How can I perform semantic search on a product database using OpenAI embeddings?

Below is one common approach to solving your problem using semantic search with embeddings:

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High-Level Plan

1. Embed Your Product Descriptions:
   Use one of OpenAI's embedding models (for example, text-embedding-ada-002) to convert each product description into a vector. This vector numerically encodes the product’s content.

2. Store into a Vector Database:
   Save the embeddings along with associated product metadata into a vector database (or index). Options include:

   • Managed solutions like Pinecone.
   • Open-source libraries like FAISS.

3. Querying:
   When you receive a query such as “Recommend me 3 products for an 8 year old who doesn’t like to go outside”, convert that query to an embedding with the same model.
   Then, perform a similarity search (e.g., using cosine similarity) against your indexed product embeddings.

4. Post-Processing with LLM (optional):
   You can feed the top-N results (e.g., 20 closest matches) into an LLM along with the query. For example, “Based on the following products, recommend the best 3 products...” so that the LLM can reason amongst the candidates and refine the recommendations.

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Example Implementation Outline

Here’s an outline in Python using OpenAI’s API and FAISS for vector indexing:

PYTHONCopied
import openai
import faiss
import numpy as np

# Set your OpenAI API key
openai.api_key = "YOUR_API_KEY"

def get_embedding(text, model="text-embedding-ada-002"):
    response = openai.Embedding.create(input=[text], model=model)
    embedding = response["data"][0]["embedding"]
    return np.array(embedding, dtype=np.float32)

# Assume you have a list of products
# Each product is a dict: { "id": "product_id", "description": "Full product description..." }
products = load_your_products()  # Replace with your data loading method

# Step 1: Compute embeddings for all products
embeddings = []
for prod in products:
    emb = get_embedding(prod["description"])
    embeddings.append(emb)

embeddings = np.vstack(embeddings)

# Step 2: Create FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)

# You may want to keep a mapping of index positions to product details
id_mapping = {i: prod for i, prod in enumerate(products)}

# Function to search products based on a query
def search_products(query, k=3):
    query_emb = get_embedding(query)
    # Reshape query embedding
    query_emb = np.expand_dims(query_emb, axis=0)
    distances, indices = index.search(query_emb, k * 5)  # retrieve more candidates
    # Optionally, post-filter candidates using your business logic or LLM prompt
    # For this example, we simply pick the top-k
    results = []
    for idx in indices[0][:k]:
        results.append(id_mapping[idx])
    return results

# Example usage:
query = "Recommend me 3 products for an 8 year old who doesn’t like to go outside"
raw_results = search_products(query, k=3)

# If necessary, use an LLM to further refine the output:
llm_prompt = f"""
Given the following products:
{[prod['description'] for prod in raw_results]}
Recommend the best 3 products for an 8 year old who doesn’t like to go outside.
"""

# You can now call the LLM to format the answer:
response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=llm_prompt,
    max_tokens=150,
    temperature=0.7
)

print(response.choices[0].text.strip())

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Additional Notes

• Scaling:
  With 10,000+ products, FAISS works efficiently on a local machine, but for larger scale or ease of integration, consider managed services like Pinecone or Weaviate.

• Embedding Consistency:
  Make sure to use the same embedding model for both indexing and query conversion.

• RAG Approach (Optional):
  Retrieval-Augmented Generation can help combine the strengths of search and LLM reasoning. Here, you first perform a semantic search, then "augment" the LLM prompt with the retrieved documents for better contextual recommendations.

• Resources and Limitations:
  This method avoids the complexities of training a custom model since you leverage pre-trained embeddings and can later refine recommendations with additional LLM prompts.

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This approach will let you effectively "search" your product catalog using natural language queries without requiring a massive custom data set or training resources.