Build RAG Chatbot with Haystack, Haystack In-memory store, Amazon Bedrock Claude 3 Haiku, and BAAI bge-m3

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 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.
BAAI bge-m3: A multilingual embedding model optimized for dense retrieval and semantic representation across 100+ languages. It excels in handling long texts, multi-task learning, and cross-lingual applications, offering high accuracy and scalability. Ideal for multilingual search engines, content recommendation systems, and enterprise-grade text analysis requiring robust semantic understanding.

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 BAAI bge-m3

from haystack import Document
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.embedders import SentenceTransformersTextEmbedder

doc_embedder = SentenceTransformersDocumentEmbedder(model="BAAI/bge-m3")
doc_embedder.warm_up()
text_embedder = SentenceTransformersTextEmbedder(model="BAAI/bge-m3")
text_embedder.warm_up()

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 = SentenceTransformersTextEmbedder(model="BAAI/bge-m3")
text_embedder.warm_up()

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 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.

BAAI bge-m3 optimization tips

To optimize BAAI bge-m3 in a RAG setup, fine-tune the model on domain-specific data to enhance retrieval relevance. Use dynamic chunking (e.g., 256-512 tokens) for balanced context capture and computational efficiency. Normalize embeddings before similarity scoring to reduce bias. Enable mixed-precision inference for faster processing without significant accuracy loss. Experiment with pooling strategies like CLS or mean-pooling for dense vectors. Regularly update the retrieval corpus to reflect new data, and apply batch processing during inference to maximize GPU utilization. Monitor embedding quality via recall@k metrics to refine parameters iteratively.

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, seamlessly orchestrating workflows to connect your data, models, and user interactions. The Haystack In-memory store became your go-to vector database, handling lightning-fast retrieval of relevant information by storing embeddings generated by the BAAI bge-m3 model—a multilingual powerhouse that captures semantic meaning with precision. Then, Amazon Bedrock’s Claude 3 Haiku stepped in as your LLM maestro, transforming retrieved context into human-like responses that feel both natural and insightful. Together, these tools form a dynamic pipeline: your data gets embedded, stored, retrieved, and synthesized into answers that balance accuracy and creativity. You even explored optimizations like chunking strategies and metadata filtering to fine-tune performance, ensuring your RAG system isn’t just functional but efficient and cost-effective—especially with the handy free RAG cost calculator to help you budget smartly.

Now that you’ve seen how these pieces fit together, imagine the possibilities! Whether you’re building chatbots, research assistants, or custom knowledge bases, you’ve got the tools to innovate. The tutorial didn’t just teach you steps—it handed you a playbook for experimentation. Tweak retrieval thresholds, experiment with hybrid search, or swap in different models to see what sparks. Remember, every optimization you apply or feature you add pushes the boundaries of what RAG can do. So go ahead—build something that surprises you, solves a real problem, or delights users. The future of intelligent applications is yours to shape, one query at a time. Let’s get coding, optimizing, and creating! 🚀

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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