Don't naive RAG do hybrid search instead (Pinecone Weaviate or pgvector + full text search & rerank)

Introduction

This tutorial will guide you through implementing hybrid search using popular tools like Pinecone, Weaviate, and Postgres (Supabase) with full-text search capabilities. We'll also explore reranking search results using Jina AI's Reranker. This approach can enhance your application's search functionality, making it more efficient and accurate.

Step 1: Setting Up Your Environment

Before diving into hybrid search, you need to set up your development environment. Follow these instructions:

1. Install Required Libraries

   • Use Python and ensure you have the following libraries installed:

     pip install pinecone-client weaviate-client supabase
     

2. Create Accounts

   • Sign up for the following services to access their APIs:
     • Pinecone
     • Weaviate
     • Supabase

3. Access API Keys

   • Obtain your API keys from each service and store them securely.

Step 2: Implementing Hybrid Search with Pinecone and Full Text Search

In this step, we'll set up a hybrid search using Pinecone and a full-text search in Supabase.

1. Initialize Pinecone

   • Start by initializing Pinecone with your API key:

     import pinecone
     
     pinecone.init(api_key='YOUR_API_KEY')
     

2. Create a Pinecone Index

   • Create an index for your search:

     pinecone.create_index('your-index-name')
     

3. Upload Vector Data

   • Upload your data as vectors to the Pinecone index:

     index = pinecone.Index('your-index-name')
     index.upsert(vectors=[('id1', vector_data1), ('id2', vector_data2)])
     

4. Set Up Supabase for Full Text Search

   • Initialize Supabase:

     from supabase import create_client, Client
     
     supabase_url = 'YOUR_SUPABASE_URL'
     supabase_key = 'YOUR_SUPABASE_KEY'
     supabase: Client = create_client(supabase_url, supabase_key)
     

5. Perform Full Text Search

   • Use Supabase to perform full-text searches:

     response = supabase.table('your_table_name').select('*').ilike('column_name', '%search_term%').execute()
     

Step 3: Reranking with Jina AI

To improve search results, implement reranking using Jina AI.

1. Install Jina AI Reranker

   • Follow the installation instructions from the Jina AI documentation.

2. Set Up Reranker

   • Initialize the Jina Reranker in your code:

     from jina import Reranker
     
     reranker = Reranker(model='your-model-name')
     

3. Prepare Data for Reranking

   • Combine results from both Pinecone and Supabase:

     combined_results = pinecone_results + supabase_results
     

4. Rerank the Results

   • Use the reranker to refine the search results:

     reranked_results = reranker.rerank(combined_results)
     

Conclusion

By following these steps, you can successfully implement a hybrid search system that leverages the strengths of Pinecone for vector storage, Supabase for full-text search, and Jina AI for reranking results. This setup enhances search efficiency and accuracy, making it ideal for various applications.

Next steps could include exploring further optimizations, such as fine-tuning your models or experimenting with additional tools for improved performance.