Build RAG Chatbot with Haystack, Haystack In-memory store, Cohere Command, and AmazonBedrock cohere embed-english-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.)
Cohere Command: A generative AI model designed for high-accuracy text generation and instruction-following, optimized for business applications. Strengths include coherent output, robust handling of complex prompts, and enterprise-grade reliability. Ideal for automating customer interactions, generating structured content (reports, emails), data extraction, and summarization, enhancing operational efficiency and scalability.
AmazonBedrock Cohere Embed-English-v3: A state-of-the-art text embedding model designed to convert English text into high-dimensional vector representations, excelling in semantic understanding and scalability. Its strengths include robust performance in semantic search, clustering, and retrieval-augmented generation (RAG), making it ideal for applications like recommendation systems, document similarity analysis, and AI-driven content organization within enterprise workflows.

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

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")

Step 3: Install and Set Up AmazonBedrock cohere embed-english-v3

Amazon Bedrock is a fully managed service that makes high-performing foundation models from leading AI startups and Amazon available through a unified API.

To use embedding models on Amazon Bedrock for text and document embedding together with Haystack, you need to initialize an AmazonBedrockTextEmbedder and AmazonBedrockDocumentEmbedderwith the model name, the AWS credentials (aws_access_key_id, aws_secret_access_key, and aws_region_name) should be set as environment variables, be configured as described above or passed as Secret arguments. Note, make sure the region you set supports Amazon Bedrock.

Now, let's start installing and setting up models with Amazon Bedrock.

pip install amazon-bedrock-haystack

import os
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockTextEmbedder
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockDocumentEmbedder
from haystack.dataclasses import Document

os.environ["AWS_ACCESS_KEY_ID"] = "..."
os.environ["AWS_SECRET_ACCESS_KEY"] = "..."
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # just an example

text_embedder = AmazonBedrockTextEmbedder(model="cohere.embed-english-v3",
                                                                                                                                                                        input_type="search_query"

document_embedder = AmazonBedrockDocumentEmbedder(model="cohere.embed-english-v3",
                                                                                                                                                                        input_type="search_document"

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 = AmazonBedrockTextEmbedder(model="cohere.embed-english-v3",
                                                                                                                                                                        input_type="search_query"

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 optimization tips

To optimize Cohere Command in a RAG setup, fine-tune parameters like temperature (lower for factual accuracy, higher for creativity) and top_p (narrow for precision). Use concise, structured input by chunking retrieved documents to fit context limits, and prepend clear instructions (e.g., "Answer using only the context"). Leverage Cohere’s built-in reranking to prioritize relevant passages. Regularly evaluate output quality with metrics like answer relevance and hallucination rates, and iteratively refine prompts and retrieval logic based on feedback.

AmazonBedrock Cohere Embed-English-v3 optimization tips

To optimize Cohere Embed-English-v3 in RAG, preprocess input text by removing redundant whitespace, normalizing casing, and filtering low-relevance content to reduce noise. Use batch embedding generation for bulk documents to minimize API calls and latency. Adjust the input_type parameter (e.g., "document" or "query") to align with use cases for context-aware embeddings. Experiment with chunk sizes (e.g., 256-512 tokens) to balance semantic capture and computational efficiency. Cache frequent or static embeddings to avoid reprocessing. Monitor embedding quality via cosine similarity checks and fine-tune retrieval thresholds for your dataset.

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 magic of building a RAG system from the ground up! You learned how Haystack, the flexible framework, acts as the backbone, orchestrating every step of the pipeline with elegance. The Haystack In-memory Store became your go-to vector database, effortlessly handling embeddings and enabling lightning-fast similarity searches. Then came Cohere Command, the powerhouse LLM that transformed your queries into insightful, human-like responses, while Amazon Bedrock’s Cohere Embed-English-v3 model turned raw text into rich, meaningful vectors—the secret sauce for context-aware retrieval. Together, these components formed a seamless flow: ingesting data, embedding it, retrieving relevant snippets, and generating answers that feel almost conversational. But it wasn’t just about connecting dots; you discovered optimization tricks like tweaking chunk sizes and adjusting similarity thresholds to boost performance. And let’s not forget the free RAG cost calculator—your new best friend for estimating expenses and making smart, budget-friendly choices without sacrificing quality.

Now that you’ve seen how these pieces click into place, imagine what’s next! You’ve got the tools to build RAG applications that answer questions, summarize content, or even power chatbots—all while keeping costs in check. Whether you’re scaling up with a different vector database or experimenting with other LLMs, the possibilities are endless. The tutorial didn’t just teach you to follow steps; it handed you a launchpad. So why wait? Start tinkering, optimize like a pro, and let your creativity run wild. Build something that surprises you, solves real problems, or sparks joy for others. The future of intelligent apps is yours to shape—go make it unforgettable! 🚀

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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