Build RAG Chatbot with Haystack, Haystack In-memory store, Google Vertex AI Gemini 2.0 Pro, and Ollama 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.)
Google Vertex AI Gemini 2.0 Pro: A multimodal AI model optimized for complex enterprise tasks, offering advanced reasoning, high accuracy, and scalability. It excels in processing text, code, and structured data, with seamless integration into Google Cloud. Ideal for data analysis, content generation, and automation in regulated industries, leveraging robust security and cloud-native deployment.
Ollama BGE-M3: A multilingual embedding model optimized for semantic understanding, retrieval, and clustering. Strengths include high accuracy across 100+ languages, robust performance in dense retrieval tasks, and scalability. Ideal for enterprise search systems, cross-lingual applications, and AI-driven knowledge management requiring precise semantic analysis.

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 Google Vertex AI Gemini 2.0 Pro

Using theVertexAIGeminiGenerator with Haystack requires authentication using Google Cloud Application Default Credentials (ADCs). This means your application must be set up with credentials that allow it to access Google Cloud services. If you're not sure how to configure ADCs, check the official Google documentation for setup instructions.

It's important to use a Google Cloud account that has the right permissions to access a project with Google Vertex AI endpoints. Without proper access, the generator won’t work as expected.

To find your project ID, you can either look it up in the Google Cloud Console under the resource manager or run the following command in your terminal.

Now let's install and set up this model.

pip install google-vertex-haystack

from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator

generator = VertexAIGeminiGenerator(model="gemini-2.0-pro-exp-02-05")

Step 3: Install and Set Up Ollama bge-m3

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="bge-m3")
document_embedder = OllamaDocumentEmbedder(model="bge-m3")

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="bge-m3")

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

Google Vertex AI Gemini 2.0 Pro optimization tips

To enhance performance in RAG, fine-tune Gemini 2.0 Pro’s context window by truncating irrelevant sections of retrieved documents, prioritize concise chunks (500-800 tokens) to balance detail and processing speed. Use structured prompts with explicit instructions (e.g., “Answer using ONLY the context below”) to reduce hallucinations. Enable low-temperature sampling (0.1–0.3) for factual accuracy, and leverage batch inference for parallel processing. Regularly validate outputs against retrieval quality, and use Vertex AI’s monitoring tools to track latency, token usage, and error rates for iterative tuning.

Ollama bge-m3 optimization tips

To optimize Ollama bge-m3 in a RAG setup, ensure input text is preprocessed (lowercasing, removing noise) and split into semantically coherent chunks (300-500 tokens) to balance retrieval accuracy and computational load. Use dynamic pooling to prioritize key phrases in embeddings. Fine-tune the model on domain-specific data with contrastive learning to enhance relevance. Adjust temperature and top-k sampling for controlled generation. Leverage batch inference for parallel processing and enable hardware acceleration (e.g., CUDA) for faster embeddings. Regularly validate retrieval performance with benchmark datasets to refine thresholds and ranking strategies.

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 seen how powerful it is to weave together cutting-edge tools like Haystack, Haystack’s In-Memory Store, Google Vertex AI Gemini 2.0 Pro, and Ollama’s bge-m3 embedding model to create a fully functional RAG system! This tutorial walked you through building a pipeline that thinks like a human but scales like a machine. You learned how Haystack acts as the backbone, seamlessly orchestrating workflows to fetch, process, and generate answers. The In-Memory Store became your lightning-fast vector database, keeping your data accessible and ready for real-time queries. Ollama’s bge-m3 embedding model transformed text into rich numerical representations, capturing meaning in ways that let your system understand context deeply. And Gemini 2.0 Pro? It’s the brain that turns those insights into natural, human-like responses—whether you’re answering questions, summarizing content, or sparking creative ideas. You even picked up pro tips for optimizing speed and accuracy, like tweaking chunk sizes and balancing precision with computational cost. Plus, that free RAG cost calculator you explored is a game-changer for keeping your projects budget-friendly without sacrificing power!

What’s next? You’ve got all the pieces to build something incredible. Imagine tailoring this pipeline to your own data, whether it’s customer support docs, research papers, or even your personal notes. The flexibility of these tools means you’re limited only by your creativity. Experiment with different embedding models, fine-tune Gemini’s outputs, or scale up with a persistent vector database when your project grows. Remember, every optimization you apply—like those nifty indexing tricks or hybrid search strategies—can turn a good system into a great one. So go ahead: dive in, iterate fearlessly, and watch your RAG applications come to life. The future of intelligent, responsive AI is yours to shape, one query at a time. Let’s build something amazing—you’ve totally got this! 🚀

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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