Build RAG Chatbot with Haystack, OpenSearch, Google Vertex AI Gemini 1.5 Pro, and jina-colbert-v2

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.
OpenSearch: An open-source search and analytics suite derived from Elasticsearch. It offers robust full-text search and real-time analytics, with vector search available as an add-on for similarity-based queries, extending its capabilities to handle high-dimensional data. Since it is just a vector search add-on rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer a much more scalable solution or hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)
Google Vertex AI Gemini 1.5 Pro: A multimodal AI model optimized for processing text, images, and code with a 1-million-token context window, enabling deep analysis of complex inputs. Strengths include scalability, high accuracy, and seamless Google Cloud integration. Ideal for enterprise applications like advanced content generation, data-driven insights, and large-scale automation workflows.
Jina-ColBERT-v2: A dense passage retrieval model optimized for semantic search and document ranking. It combines ColBERT's contextualized late interaction with efficient indexing, delivering high accuracy in understanding query intent and matching relevant text. Ideal for large-scale enterprise search, Q&A systems, and content discovery platforms requiring nuanced semantic understanding and rapid retrieval.

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 1.5 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-1.5-pro")

Step 3: Install and Set Up jina-colbert-v2

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-colbert-v2")
document_embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-colbert-v2")

Step 4: Install and Set Up OpenSearch

If you have Docker set up, we recommend pulling the Docker image and running it.

docker pull opensearchproject/opensearch:2.11.0
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" opensearchproject/opensearch:2.11.0

Once you have a running OpenSearch instance, install the opensearch-haystack integration:

pip install opensearch-haystack

from haystack_integrations.components.retrievers.opensearch  import OpenSearchEmbeddingRetriever
from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore

document_store = OpenSearchDocumentStore(hosts="http://localhost:9200", use_ssl=True,
verify_certs=False, http_auth=("admin", "admin"))
retriever = OpenSearchEmbeddingRetriever(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 = OpenSearchEmbeddingRetriever(document_store=document_store) 
 text_embedder = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="jina-colbert-v2")

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.

OpenSearch optimization tips

To optimize OpenSearch in a Retrieval-Augmented Generation (RAG) setup, fine-tune indexing by enabling efficient mappings and reducing unnecessary stored fields. Use HNSW for vector search to speed up similarity queries while balancing recall and latency with appropriate ef_search and ef_construction values. Leverage shard and replica settings to distribute load effectively, and enable caching for frequent queries. Optimize text-based retrieval with BM25 tuning and custom analyzers for better relevance. Regularly monitor cluster health, index size, and query performance using OpenSearch Dashboards and adjust configurations accordingly.

Google Vertex AI Gemini 1.5 Pro optimization tips

To optimize Gemini 1.5 Pro in RAG, prioritize reducing input token usage by truncating or summarizing retrieved documents to fit its 1M-token context window efficiently. Use batch processing for parallel inference requests to minimize latency. Fine-tune temperature and top-k/p settings to balance creativity and factual accuracy. Leverage system prompts to enforce strict adherence to retrieved content, reducing hallucinations. Enable streaming for real-time responses and implement caching for frequent queries. Profile model performance with Vertex AI’s monitoring tools to adjust resource allocation and optimize costs. Test retrieval chunk sizes and embedding dimensions to align with Gemini’s input expectations.

jina-colbert-v2 optimization tips

To optimize jina-colbert-v2 in a RAG setup, ensure input text is preprocessed by truncating or chunking documents to fit its 512-token limit, preserving context. Use batch inference for dense embeddings to maximize GPU utilization. Fine-tune on domain-specific data to improve relevance. Leverage ColBERT’s late interaction by precomputing document embeddings and caching them for faster retrieval. Adjust the compression ratio for query-document token tensors to balance speed and accuracy. Filter irrelevant documents early using metadata to reduce computational overhead. Monitor retrieval latency and accuracy to iteratively refine parameters.

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 cutting-edge RAG system from the ground up! You now understand how Haystack acts as the flexible backbone, seamlessly orchestrating the flow of data between components. OpenSearch steps in as your trusty vector database, storing and retrieving context-rich embeddings generated by the lightning-fast jina-colbert-v2 model, which excels at capturing semantic meaning. At the heart of it all, Google’s Vertex AI Gemini 1.5 Pro brings its advanced reasoning capabilities to the table, synthesizing answers that feel human and insightful. You’ve seen firsthand how these pieces fit together—ingesting data, converting it into embeddings, querying with precision, and generating responses that balance accuracy with creativity. Plus, you’ve picked up pro tips like optimizing chunk sizes for better retrieval and tweaking prompts to guide Gemini’s outputs, ensuring your system is both efficient and effective. And let’s not forget the free RAG cost calculator—a game-changer for budgeting your projects without surprises!

Now that you’ve got this toolkit, imagine the possibilities! Whether you’re building a chatbot, a research assistant, or a knowledge hub, you’re equipped to innovate. The tutorial didn’t just teach you how to build—it showed you how to think like an architect, balancing performance, cost, and user experience. So why wait? Grab your code editor, experiment with different datasets, and see how small tweaks can lead to big breakthroughs. Share your creations, iterate fearlessly, and remember: every line of code you write is a step toward shaping the future of intelligent applications. The world of RAG is yours to explore—go make it awesome! 🚀

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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