Build RAG Chatbot with Haystack, Pgvector, Anthropic Claude 3 Opus, and OpenAI text-embedding-3-small

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.
Pgvector: an open-source extension for PostgreSQL that enables efficient storage and querying of high-dimensional vector data, essential for machine learning and AI applications. Designed to handle embeddings, it supports fast approximate nearest neighbor (ANN) searches using algorithms like HNSW and IVFFlat. Since it is just a vector search add-on to traditional search rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer a much more scalable solution or hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)
Anthropic Claude 3 Opus: A state-of-the-art multimodal AI model designed for complex reasoning, advanced analysis, and nuanced content creation. Its strengths include exceptional contextual understanding, accuracy in technical or specialized domains, and ethical alignment. Ideal for strategic business planning, academic research, and sophisticated AI-driven applications requiring high-level cognitive capabilities.
OpenAI text-embedding-3-small: Designed to generate dense vector representations of text, this model excels in efficiency and cost-effectiveness, optimized for speed and low resource usage. It delivers competitive performance in semantic similarity, retrieval, and clustering tasks, making it ideal for large-scale applications like search engines, recommendation systems, and text classification where balancing accuracy with computational cost is crucial.

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 Opus

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-opus-latest")

Step 3: Install and Set Up OpenAI text-embedding-3-small

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})

document_embedder = OpenAIDocumentEmbedder(meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Now let's install and set up the model.

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder
from haystack.components.embedders import OpenAITextEmbedder

text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-3-small")
document_embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-3-small")

Step 4: Install and Set Up Pgvector

To quickly set up a PostgreSQL database with pgvector, you can use Docker:

docker run -d -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres -e POSTGRES_DB=postgres ankane/pgvector

To use pgvector with Haystack, install the pgvector-haystack integration:

pip install pgvector-haystack

import os
from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import PgvectorEmbeddingRetriever

os.environ["PG_CONN_STR"] = "postgresql://postgres:postgres@localhost:5432/postgres"

document_store = PgvectorDocumentStore()
retriever = PgvectorEmbeddingRetriever(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 = PgvectorEmbeddingRetriever(document_store=document_store) 
 text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"), model="text-embedding-3-small")

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.

pgvector optimization tips

To optimize pgvector in a Retrieval-Augmented Generation (RAG) setup, consider indexing your vectors using GiST or IVFFlat to significantly speed up search queries and improve retrieval performance. Make sure to leverage parallelization for query execution, allowing multiple queries to be processed simultaneously, especially for large datasets. Optimize memory usage by tuning the vector storage size and using compressed embeddings where possible. To further enhance query speed, implement pre-filtering techniques to narrow down search space before querying. Regularly rebuild indexes to ensure they are up to date with any new data. Fine-tune vectorization models to reduce dimensionality without sacrificing accuracy, thus improving both storage efficiency and retrieval times. Finally, manage resource allocation carefully, utilizing horizontal scaling for larger datasets and offloading intensive operations to dedicated processing units to maintain responsiveness during high-traffic periods.

Anthropic Claude 3 Opus optimization tips

To maximize Claude 3 Opus performance in RAG systems, fine-tune retrieval precision using hybrid search with dense vectors and keyword boosting to align with Opus' reasoning strengths. Structure retrieved context using XML tags for clear document boundaries, and prepend explicit instructions about source prioritization. Experiment with temperature (0.2-0.5) and max tokens to balance creativity vs focus. Implement query rewriting with Opus' own API to clarify ambiguous user inputs before retrieval. Batch process embeddings for frequent documents during indexing to reduce latency. Monitor output quality with hallucination checks against retrieved context.

OpenAI text-embedding-3-small optimization tips

Optimize input text by truncating or chunking to stay within the 8191-token limit while preserving semantic context. Normalize embeddings to unit vectors to improve cosine similarity accuracy. Batch embedding requests to reduce API calls and latency. Experiment with dimensionality reduction (e.g., 256-dim) to balance performance and storage costs. Preprocess text by removing redundant or noisy content and standardizing formats (lowercase, trimming whitespace). Cache frequent or static embeddings to avoid redundant computations. Monitor retrieval quality via metrics like recall@k and adjust chunking strategies or hybrid retrieval methods if needed. Fine-tune temperature and top-k parameters during generation to align with embedding outputs.

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 power of combining cutting-edge tools to build a robust RAG system! You learned how Haystack, the flexible framework, acts as the glue that seamlessly connects your components, orchestrating everything from data ingestion to answer generation. With Pgvector as your vector database, you saw how to store and retrieve embeddings efficiently, ensuring your system can quickly access relevant information even from massive datasets. The magic of OpenAI’s text-embedding-3-small transformed raw text into rich numerical representations, capturing the essence of your documents for smarter searches. And when it came time to generate human-like responses, Anthropic Claude 3 Opus stepped in, showcasing its knack for delivering nuanced, context-aware answers that feel almost conversational. Along the way, you picked up optimization tricks—like tweaking chunk sizes and balancing latency with accuracy—to fine-tune performance. Plus, the free RAG cost calculator gave you a practical tool to estimate expenses and scale wisely, so you can build smarter without breaking the bank.

Now that you’ve seen how these pieces fit together, imagine what you can create! Whether it’s a customer support assistant, a research tool, or a creative brainstorming partner, your RAG pipeline is ready to tackle real-world challenges. The skills you’ve gained here aren’t just theoretical—they’re a launchpad for innovation. So go ahead: experiment with different models, refine your retrieval strategies, and play with hybrid approaches. Every tweak brings you closer to a system that’s uniquely yours. The future of intelligent applications is in your hands, and you’ve got everything you need to start building. Let curiosity guide you, embrace the iterative process, and most importantly—have fun shaping the next generation of AI-powered solutions. The journey’s just beginning, and you’re leading the way! 🚀

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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