Build RAG Chatbot with Haystack, Haystack In-memory store, OpenAI GPT-o1, and Ollama all-minilm

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.
Ollama all-minilm: A compact language model optimized for efficient NLP tasks, offering fast inference and low resource consumption. Strengths include scalability, versatility in text processing, and seamless integration. Ideal for real-time applications, edge deployments, and scenarios requiring lightweight yet robust language 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 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 Ollama all-minilm

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="all-minilm")
document_embedder = OllamaDocumentEmbedder(model="all-minilm")

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 = OllamaTextEmbedder(model="all-minilm")

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.

Ollama all-minilm optimization tips

Optimize Ollama all-minilm in RAG by preprocessing input text into concise, semantically meaningful chunks (256-512 tokens) to align with its context window. Use metadata filtering during retrieval to prioritize relevant documents and reduce noise. Fine-tune the model on domain-specific data to enhance answer relevance. Adjust temperature (0.2-0.5) and top-p (0.85-0.95) for balanced creativity and accuracy. Cache frequent queries to reduce latency, and employ batch processing for parallel inference. Quantize the model to 8-bit for faster inference with minimal accuracy loss. Regularly evaluate retrieval hit rates and answer quality to iteratively refine the pipeline.

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 RAG system from the ground up! You learned how Haystack, the flexible framework, acts as the backbone of your pipeline, seamlessly connecting components like the Haystack In-Memory Store (your lightweight vector database) to store and retrieve documents efficiently. Pairing this with an embedding model like all-minilm transformed your raw text into rich vector representations, enabling lightning-fast semantic search. Then came the star of the show: the LLM (OpenAI GPT-4 or Ollama), which turned retrieved context into coherent, human-like answers. You saw firsthand how these pieces fit together—ingesting data, querying it intelligently, and generating responses that feel almost magical. Plus, you picked up pro tips like optimizing chunk sizes for embeddings and balancing speed with accuracy, ensuring your RAG system isn’t just functional but exceptional.

But wait—there’s more! You also discovered tools like the free RAG cost calculator to estimate expenses and tweak your setup for budget-friendly scaling. Now that you’ve seen how powerful yet approachable RAG can be, imagine what’s next! Whether you’re enhancing customer support bots, building research assistants, or creating personalized learning tools, you’ve got the skills to innovate. So go ahead—experiment, iterate, and let your creativity run wild. The future of intelligent applications is in your hands, and every line of code you write brings it closer. Ready, set, build! 🚀

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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