Build RAG Chatbot with Haystack, Zilliz Cloud, Amazon Bedrock Claude 3 Haiku, and jina-clip-v2

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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It 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.
Jina-CLIP-V2: A multimodal AI model designed to seamlessly connect text and visual data, excelling in cross-modal retrieval tasks. Strengths include high accuracy in image-text matching, multilingual support, and scalable architecture. Ideal for semantic image search, content moderation, and personalized recommendations in e-commerce or media platforms.

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 jina-clip-v2

pip install jina-haystack

from haystack_integrations.components.embedders.jina import JinaTextEmbedder
from haystack_integrations.components.embedders.jina import JinaDocumentEmbedder

text_embedder = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-clip-v2")
document_embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-clip-v2")

Step 4: Install and Set Up Zilliz Cloud

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": ZILLIZ_CLOUD_URI, "token": ZILLIZ_CLOUD_TOKEN}, 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 = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-clip-v2")

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

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.

Jina-CLIP-v2 optimization tips

To optimize Jina-CLIP-v2 in a RAG setup, preprocess inputs by cleaning text, normalizing formats, and truncating overly long documents to reduce noise. Use batch inference for embeddings to leverage GPU parallelism, and ensure model weights are quantized or pruned for faster inference. Cache frequently accessed embeddings to avoid redundant computations. Fine-tune the model on domain-specific data to improve retrieval relevance. Pair with efficient vector indexes (e.g., FAISS) for low-latency similarity searches, and monitor embedding quality via recall metrics to balance speed and accuracy. Regularly update the index with fresh data.

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 magic of building a modern RAG system from the ground up! You learned how Haystack acts as the flexible framework that orchestrates the entire pipeline, seamlessly connecting your data sources, processing workflows, and AI models. Zilliz Cloud stepped in as your powerhouse vector database, storing and retrieving embeddings at lightning speed, ensuring your RAG system can handle real-time queries with ease. Then came Amazon Bedrock’s Claude 3 Haiku, the LLM that transformed those retrieved contexts into human-like, coherent responses—balancing speed, accuracy, and cost-efficiency like a pro. And let’s not forget jina-clip-v2, the embedding model that turned your raw data into meaningful vectors, capturing semantic nuances so your system understands exactly what users are asking for. Together, these tools showed you how to bridge data retrieval with generative AI, creating a system that’s both intelligent and responsive. Plus, you picked up pro tips like optimizing chunk sizes and tuning retrieval parameters to squeeze every drop of performance out of your setup—and even got a free RAG cost calculator to keep your projects budget-friendly!

Now that you’ve seen how these pieces fit together, the real fun begins. Imagine the applications you could build—customer support bots that pull from vast knowledge bases, research assistants that synthesize papers in seconds, or creative tools that generate ideas grounded in real-world data. The best part? You’ve got everything you need to start experimenting. Tweak the models, play with different datasets, and watch your RAG system evolve. Don’t just stop here—optimize, iterate, and innovate. The future of AI-powered applications is in your hands, and this tutorial is just the launchpad. So fire up Zilliz Cloud, let Claude 3 Haiku flex its creative muscles, and build something awesome. Your next breakthrough is waiting—go 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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