in one of the following ways:

The recommended approach is to set it as the ANTHROPIC_API_KEY environment variable.
Alternatively, you can pass it directly when initializing the component using Haystack’s Secret API: Secret.from_token("your-api-key-here").

When configuring Anthropic models, make sure to define the Anthropic model you want to use by specifying it in the model parameter.

This component generates text based on a given prompt. Additionally, you can customize the generation process by providing extra parameters available in the Anthropic Messaging API. These parameters can be passed using generation_kwargs, either during initialization or when calling the run() method. To explore all available options, refer to the Anthropic documentation.

Finally, the run() method requires a single string as input to generate text.

Now let's install the anthropic-haystack package to use the AnthropicGenerator:

pip install anthropic-haystack

from haystack_integrations.components.generators.anthropic import AnthropicGenerator

generator = AnthropicGenerator(model="claude-3-opus-latest")

Step 3: Install and Set Up AmazonBedrock titan-embed-text-v1

Amazon Bedrock is a fully managed service that makes high-performing foundation models from leading AI startups and Amazon available through a unified API.

To use embedding models on Amazon Bedrock for text and document embedding together with Haystack, you need to initialize an AmazonBedrockTextEmbedder and AmazonBedrockDocumentEmbedderwith the model name, the AWS credentials (aws_access_key_id, aws_secret_access_key, and aws_region_name) 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

import os
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockTextEmbedder
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockDocumentEmbedder
from haystack.dataclasses import Document

os.environ["AWS_ACCESS_KEY_ID"] = "..."
os.environ["AWS_SECRET_ACCESS_KEY"] = "..."
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # just an example

text_embedder = AmazonBedrockTextEmbedder(model="amazon.titan-embed-text-v1",
                                                                                                                                                                        input_type="search_query"

document_embedder = AmazonBedrockDocumentEmbedder(model="amazon.titan-embed-text-v1",
                                                                                                                                                                        input_type="search_document"

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 = AmazonBedrockTextEmbedder(model="amazon.titan-embed-text-v1",
                                                                                                                                                                        input_type="search_query"

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.

Anthropic Claude 3 Opus optimization tips

To maximize Claude 3 Opus performance in RAG systems, fine-tune retrieval precision using hybrid search with dense vectors and keyword boosting to align with Opus' reasoning strengths. Structure retrieved context using XML tags for clear document boundaries, and prepend explicit instructions about source prioritization. Experiment with temperature (0.2-0.5) and max tokens to balance creativity vs focus. Implement query rewriting with Opus' own API to clarify ambiguous user inputs before retrieval. Batch process embeddings for frequent documents during indexing to reduce latency. Monitor output quality with hallucination checks against retrieved context.

AmazonBedrock titan-embed-text-v1 optimization tips

To optimize titan-embed-text-v1 in a RAG setup, preprocess inputs by removing redundant whitespace and truncating excessively long texts to fit its 8K-token limit. Use batch embedding requests to reduce latency and costs. Fine-tune chunking strategies to balance context retention (e.g., 512-token segments) and avoid fragmentation. Normalize embeddings to improve retrieval accuracy. Leverage metadata filtering to refine retrieved results. Test newer model versions for performance gains. Cache frequent or repeated queries to minimize redundant computations. Monitor embedding quality via cosine similarity thresholds and adjust retrieval thresholds dynamically.

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 combining cutting-edge tools to build a RAG system from the ground up! You learned how Haystack acts as the orchestration framework, seamlessly connecting your pipeline components while handling tasks like chunking, routing, and response formatting. Milvus stepped in as your high-performance vector database, blazing through similarity searches to find the most relevant contextual data for your queries. The magic truly came alive when Anthropic Claude 3 Opus (via Amazon Bedrock) transformed those retrieved snippets into human-like responses, showcasing its knack for reasoning and nuanced language generation. And let’s not forget the Titan Embeddings Model, which turned your raw text into rich numerical representations, making semantic search possible in the first place! Together, these tools form a dynamic pipeline that balances speed, accuracy, and natural interaction—proving that sophisticated AI applications are within your reach.

But this tutorial didn’t stop at the basics! You also discovered pro tips for optimizing each layer—like tuning Milvus indexing parameters for faster searches or adjusting Claude’s temperature setting for more creative outputs. The included free RAG cost calculator gave you real-world insights into managing expenses as you scale, ensuring your projects stay budget-friendly. Now that you’ve seen how these pieces click together like a well-oiled machine, it’s time to run wild with your ideas! Whether you’re building a research assistant, a customer support bot, or something entirely new, you’ve got the tools and knowledge to innovate. So fire up your IDE, experiment with different configurations, and watch as your RAG creations come to life—the future of intelligent applications is yours to shape! 🚀

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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