Introduction to Document Retrieval in Generative AI

Generative AI has revolutionized the way we interact with information, and document retrieval systems play a crucial role in this landscape. By leveraging vector search techniques, we can create highly efficient and accurate retrieval systems that power various AI applications, from chatbots to content recommendation engines.

In this blog post, we'll explore the key components of implementing document retrieval systems with vector search for generative AI applications. We'll cover the basics of vector embeddings, indexing methods, and similarity search algorithms, providing practical examples along the way.

Understanding Vector Embeddings

At the heart of vector search lies the concept of vector embeddings. These are dense numerical representations of text, images, or other data types in a high-dimensional space. For document retrieval, we typically work with text embeddings.

Creating Text Embeddings

To create text embeddings, we can use pre-trained models like BERT, GPT, or sentence transformers. Here's a simple example using the sentence-transformers library in Python:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ["This is an example sentence", "Each sentence is converted to a vector"]
embeddings = model.encode(sentences)

print(embeddings.shape)

# Output: (2, 384)

In this example, we've created vector embeddings for two sentences, each represented by a 384-dimensional vector.

Indexing Vector Embeddings

Once we have our vector embeddings, we need to index them for efficient retrieval. There are several indexing methods available, but one popular approach is the Approximate Nearest Neighbor (ANN) indexing.

Implementing ANN Indexing with Faiss

Faiss is a library developed by Facebook AI Research for efficient similarity search and clustering of dense vectors. Here's how you can use Faiss to index your vector embeddings:

import numpy as np
import faiss

# Assume we have a collection of document embeddings
document_embeddings = np.random.rand(10000, 384).astype('float32')

# Create a Faiss index
index = faiss.IndexFlatL2(384)
index.add(document_embeddings)

print(f"Number of vectors indexed: {index.ntotal}")

In this example, we've created a simple L2 (Euclidean distance) index for our document embeddings. For larger datasets, you might want to use more advanced indexing methods like IVF (Inverted File) or HNSW (Hierarchical Navigable Small World) for better performance.

Performing Similarity Search

With our indexed embeddings, we can now perform similarity searches to retrieve relevant documents for a given query.

Implementing K-Nearest Neighbors Search

Here's an example of how to perform a K-Nearest Neighbors (KNN) search using our Faiss index:

# Assume we have a query embedding
query_embedding = np.random.rand(1, 384).astype('float32')

# Perform KNN search
k = 5

# Number of nearest neighbors to retrieve
distances, indices = index.search(query_embedding, k)

print(f"Indices of {k} nearest neighbors: {indices[0]}")
print(f"Distances to {k} nearest neighbors: {distances[0]}")

This code snippet retrieves the 5 nearest neighbors to our query embedding, returning their indices and distances.

Enhancing Retrieval Quality

To improve the quality of retrieved documents, consider implementing the following techniques:

1. Query expansion: Augment the original query with related terms or concepts to broaden the search.

2. Re-ranking: Apply a more sophisticated ranking algorithm to the initial set of retrieved documents.

3. Hybrid approaches: Combine vector search with traditional keyword-based search for better results.

Integrating with Generative AI Models

Once you have a working document retrieval system, you can integrate it with generative AI models to create powerful applications. For example, you could use the retrieved documents as context for a language model to generate more informed and relevant responses.

Here's a simple example using the OpenAI API:

import openai

def generate_response(query, retrieved_documents):
    context = "\n".join(retrieved_documents)
    prompt = f"Context:\n{context}\n\nQuery: {query}\nResponse:"
    
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=150
    )
    
    return response.choices[0].text.strip()

# Usage
query = "What are the benefits of vector search?"
retrieved_docs = ["Vector search enables efficient similarity-based retrieval.",
                  "It works well with high-dimensional data like text embeddings."]

response = generate_response(query, retrieved_docs)
print(response)

This example demonstrates how you can use retrieved documents as context for a generative AI model to produce more informed responses.

Conclusion

Implementing document retrieval systems with vector search is a powerful technique for enhancing generative AI applications. By understanding vector embeddings, indexing methods, and similarity search algorithms, you can create efficient and accurate retrieval systems that significantly improve the performance of your AI-powered apps.