Build RAG Chatbot with Haystack, Haystack In-memory store, Cohere Command R, 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.
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.)
Cohere Command R: A scalable enterprise AI model optimized for Retrieval-Augmented Generation (RAG), designed to handle complex workflows with high accuracy. Strengths include multilingual support, low-latency performance, and secure integration with business data. Ideal for automating customer support, data analysis, and generating context-aware insights from large datasets.
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 Cohere Command R

To use Cohere models with Haystack for a RAG pipeline, you need to get a Cohere API Key first. You can write this key in:

The api_key init parameter using Secret API
The COHERE_API_KEY environment variable (recommended)

Now, let's install and set up the Cohere model.

pip install cohere-haystack

from haystack_integrations.components.generators.cohere import CohereGenerator

generator = CohereGenerator(model="command-r")

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 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-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.

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

Cohere Command R optimization tips

To optimize Cohere Command R in a RAG setup, fine-tune prompts for clarity and specificity, using explicit instructions to guide context-aware responses. Limit input context to relevant chunks (e.g., 256-512 tokens) to reduce noise and computational overhead. Adjust temperature and top-p values to balance creativity and factual accuracy—lower values enhance precision for retrieval tasks. Implement query augmentation (e.g., synonyms, rephrasing) to improve retrieval alignment. Use Cohere’s built-in reranking to prioritize high-confidence documents. Regularly validate outputs against source data to minimize hallucinations and ensure consistency. Profile latency and batch requests where possible for scalability.

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 taken a huge leap in understanding how to weave together cutting-edge tools to create a powerful RAG system! You learned how Haystack acts as the backbone, seamlessly orchestrating the entire pipeline, while the Haystack In-Memory Store provided a lightweight yet efficient vector database to store and retrieve embeddings at lightning speed. The magic of Cohere Command R shone through as it generated precise, context-aware responses, and the jina-colbert-v2 embedding model proved its mettle by transforming text into rich, semantic vectors that capture meaning far beyond keywords. Together, these components form a dynamic RAG pipeline that fetches relevant documents and crafts intelligent answers—like giving your app a supercharged brain! You also discovered practical optimization tricks, like adjusting chunk sizes for embeddings and balancing speed with accuracy, ensuring your system isn’t just powerful but also cost-effective. And hey, that free RAG cost calculator? It’s your secret weapon for planning scalable projects without breaking the bank.

Now that you’ve seen how these pieces fit together, imagine the doors this opens! Whether you’re building a smarter chatbot, a research assistant, or a knowledge hub, you’ve got the tools to make it happen. The tutorial didn’t just teach you steps—it handed you a blueprint for innovation. So what’s next? Tweak those parameters, experiment with new datasets, and let your creativity run wild. Every optimization you try, every feature you add, pushes the boundaries of what’s possible. The future of intelligent applications is literally at your fingertips, and you’re already equipped to shape it. Ready, set, build—your RAG-powered masterpiece is waiting to 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

We'd Love to Hear What You Think!

We’d love to hear your thoughts! 🌟 Leave your questions or comments below or join our vibrant Milvus Discord community to share your experiences, ask questions, or connect with thousands of AI enthusiasts. Your journey matters to us!

If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖