MIGRATING DATA FROM PINECONE SERVERLESS TO MONGODB : A STEP-BY-STEP GUIDE

Tanya kansal -

To manage Pinecone data well, you need a storage system that can handle different types of data easily, like MongoDB. Let's see how MongoDB's way of handling data can help store Pinecone data better:

  1. Handling Different Types of Data: MongoDB is good at handling different types of data. It can work well with how your application uses data. This makes it easier to develop and use Pinecone data.
  2. Handling More Data: As you get more Pinecone data, MongoDB can manage it well by spreading it across many servers. Because of its horizontal scaling capabilities, MongoDB can manage numerous concurrent read and write requests This helps keep things running smoothly even as your data gets bigger.
  3. Finding and Using Data: MongoDB can quickly find and put together pieces of data, especially when you have a lot of it using aggregation queries and indexing searching. It can do more complicated things with data compared to Pinecone.
  4. Keeping Data Safe: MongoDB makes sure your data stays safe. It has strong rules and ways of setting up your database to keep your Pinecone data private and secure, even if you have a lot of it you can also dump your whole pinecone data in your local server.

The Manual Method: Using Code-Based Integration to Integrate Pinecone and Mongodb

To manually migrate data from Pinecone to MongoDB , you can follow the following steps:--

Step 1: Install the necessary libraries

   from pinecone import Pinecone as PineconeClient
    import os
    import pymongo

Step 2: Connect to your MongoDB database using dot env.

    mongo_uri = os.getenv("MONGO_URI")
    database_name = os.getenv("DATABASE_NAME")
    collection_name = os.getenv("COLLECTION_NAME")
    collection_name2 = os.getenv("COLLECTION_NAME2")

    mongo_client = pymongo.MongoClient(mongo_uri)
    db = mongo_client[database_name]
    collectionName = db[collection_name]
    collectionName2 = db[collection_name2]
    ATLAS_VECTOR_SEARCH_INDEX_NAME = "default"

Step 3: Install and configure Pinecone- serverless

    Sign up for a Pinecone account.

Step 4: Initialize the Pinecone client

    pinecone = PineconeClient(api_key=os.getenv("PINECONE_API_KEY"),
                             environment=os.getenv("PINECONE_REGION"))

Step 5: Retrieve the data from Pinecone

Using this, you will obtain all namespaces and iterate through each of them to retrieve all vectors and data corresponding to each particular namespace. If you wish to obtain metadata and values, mark them as True.

    pinecone_data =[]

    index = pinecone.Index(os.getenv('PINECONE_INDEX_NAME'))

    num_vectors = index.describe_index_stats()
    for namesp in num_vectors['namespaces']:
        query_response = index.query(
            top_k = 100,
            vector =  [0] * 1536, 
            namespace= namesp,
            includeMetadata=True,
            includeValues=True
        )

        pinecone_data.append(query_response)

Step 6: Construct document to insert into MongoDB

    for response in pinecone_data:
        response_dict = response.to_dict() 
        matches = response_dict['matches']
        for match in matches:
            text = match['metadata']['text']
            values = match['values']

           
            document = {'text': text, 'values': values}

Step 7: Insert document into MongoDB

    insertedDocuments = collectionName2.insert_one(document)

Step 8: Verify the migration

  • Retrieve a document from Mongodb to ensure the data migration was successful. The above code samples provide a basic overview of the manual migration process. Ensure you replace the placeholder values with actual values specific to your setup.