Build RAG Chatbot with Haystack, Haystack In-memory store, Google Vertex AI Gemini 2.0 Flash, and jina-embeddings-v3

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 Flash: A lightweight, high-speed AI model optimized for rapid inference and cost-effective scalability. It excels in real-time applications requiring low latency, such as chatbots, summarization, and data processing, balancing performance with efficiency for high-volume enterprise workloads on Google Cloud.
Jina-Embeddings-v3: A state-of-the-art embedding model designed for high-dimensional vector representations of text, excelling in multilingual understanding and long-context retention. Its scalable architecture ensures robust performance in semantic search, clustering, and retrieval-augmented generation (RAG) systems. Ideal for applications requiring precise semantic analysis across diverse languages and lengthy documents, combining accuracy with efficiency.

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 Flash

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-flash-001")

Step 3: Install and Set Up jina-embeddings-v3

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-embeddings-v3")
document_embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-embeddings-v3")

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 = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-embeddings-v3")

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 Flash optimization tips

To optimize Gemini 2.0 Flash in RAG, balance chunk size for retrieved documents—smaller chunks (200-400 tokens) improve precision, while larger ones preserve context. Use preprocessing to clean text, remove noise, and add metadata (e.g., timestamps) for relevance scoring. Adjust model parameters: lower temperature (0.2-0.5) for factual outputs, and limit max_output_tokens to reduce latency. Batch process embeddings to cut API calls. Cache frequent queries and responses. Fine-tune with domain-specific data if available. Monitor latency and cost via Vertex AI’s tools, and use asynchronous calls for non-blocking operations.

Jina-embeddings-v3 optimization tips

To optimize Jina-embeddings-v3 in a RAG setup, preprocess input text by normalizing casing, removing redundant whitespace, and truncating to the model’s maximum sequence length (e.g., 8,192 tokens). Batch embedding generation for parallel processing, balancing GPU/CPU memory constraints. Use FP16 precision for faster inference if hardware supports it. Cache frequently accessed document embeddings to reduce recomputation. Experiment with dimensionality reduction (e.g., PCA) if downstream tasks tolerate lower-dimensional vectors. Regularly update to the latest model version for performance improvements. Monitor latency and adjust batch sizes dynamically for throughput-latency trade-offs.

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 uncovered the magic of building a powerful RAG system from the ground up! You learned how to seamlessly integrate Haystack’s flexible framework with the Haystack In-memory store—a lightweight yet robust vector database—to manage and retrieve data efficiently. The tutorial showed you how Jina’s cutting-edge embedding model, jina-embeddings-v3, transforms text into rich, high-dimensional vectors, enabling your system to understand context and relationships in your data like never before. Then, by connecting Google’s Vertex AI Gemini 2.0 Flash, you saw how lightning-fast LLM capabilities can generate human-like responses, turning raw information into actionable insights. Together, these components form a dynamic pipeline that retrieves, understands, and synthesizes information with remarkable precision—proving just how accessible and impactful RAG technology can be!

But wait, there’s more! You also picked up pro tips for optimizing your system, like balancing speed and accuracy with chunking strategies or tweaking retrieval thresholds. And don’t forget the free RAG cost calculator introduced in the tutorial—a game-changer for budgeting your projects and scaling responsibly. Now that you’ve seen how these pieces fit together, imagine the possibilities! Whether you’re building smarter chatbots, enhancing research tools, or revolutionizing customer support, you’ve got the tools to innovate. So why wait? Start experimenting, tweak those parameters, and let your creativity run wild. The future of intelligent applications is yours to shape—go build something amazing and watch your ideas come to life! 🚀

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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