Build RAG Chatbot with Haystack, Haystack In-memory store, OpenAI GPT-o1, 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.
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.)
OpenAI GPT-1: A foundational transformer-based language model designed for natural language understanding and generation. Strengths include coherent text generation, contextual comprehension, and adaptability to diverse NLP tasks. Ideal for text completion, basic conversational agents, and early-stage language research, serving as a precursor to more advanced models like GPT-3 and GPT-4.
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 OpenAI GPT-o1

To use OpenAI models, you need to get an OpenAI API key. The Haystack integration with OpenAI models uses an OPENAI_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key:

generator = OpenAIGenerator(api_key=Secret.from_token("<your-api-key>"), model="gpt-4o-mini")

Then, the generator component needs a prompt to operate, but you can pass any text generation parameters valid for the openai.ChatCompletion.create method directly to this component using the generation_kwargs parameter, both at initialization and to run() method. For more details on the parameters supported by the OpenAI API, refer to the OpenAI documentation.

Now let's install and set up OpenAI models.

from haystack.components.generators import OpenAIGenerator

generator = OpenAIGenerator(model="o1", api_key=Secret.from_token("<your-api-key>"))

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 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 = 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.

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

OpenAI GPT-01 optimization tips

To optimize OpenAI GPT-01 in a RAG setup, fine-tune prompts to include explicit instructions and structured context (e.g., “Answer using: [retrieved text]”). Limit response length with max_tokens to reduce verbosity and cost. Use a lower temperature (0.2–0.5) for factual accuracy. Preprocess retrieved documents to remove irrelevant content, ensuring inputs fit token limits. Cache frequent queries to minimize API calls. Experiment with chunking strategies for context injection and prioritize critical information at the prompt’s start or end. Monitor latency and adjust batch sizes for throughput efficiency.

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 now, you’ve unlocked the power of building a RAG system from the ground up! This tutorial showed you how to seamlessly weave together four critical components: the Haystack framework as your orchestration backbone, the Haystack In-Memory Store for lightning-fast vector storage and retrieval, OpenAI GPT-3 as your creative LLM powerhouse, and Amazon Bedrock’s Titan Embed Text v1 to transform text into rich, meaningful embeddings. You learned how Haystack’s flexibility lets you stitch these pieces into a cohesive pipeline—ingesting data, generating embeddings, storing them for instant access, and finally, using GPT-3 to craft intelligent, context-aware responses. Along the way, you saw how each component shines: Titan’s embeddings capture nuance, the in-memory store delivers speed, and GPT-3 turns retrieved knowledge into natural, human-like answers. Plus, you picked up pro tips for optimizing performance, like balancing cost and accuracy, and even discovered a free RAG cost calculator to keep your projects budget-friendly and scalable.

But this is just the beginning! You’re now equipped to build smarter, faster, and more efficient RAG applications. Whether you’re enhancing customer support bots, creating dynamic Q&A systems, or exploring uncharted AI use cases, the tools and techniques you’ve mastered today are your launchpad. Remember, every optimization tweak or creative experiment pushes the boundaries of what’s possible. So why wait? Dive into your next project with confidence, experiment fearlessly, and watch your ideas come to life. The future of intelligent applications is in your hands—start building, keep iterating, and let your innovations inspire the world! 🚀

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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