Discover the power of GPT-3 embeddings in building state-of-the-art recommendation systems that leverage cosine similarity to provide personalized and relevant content suggestions to your users.

Introduction

GPT-3, developed by OpenAI, is a powerful and versatile language model known for generating high-quality text. Its embeddings, or vector representations, can also be used to create cutting-edge recommendation systems by leveraging cosine similarity for finding related content. This comprehensive guide will walk you through the entire process, from generating GPT-3 embeddings to deploying your recommendation system in production.

Get Started with GPT-3 Integration

To begin, you'll need to generate GPT-3 embeddings for your set of documents. This is accomplished using the OpenAI GPT-3 API, which returns a vector representation when provided with a block of text. Generate the vectors for each document and store them in an array. Here's a Python example:

pythonCopy codeimport openai

openai.api_key = "your_api_key"

def generate_gpt3_vector(text):
    response = openai.Completion.create(engine="text-davinci-002", prompt=text, n=1, max_tokens=1, return_prompt=True, echo=True, stop=None, temperature=0.0, top_p=0.0)
    return response["choices"][0]["logprobs"]["token_logprobs"]

documents = [...] # Your list of documents
gpt3_vectors = [generate_gpt3_vector(doc) for doc in documents]

And that's where we begin. Rember to replace "your_api_key" with the real API key.

Working on the Docs

With the GPT-3 embeddings generated, you can now compute the cosine similarity between document pairs. Cosine similarity measures the cosine of the angle between two non-zero vectors in an inner product space. To calculate it, use the following formula:

cosine_similarity = (A * B) / (||A|| * ||B||)

Here, A and B represent two document vectors, while ||A|| and ||B|| denote the magnitudes of these vectors. The Python code for calculating cosine similarity between document pairs is as follows:

pythonCopy codefrom numpy import dot
from numpy.linalg import norm

def cosine_similarity(a, b):
    return dot(a, b) / (norm(a) * norm(b))

similarities = [[cosine_similarity(a, b) for b in gpt3_vectors] for a in gpt3_vectors]

After calculating cosine similarity scores for all document pairs, identify the most similar documents by sorting scores in descending order and selecting the top N results. Use the resulting list of related documents to recommend content to your users. The following Python code demonstrates this process:

import heapq

def find_top_n_similar_docs(doc_index, similarities, n):
    return heapq.nlargest(n + 1, range(len(similarities)), key=lambda i: similarities[doc_index][i])[1:]

def recommend_similar_documents(document_index, n=5):
    top_n_similar_docs = find_top_n_similar_docs(document_index, similarities, n)
    return [(i, documents[i], similarities[document_index][i]) for i in top_n_similar_docs]

recommended_documents = recommend_similar_documents(0)
for index, document, similarity in recommended_documents:
    print(f"Document {index}: {document}\nSimilarity: {similarity}\n")

In this example, recommend_similar_documents takes the index of a document and returns the top N similar documents, along with their cosine similarity scores.

For this approach, the GPT-3 embeddings are designed to encode contextual information and semantic meaning, which can help identify similarities even when documents use different words or phrases to express the same concepts. An alternative approach is using NLP based text extraction algorithm such as what Kanaries RATH has implemented.

Put It to Work

To implement your recommendation system in production, you can index GPT-3 vectors using Elasticsearch. Utilize the dense_vector field type with the l2_norm similarity metric. The mapping should resemble the following:

{
  "mappings": {
    "properties": {
      "my_vector": {
        "type": "dense_vector",
        "dims": 1024,
        "similarity": "l2_norm",
        "index": true
      },
      "document": {
        "type": "text"
      }
    }
  }
}

Using Annoy for Vector Similarity Search

Annoy (Approximate Nearest Neighbors Oh Yeah) is a library that enables fast approximate nearest neighbor searches on large vector datasets. It can be used to index GPT-3 vectors and efficiently perform similarity searches.

from annoy import AnnoyIndex

def index_gpt3_vectors(vectors, dimensions=1024):
    index = AnnoyIndex(dimensions, 'angular')

    for i, vector in enumerate(vectors):
        index.add_item(i, vector)

    index.build(10)
    return index

gpt3_vectors_index = index_gpt3_vectors(gpt3_vectors)

def find_top_n_similar_docs_annoy(document_index, index, n=5):
    return index.get_nns_by_item(document_index, n + 1)[1:]

recommended_documents_annoy = find_top_n_similar_docs_annoy(0, gpt3_vectors_index)

Indexing GPT-3 Vectors with Milvus

Milvus is an open-source vector similarity search engine that can be used for efficient similarity search and retrieval of GPT-3 vectors. To use Milvus, you'll need to install the pymilvus library and have a Milvus server running.

pythonCopy codefrom pymilvus import DataType, Collection, CollectionSchema, FieldSchema, connections

def create_collection(collection_name):
    connections.connect()
    dim = 1024
    fields = [
        FieldSchema(name="document_id", dtype=DataType.INT64, is_primary=True),
        FieldSchema(name="gpt3_vector", dtype=DataType.FLOAT_VECTOR, dim=dim),
    ]
    schema = CollectionSchema(fields, description="GPT-3 vector collection")
    collection = Collection(name=collection_name, schema=schema)
    return collection

def insert_vectors(collection, vectors):
    num_vectors = len(vectors)
    entities = [
        {"document_id": i, "gpt3_vector": vectors[i]}
        for i in range(num_vectors)
    ]
    ids = collection.insert(entities)
    return ids

def index_vectors_with_milvus(collection_name, vectors):
    collection = create_collection(collection_name)
    insert_vectors(collection, vectors)
    return collection

def find_top_n_similar_docs_milvus(document_index, collection, n=5):
    from pymilvus import utility, DefaultConfig
    query_vector = collection[document_index].gpt3_vector
    search_params = {
        "metric_type": "L2",
        "params": {"nprobe": 10},
    }
    results = utility.search_vectors(collection, [query_vector], top_k=n + 1, params=search_params)
    return [result.id for result in results[0][1:]]

gpt3_vectors_collection = index_vectors_with_milvus("gpt3_vectors", gpt3_vectors)
recommended_documents_milvus = find_top_n_similar_docs_milvus(0, gpt3_vectors_collection)

Both the Annoy and Milvus examples provide alternative approaches to Elasticsearch for indexing GPT-3 vectors and performing similarity searches in production. These solutions can help mitigate the computational costs associated with vector similarity searches and provide faster results, especially with large vector datasets.

Conclusion

To sum up, leveraging GPT-3 embeddings and cosine similarity provides an advanced and effective approach to building recommendation systems that can uncover meaningful connections between documents. By tapping into the sophisticated language understanding capabilities of GPT-3, we can create more accurate and contextually relevant vector representations of text, ultimately leading to improved content discovery for users.

As we continue to explore the potential of GPT-3 and other language models, the possibilities for creating powerful and intelligent recommendation systems are limitless.