Implementing Zero-Shot Learning Pipelines with FastAPI, Pinecone, and Azure Functions for Intelligent Search Applications

In today’s era of intelligent search applications, Zero-Shot Learning (ZSL) has emerged as a powerful way to enable search systems to infer results for categories they have never seen during training. This capability is particularly useful when dealing with dynamic datasets where new categories are frequently introduced. In this blog post, we’ll walk through the implementation of a ZSL pipeline using **FastAPI**, **Pinecone**, and **Azure Functions**.

We’ll explore the architecture, technical details, and provide practical examples to help you build your own ZSL-powered intelligent search platform.

Zero-Shot Learning is a machine learning paradigm where a model is trained on a base set of classes but can generalize to unseen classes using auxiliary information such as textual descriptions or embeddings. In search applications, ZSL can significantly improve user experience by enabling the system to infer relationships between queries and documents without requiring explicit retraining.

For example, imagine a search application that identifies documents related to “quantum computing.” Even if “quantum computing” was not part of the training data, ZSL can infer its relevance by comparing embeddings generated from textual descriptions.

To implement a Zero-Shot Learning pipeline, we’ll use the following tools:

1. FastAPI: A modern web framework for building APIs quickly and efficiently.
2. Pinecone: A vector database for managing embeddings and enabling similarity search.
3. Azure Functions: Serverless compute for deploying lightweight inference logic.

The architecture will consist of:

• FastAPI Backend: Handles incoming search queries and interacts with Pinecone.
• Pinecone Database: Stores embeddings for documents and queries.
• Azure Functions: Performs Zero-Shot Learning inference using pre-trained models and returns embeddings.

First, create a Pinecone account and initialize a vector index to store embeddings. Then, set up a FastAPI backend to handle queries.

from fastapi import FastAPI, HTTPException
import pinecone
from pydantic import BaseModel

# Initialize Pinecone
pinecone.init(api_key="YOUR_PINECONE_API_KEY", environment="YOUR_ENVIRONMENT")
index = pinecone.Index("zsl-index")

# Initialize FastAPI app
app = FastAPI()

class Query(BaseModel):
    text: str

@app.post("/search")
def search(query: Query):
    # Convert query text into embeddings (assume embeddings are provided by Azure Functions)
    embedding = get_embedding_from_azure(query.text)
    
    # Perform similarity search in Pinecone
    results = index.query(vector=embedding, top_k=5, include_metadata=True)
    return {"results": results}

def get_embedding_from_azure(text: str):
    # Call Azure Function to get embeddings (dummy implementation)
    # Replace with actual Azure HTTP API call logic
    return [0.1, 0.2, 0.3, 0.4, 0.5]

Azure Functions will host the Zero-Shot Learning inference logic, generating embeddings for both queries and documents using pre-trained models like OpenAI’s GPT or sentence transformers.

import azure.functions as func
from sentence_transformers import SentenceTransformer

# Load model once during startup
model = SentenceTransformer("all-mpnet-base-v2")

def main(req: func.HttpRequest) -> func.HttpResponse:
    try:
        text = req.params.get("text")
        if not text:
            return func.HttpResponse("Missing 'text' parameter", status_code=400)
        
        # Generate embeddings
        embeddings = model.encode(text).tolist()
        return func.HttpResponse(str(embeddings), status_code=200)
    except Exception as e:
        return func.HttpResponse(f"Error: {str(e)}", status_code=500)

Once embeddings are generated, store documents in Pinecone for future queries.

documents = [
    {"id": "doc1", "text": "Introduction to quantum computing", "metadata": {"category": "science"}},
    {"id": "doc2", "text": "Machine learning basics", "metadata": {"category": "technology"}},
]

for doc in documents:
    # Generate embeddings for documents using Azure Function
    embedding = get_embedding_from_azure(doc["text"])
    
    # Upsert into Pinecone
    index.upsert([{
        "id": doc["id"],
        "vector": embedding,
        "metadata": doc["metadata"]
    }])

Now that Pinecone has stored document embeddings, query the index using text input and return the most relevant documents.

query_text = "Explain quantum mechanics"
query_embedding = get_embedding_from_azure(query_text)

# Perform similarity search
search_results = index.query(vector=query_embedding, top_k=5, include_metadata=True)

# Print results
for result in search_results["matches"]:
    print(f"Document ID: {result['id']}, Score: {result['score']}, Metadata: {result['metadata']}")

– Package the Azure Function as a Python project. – Deploy to Azure using the Azure CLI or Visual Studio Code’s Azure extension.

– Use Docker to containerize the FastAPI app. – Deploy on a cloud service like AWS EC2, Azure App Service, or Google Cloud Run.

With FastAPI, Pinecone, and Azure Functions, implementing a Zero-Shot Learning pipeline becomes seamless and efficient. This architecture enables intelligent search applications that can handle dynamic and unseen categories without retraining. By leveraging pre-trained models and vector databases, you can create scalable solutions that enhance user experience and deliver accurate results.