Build RAG Chatbot with Haystack, Haystack In-memory store, STACKIT E5-mistral-7b-instruct, and Cohere embed-english-v2.0

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.)
STACKIT E5-mistral-7b-instruct: A 7B-parameter instruction-tuned language model optimized for task-specific guidance and multi-turn dialogue. It excels in understanding complex prompts, generating coherent responses, and adapting to diverse applications like chatbots, automation, and content creation. Ideal for developers seeking efficient, scalable AI solutions with minimal computational overhead.
Cohere embed-english-v2.0: A powerful text embedding model designed to convert English text into high-dimensional vector representations. It excels at capturing semantic relationships, enabling tasks like semantic search, clustering, and text classification. Optimized for accuracy and scalability, it is ideal for applications requiring robust natural language understanding, such as recommendation systems, document retrieval, and retrieval-augmented generation (RAG) pipelines.

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 STACKIT E5-mistral-7b-instruct

STACKIT is the cloud and colocation provider of the Schwarz Group. We can use different models on its cloud services with ease through its API.

pip install stackit-haystack

from haystack_integrations.components.generators.stackit import STACKITChatGenerator
from haystack.dataclasses import ChatMessage

generator = STACKITChatGenerator(model="intfloat/e5-mistral-7b-instruct")

Step 3: Install and Set Up Cohere embed-english-v2.0

To start using this integration with Haystack, install it with:

pip install cohere-haystack

from haystack import Document
from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder
from haystack_integrations.components.embedders.cohere.text_embedder import CohereTextEmbedder

text_embedder = CohereTextEmbedder(model="embed-english-v2.0")
document_embedder = CohereDocumentEmbedder(model="embed-english-v2.0")

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 = CohereTextEmbedder(model="embed-english-v2.0")

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

STACKIT E5-mistral-7b-instruct optimization tips

To optimize STACKIT E5-mistral-7b-instruct in RAG, fine-tune the model on domain-specific data to align embeddings with retrieval tasks. Use dynamic chunking (256-512 tokens) for balanced context retention and computational efficiency. Apply quantization (e.g., 4-bit) to reduce memory usage without significant accuracy loss. Leverage instruction prefixes like "Retrieve relevant info for:" to sharpen focus. Implement cache layers for repetitive queries and prune low-scoring retrieved documents pre-generation. Monitor latency and adjust temperature (0.1-0.3) to balance determinism and creativity. Prioritize GPU memory optimization via mixed precision and kernel fusion.

Cohere embed-english-v3.0 optimization tips

To optimize Cohere embed-english-v3.0 in RAG, ensure input text is clean and concise—remove redundant whitespace, special characters, or irrelevant content. Use shorter chunks (e.g., 256-512 tokens) aligned with semantic boundaries to improve relevance. Batch embedding requests for efficiency. Fine-tune truncation settings to retain critical context. Pair with a low-latency vector database (e.g., FAISS or HNSW) and pre-filter noisy data. Monitor embedding quality via retrieval accuracy metrics (e.g., recall@k) and adjust chunking or preprocessing as needed. Leverage Cohere’s input_type parameter (search_document/search_query) for task-aware embeddings.

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 unlocked the magic of building a RAG system from scratch! This tutorial walked you through seamlessly integrating Haystack’s flexible framework, its lightning-fast in-memory vector store, STACKIT’s E5-mistral-7b-instruct for generating insightful responses, and Cohere’s embed-english-v2.0 to transform text into rich embeddings. Together, these tools form a powerhouse pipeline that ingests data, retrieves context, and crafts answers with precision. You’ve seen how Haystack orchestrates the entire flow—from loading documents and chunking text to embedding content and querying the vector database. The in-memory store’s speed ensures real-time retrieval, while Cohere’s embeddings capture nuanced meaning, making your system both smart and responsive. STACKIT’s LLM then ties it all together, leveraging retrieved context to deliver answers that feel human and deeply informed. Plus, those optimization tips—like tweaking chunk sizes, adjusting similarity thresholds, or using metadata filters—give you the keys to fine-tune performance for your unique use case. And let’s not forget the free RAG cost calculator, your new best friend for estimating expenses without surprises!

But this is just the beginning. You’re now equipped to create RAG applications that can transform how you handle information, whether it’s powering chatbots, enhancing research tools, or personalizing customer experiences. The knowledge you’ve gained isn’t just theoretical—it’s a launchpad. Experiment with different models, play with hybrid search strategies, or dive into advanced filtering techniques. Every tweak and iteration brings you closer to a system that’s uniquely yours. So what are you waiting for? Take these tools, mix in your creativity, and start building. Optimize fearlessly, innovate boldly, and watch as your ideas evolve into solutions that amaze. The world of intelligent applications is yours to shape—go make it happen!

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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