Introduction

As artificial intelligence continues to advance, question-answering systems have become increasingly sophisticated. One of the key technologies driving this progress is the use of embeddings and vector databases. In this blog post, we'll explore how to build a simple yet effective question-answering system using these powerful tools.

Understanding Embeddings

Before we dive into building our system, let's briefly discuss what embeddings are and why they're useful in natural language processing tasks.

Embeddings are dense vector representations of words, phrases, or even entire documents. They capture semantic meaning in a way that allows machines to understand and process language more effectively. For example, in a well-trained embedding space, similar words or concepts will be closer together, while dissimilar ones will be farther apart.

The Components of Our Question-Answering System

Our simple question-answering system will consist of the following components:

1. A pre-trained embedding model
2. A vector database to store our knowledge base
3. A similarity search function
4. A user interface for input and output

Let's break down each of these components and see how they work together.

Step 1: Choosing a Pre-trained Embedding Model

For our system, we'll use a pre-trained embedding model to convert our text into vector representations. One popular choice is the Sentence-BERT (SBERT) model, which is specifically designed for sentence embeddings.

Here's how you can use the sentence-transformers library to load a pre-trained SBERT model:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')

This model will allow us to convert sentences or short paragraphs into 384-dimensional vectors.

Step 2: Creating a Vector Database

Next, we need a place to store our knowledge base. For this example, we'll use a simple in-memory vector database, but in a production environment, you might want to use a more robust solution like Faiss or Pinecone.

Let's create a simple function to add items to our database:

import numpy as np

vector_db = []

def add_to_db(text, embedding):
    vector_db.append((text, embedding))

Step 3: Populating the Database

Now that we have our database set up, let's add some sample knowledge to it:

knowledge = [
    "The capital of France is Paris.",
    "The Eiffel Tower is located in Paris.",
    "The Louvre Museum houses the Mona Lisa painting.",
    "The Seine River runs through Paris.",
]

for item in knowledge:
    embedding = model.encode(item)
    add_to_db(item, embedding)

Step 4: Implementing Similarity Search

To find the most relevant answer to a user's question, we'll use cosine similarity to compare the question's embedding with the embeddings in our database:

from scipy.spatial.distance import cosine

def find_most_similar(query_embedding):
    similarities = [1 - cosine(query_embedding, item[1]) for item in vector_db]
    most_similar_idx = np.argmax(similarities)
    return vector_db[most_similar_idx][0]

Step 5: Creating a Simple User Interface

Finally, let's create a simple interface for users to ask questions:

def ask_question(question):
    question_embedding = model.encode(question)
    answer = find_most_similar(question_embedding)
    return answer

# Example usage
while True:
    user_question = input("Ask a question (or type 'quit' to exit): ")
    if user_question.lower() == 'quit':
        break
    response = ask_question(user_question)
    print(f"Answer: {response}\n")

Putting It All Together

Now that we have all the components, let's see our simple question-answering system in action:

# Example interaction
Ask a question (or type 'quit' to exit): Where is the Eiffel Tower?
Answer: The Eiffel Tower is located in Paris.

Ask a question (or type 'quit' to exit): What can I see at the Louvre?
Answer: The Louvre Museum houses the Mona Lisa painting.

Ask a question (or type 'quit' to exit): What river is in Paris?
Answer: The Seine River runs through Paris.

Ask a question (or type 'quit' to exit): quit

Limitations and Future Improvements

While this simple system demonstrates the basic principles of using embeddings for question-answering, it has several limitations:

1. It can only return exact matches from the knowledge base.
2. It doesn't handle complex queries or multi-step reasoning.
3. The in-memory database isn't scalable for large amounts of data.

To improve this system, you could:

• Implement more advanced natural language processing techniques
• Use a more sophisticated vector database for efficient similarity search
• Incorporate techniques like query expansion or answer generation

Conclusion

Building a question-answering system using embeddings and vector databases is an exciting way to leverage the power of natural language processing. While our example is simple, it demonstrates the core concepts that drive more advanced systems. As you continue to explore this field, you'll discover even more powerful techniques for creating AI-powered applications that can understand and respond to human language.