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Build an AI Search Engine with Nuclear DB for ChatGPT

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Introduction

Hi, I'm Carmen, an engineer here at Nuclear. In this tutorial, I will guide You through the process of performing semantic search using Nuclear DB. Semantic search is becoming increasingly important in the field of natural language processing, as it allows users to search for information that is similar in meaning rather than just Based on keywords. Nuclear DB, our open-source vector database built in Python, provides a seamless solution for storing and searching text vectors.

What is Semantic Search?

Semantic search is a technique that aims to understand the meaning behind a user's search query and retrieve Relevant information based on that meaning. Traditional keyword-based search often falls short in delivering accurate results, as it does not take into account the Context or intent behind the search. With semantic search, we can overcome this limitation and provide more relevant and context-aware search results.

Why Use Nuclear DB for Semantic Search?

Nuclear DB is a powerful vector database that offers native support for semantic search. Built in Python and trusted by many developers, Nuclear DB provides an efficient and user-friendly interface to store text vectors and perform advanced searches. With its out-of-the-box support for semantic search, Nuclear DB makes it easy to implement this powerful technique in your applications.

Installing Dependencies

To get started with semantic search using Nuclear DB, we first need to install the necessary dependencies. Fortunately, we don't need a lot of external libraries for this task. We will be using the Nuclear DB Python SDK to access Nuclear DB, Sentence Transformers for encoding text vectors, and the Data Sets library from Hugging Face to load our data. Let's install these dependencies using pip:

pip install nuclear-dbsdk sentence-transformers datasets

Setting Up Nuclear DB

Before we can proceed, we need to ensure that Nuclear DB is up and running. If you already have Nuclear DB running locally, you can skip this step. Otherwise, you have two options: either start the Nuclear DB Docker image or install it using pip. Let's go with the pip installation for simplicity:

pip install nuclear-db
nuclear-db

Once Nuclear DB is running, we need to verify that the service is running correctly. We can use the requests library to send a GET request to the local host and check for a 200 response:

import requests

response = requests.get('http://localhost:8000')
if response.status_code == 200:
    print('Nuclear DB is running correctly.')
else:
    print('There was an issue with Nuclear DB.')

If we see the "Nuclear DB is running correctly" message, we can proceed to the next steps.

Loading the Dataset

To perform semantic search, we need a dataset of Prompts or queries. In this tutorial, I will be using the DPT prompts dataset from Hugging Face. However, feel free to use any dataset that suits your needs. Let's load the dataset and take a quick look at its Contents:

from datasets import load_dataset

dataset = load_dataset('dpt_prompts')

print(dataset)

The dataset consists of a collection of prompts, where each prompt describes a certain task or instruction. We will be using the 'prompt' field for our semantic search.

Loading the Model

In order to obtain text vectors for our prompts, we need to load a pre-trained model. For this tutorial, we will be using the MS MARCO model, which is commonly used in information retrieval tasks. Let's load the model:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('msmarco-distilbert-base-v3')

With the model loaded, we can move on to the next step.

Populating the Database

To perform semantic search, we need to populate our Nuclear DB with the vectors of our prompts. For each prompt in our dataset, we will upload the prompt text and encode it using the loaded model. This process might take some time depending on the dataset size, so feel free to grab a cup of coffee while we populate the database:

import nuclear

nuclear.initialize('localhost')

knowledge_box = nuclear.Box("prompts")

for prompt in dataset['prompt']:
    vector = model.encode(prompt)
    knowledge_box.push((prompt, vector))

print("Database population completed.")

Once the database population is completed, we can move on to the final step.

Performing Semantic Search

Now that we have everything set up, it's time to perform semantic search on our populated database. Let's say we want to find prompts related to coding, art, emotions, and language learning. We start by converting our search queries into vectors using the same model we used to encode our prompts. Then, we pass these vectors to the Nuclear DB search function to retrieve the most similar prompts:

search_queries = ["coding", "art", "emotions", "language learning"]

query_vectors = [model.encode(query) for query in search_queries]

results = knowledge_box.search(query_vectors)

for i, query in enumerate(search_queries):
    print(f"Results for query '{query}':")
    print(results[i])

The results will consist of a list of prompts that are most similar to each search query. You can customize the number of results returned by setting the k parameter in the search function.

Results and Conclusion

After performing semantic search on our Nuclear DB, we obtained a variety of prompts related to coding, art, emotions, and language learning. Some of the results include developer-related prompts, language detection, emotions in writing, and even an AI writing tutor. These results demonstrate the power and usefulness of semantic search in finding semantically related information.

In conclusion, Nuclear DB provides a seamless solution for performing semantic search. With its easy-to-use Python API and support for popular libraries like Sentence Transformers, Nuclear DB is a practical choice for implementing sophisticated search functionality in your applications. Give it a try and explore the possibilities of semantic search!

Highlights:

Perform semantic search using Nuclear DB
Understand the meaning behind user search queries
Retrieve relevant information based on meaning
Nuclear DB is an open-source vector database
Built in Python and trusted by developers
Install dependencies: Nuclear DB SDK, Sentence Transformers, and Data Sets
Set up Nuclear DB locally
Load the dataset and model
Populate Nuclear DB with prompt vectors
Perform semantic search and retrieve similar prompts
Explore a wide range of applications for semantic search
Nuclear DB simplifies the implementation of semantic search

FAQ:

Q: What is semantic search? A: Semantic search is a technique that aims to understand the meaning behind a user's search query and retrieve relevant information based on that meaning.

Q: Why should I use Nuclear DB for semantic search? A: Nuclear DB is a powerful vector database that provides native support for semantic search. It offers an efficient and user-friendly interface for storing and searching text vectors.

Q: How do I install the dependencies for semantic search using Nuclear DB? A: You can install the necessary dependencies, including Nuclear DB SDK, Sentence Transformers, and Data Sets, using the pip package manager.

Q: How long does it take to populate the database in Nuclear DB? A: The time taken to populate the database depends on the size of the dataset. It is recommended to be patient and allow enough time for the process to complete.

Q: Can I customize the number of results returned by the semantic search? A: Yes, you can customize the number of results returned by setting the k parameter in the search function of Nuclear DB.

Q: What are some practical applications of semantic search? A: Semantic search can be applied in various domains, such as information retrieval, recommendation systems, chatbots, and more. It can improve the accuracy and relevance of search results.

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