Build RAG Chatbot with Haystack, Pgvector, OpenAI GPT-4o mini, and OpenAI text-embedding-3-large

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.)
OpenAI GPT-4o mini: A streamlined, cost-efficient variant of GPT-4, optimized for scalable AI applications. It balances high performance with reduced computational demands, offering fast response times and lower costs. Ideal for real-time chatbots, content generation, and integration into resource-constrained environments like mobile apps or high-volume transactional systems.
OpenAI text-embedding-3-large: A state-of-the-art embedding model designed to convert text into high-dimensional vectors, capturing deep semantic relationships. Renowned for its accuracy, scalability, and ability to handle long contexts (up to 8192 tokens), it excels in semantic search, retrieval-augmented generation (RAG), recommendation systems, and multilingual NLP tasks requiring nuanced language 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 OpenAI GPT-4o mini

To use OpenAI models, you need to get an OpenAI API key. The Haystack integration with OpenAI models uses an OPENAI_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key:

generator = OpenAIGenerator(api_key=Secret.from_token("<your-api-key>"), model="gpt-4o-mini")

Then, the generator component needs a prompt to operate, but you can pass any text generation parameters valid for the openai.ChatCompletion.create method directly to this component using the generation_kwargs parameter, both at initialization and to run() method. For more details on the parameters supported by the OpenAI API, refer to the OpenAI documentation.

Now let's install and set up OpenAI models.

from haystack.components.generators import OpenAIGenerator

generator = OpenAIGenerator(model="gpt-4o-mini", api_key=Secret.from_token("<your-api-key>"))

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

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

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-large")

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.

OpenAI GPT-4o Mini optimization tips

To optimize the OpenAI GPT-4o Mini in a RAG setup, ensure concise input formatting by truncating or summarizing retrieved documents to stay within token limits. Use precise query phrasing to improve retrieval relevance, and filter redundant context to reduce noise. Leverage temperature and max_tokens parameters to balance creativity and focus. Cache frequent queries to minimize API calls and latency. Regularly validate outputs against ground truth to refine prompts and retrieval logic. Prioritize structured templates for consistent responses and implement error handling for rate limits or timeouts.

OpenAI text-embedding-3-large optimization tips

Optimize OpenAI text-embedding-3-large in RAG by adjusting the dimensions parameter to balance accuracy and efficiency—lower values reduce latency and cost while retaining semantic relevance. Batch embedding requests to maximize throughput, preprocess text to remove noise (e.g., truncate to 8191 tokens, normalize whitespace), and cache frequent queries. Use cosine similarity for retrieval alignment, validate embeddings with domain-specific benchmarks, and fine-tune hybrid search strategies (e.g., combining sparse/dense vectors) to improve recall. Monitor API rate limits and leverage asynchronous calls for scalability.

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 acts as the backbone, seamlessly connecting components to craft a pipeline that’s both powerful and flexible. With Pgvector as your vector database, you discovered how to store and retrieve embeddings efficiently, turning unstructured data into searchable knowledge. The OpenAI text-embedding-3-large model became your secret weapon for transforming text into rich, high-dimensional vectors, capturing nuanced meanings that make your system smarter. Then, OpenAI GPT-4o mini stepped in as the brains of the operation, generating human-like responses by synthesizing retrieved context with creativity and precision. Together, these tools transformed raw data into dynamic, context-aware answers—like having a supercharged assistant that never stops learning!

But wait, there’s more! You also picked up pro tips for optimizing your RAG pipeline, like tweaking chunk sizes for better retrieval or fine-tuning query strategies to balance speed and accuracy. And let’s not forget the free RAG cost calculator—a game-changer for keeping your projects budget-friendly while scaling up. Imagine the possibilities now: chatbots that feel eerily human, research tools that dig deeper than ever, or personalized content engines that adapt on the fly. You’ve got the blueprint, the tools, and the know-how. So what’s next? It’s time to experiment, iterate, and let your creativity run wild. Build something bold, optimize fearlessly, and watch your ideas come to life. The future of intelligent apps is in your hands—go make it awesome! 🚀

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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