Build RAG Chatbot with Haystack, Haystack In-memory store, Anthropic Claude 3 Sonnet, and BAAI bge-m3

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 Sonnet: A versatile AI model optimized for complex reasoning, multilingual tasks, and processing long-context inputs. It balances high performance with cost-efficiency, ideal for enterprise-scale applications like data analysis, real-time customer support, content creation, and research tasks requiring accuracy and scalability across diverse industries.
BAAI bge-m3: A multilingual embedding model optimized for dense retrieval and semantic representation across 100+ languages. It excels in handling long texts, multi-task learning, and cross-lingual applications, offering high accuracy and scalability. Ideal for multilingual search engines, content recommendation systems, and enterprise-grade text analysis requiring robust semantic understanding.

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 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-sonnet-20240229")

Step 3: Install and Set Up BAAI bge-m3

from haystack import Document
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.embedders import SentenceTransformersTextEmbedder

doc_embedder = SentenceTransformersDocumentEmbedder(model="BAAI/bge-m3")
doc_embedder.warm_up()
text_embedder = SentenceTransformersTextEmbedder(model="BAAI/bge-m3")
text_embedder.warm_up()

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 = SentenceTransformersTextEmbedder(model="BAAI/bge-m3")
text_embedder.warm_up()

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 Sonnet optimization tips

To optimize Claude 3 Sonnet in RAG workflows, refine retrieval chunk sizes to balance context relevance and token efficiency—aim for 500-800 token chunks with 15% overlap. Use structured prompts with XML tags or markdown to separate instructions from retrieved content, explicitly directing Claude to ground responses in provided sources. Lower temperature (0.2-0.4) improves factual consistency, while adding validation steps like “Verify this answer is fully supported by the context” reduces hallucinations. Prioritize system prompts to define response formats and enforce source citation. Test top-p (0.7-0.9) and max tokens to control output breadth without truncation.

BAAI bge-m3 optimization tips

To optimize BAAI bge-m3 in a RAG setup, fine-tune the model on domain-specific data to enhance retrieval relevance. Use dynamic chunking (e.g., 256-512 tokens) for balanced context capture and computational efficiency. Normalize embeddings before similarity scoring to reduce bias. Enable mixed-precision inference for faster processing without significant accuracy loss. Experiment with pooling strategies like CLS or mean-pooling for dense vectors. Regularly update the retrieval corpus to reflect new data, and apply batch processing during inference to maximize GPU utilization. Monitor embedding quality via recall@k metrics to refine parameters iteratively.

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 a powerful toolkit for building intelligent, context-aware applications! You learned how Haystack acts as the backbone of your RAG pipeline, seamlessly connecting components like a conductor guiding an orchestra. The Haystack In-memory Store became your lightweight, high-speed vector database, handling embeddings with ease—perfect for prototyping or scaling dynamically. Then came Anthropic Claude 3 Sonnet, the LLM superstar that transformed your retrieved context into eloquent, human-like responses, balancing depth with efficiency. And let’s not forget the BAAI bge-m3 embedding model, which turned raw text into rich numerical representations, ensuring your system understood nuance and relevance like never before. Together, these pieces formed a cohesive RAG system that retrieves, understands, and generates answers with precision—a thrilling blend of speed, accuracy, and creativity!

But wait, there’s more! You also discovered pro tips for optimizing performance, like tweaking chunk sizes for embeddings or balancing latency with quality. The free RAG cost calculator gave you a crystal ball to estimate expenses upfront, empowering smarter decisions. Now, imagine what’s next: refining your pipeline for niche use cases, experimenting with hybrid retrieval strategies, or even integrating multimodal data. The possibilities are endless, and you’re equipped to explore them all. So go ahead—build that dream app, optimize it to perfection, and let your creativity run wild. The future of AI-driven solutions is in your hands, and this is just the beginning. Ready, set, innovate! 🚀

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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