Build RAG Chatbot with Haystack, Haystack In-memory store, Anthropic Claude 3.7 Sonnet, and Mistral Embed

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.)
Anthropic Claude 3.7 Sonnet: A versatile multimodal AI model optimized for enterprise applications, excelling in complex reasoning, multilingual tasks, and data analysis. Its strengths include rapid processing, high accuracy, and robust contextual understanding, ideal for content generation, customer support automation, R&D acceleration, and scalable AI-driven workflows. Combines performance with cost-efficiency for real-world deployment.
Mistral Embed: A high-performance embedding model designed to convert text into dense vector representations, capturing semantic meaning for tasks like retrieval, clustering, and similarity analysis. It excels in efficiency, multilingual support, and scalability, making it ideal for semantic search engines, multilingual content organization, and large-scale data processing applications requiring rapid, context-aware text analysis.

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 Anthropic Claude 3.7 Sonnet

To use Anthropic models, you need an Anthropic API key. You can provide this key in one of the following ways:

The recommended approach is to set it as the ANTHROPIC_API_KEY environment variable.
Alternatively, you can pass it directly when initializing the component using Haystack’s Secret API: Secret.from_token("your-api-key-here").

When configuring Anthropic models, make sure to define the Anthropic model you want to use by specifying it in the model parameter.

This component generates text based on a given prompt. Additionally, you can customize the generation process by providing extra parameters available in the Anthropic Messaging API. These parameters can be passed using generation_kwargs, either during initialization or when calling the run() method. To explore all available options, refer to the Anthropic documentation.

Finally, the run() method requires a single string as input to generate text.

Now let's install the anthropic-haystack package to use the AnthropicGenerator:

pip install anthropic-haystack

from haystack_integrations.components.generators.anthropic import AnthropicGenerator

generator = AnthropicGenerator(model="claude-3-7-sonnet-latest")

Step 3: Install and Set Up Mistral Embed

pip install mistral-haystack

from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
from haystack import Document
from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder

text_embedder = MistralTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="mistral-embed")
document_embedder = MistralDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="mistral-embed")

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 = MistralTextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="mistral-embed")

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

Anthropic Claude 3.7 Sonnet optimization tips

Optimize prompts by structuring retrieved context with clear XML tags or markdown headers to enhance Claude’s parsing efficiency. Use dynamic context window management, prioritizing high-relevance chunks while truncating redundant content. Implement query-aware document reranking to surface critical information early. Fine-tune temperature (0.2-0.5) for factual consistency and enable streaming for latency-sensitive applications. Cache frequent query embeddings to reduce reprocessing overhead. Leverage system prompts to enforce response constraints and validate outputs against retrieval sources to mitigate hallucinations. Monitor token usage per API call to balance cost and performance.

Mistral Embed optimization tips

To optimize Mistral Embed in a RAG setup, preprocess text by removing redundant whitespace, special characters, and normalizing casing to reduce embedding noise. Use batch processing for bulk embeddings to leverage GPU parallelism. Fine-tune Mistral Embed on domain-specific data if retrieval accuracy is low. Reduce input sequence length via truncation or sliding windows for long documents. Cache frequent queries to save compute. Test different pooling strategies (mean, max) for sentence-level embeddings and normalize outputs to improve similarity scoring consistency.

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 the ground up—and that’s no small feat! You’ve seen how Haystack acts as the backbone, orchestrating every step of the pipeline with its flexible framework. The Haystack In-memory store stepped in as your lightning-fast vector database, keeping your data accessible and organized without the overhead of external services. Then came Anthropic Claude 3.7 Sonnet, the powerhouse LLM that transformed your queries into insightful, nuanced answers, proving just how smart and adaptable modern language models can be. And let’s not forget Mistral Embed, the embedding model that turned your raw text into rich, searchable vectors, bridging the gap between human language and machine understanding. Together, these tools showed you how to retrieve, process, and generate responses in a seamless flow—like building a conversation engine that learns from your data in real time!

But wait—there’s more! You’ve also picked up pro tips for optimizing your RAG pipeline, like tweaking chunking strategies and fine-tuning retrieval with metadata filters. And that free RAG cost calculator? It’s your secret weapon for balancing performance and budget, ensuring your projects stay efficient and scalable. Imagine what’s next: you’re now equipped to create chatbots that feel human, build knowledge bases that think, or even craft AI assistants tailored to niche industries. The possibilities are endless, and the tools are in your hands. So go ahead—dive into your own projects, experiment fearlessly, and let your creativity run wild. The future of intelligent applications is yours to shape. Start building, optimize like a pro, and let your RAG innovations shine! 🚀

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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