[Question]: Using Pinecone and S3 to update my index.

[kylemassimilian]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

I am trying to create a Q&A chatbot over a large set of documents. I'm holding my data in an S3 bucket, as I have a Zapier integration that updates my bucket every time someone uploads a new file to Egnyte (where we keep all our files). Originally, I had a demo working where I loaded a small # of docs from the S3 bucket, created a vector index, and used a chat_engine. Using the Llamaindex's VectorStoreIndex temporarily downloads the file to your local disk (as far as I understand). Therefore, to scale this to a large number of files, I need to use a different approach. My plan is to connect my bucket to Pinecone, configure the index using llama, and use that as the backbone of my app. It's important that I create a solution where my index updates with new docs. Do I need to create embeddings for my data using a model (myself) each time there's a new file and then upload this to Pinecone? Do I need to create a Lambda function w/ an event to update my Pinecone index when new files are added to my S3 bucket? Apologies, I am new to Pinecone/vector dbs. Could someone please tell me if I am thinking about this correctly and/or there's a better way? Thank you.

[tyre]

Yes your architecture looks about right:

1. AWS Lambda function executed when a new document enters the S3 bucket
2. Pull that file locally and load into a set of LlamaIndex Documents
3. Use the PineconeVectorStore to add the new documents to Pinecone

For (2) there is an S3 reader on llamahub that looks like it can do the loading part in a couple lines of code.

For (3) there is an example in the docs about customizing the storage layer to use Pinecone.

Does that make sense?

[dosubot[bot]]

🤖

Hello,

Your approach to using Pinecone with LlamaIndex for indexing large amounts of data is correct. As you've mentioned, you can use the PineconeVectorStore in LlamaIndex to add, delete, and query nodes in your Pinecone index. This is demonstrated in the PineconeIndexDemo-0.6.0.ipynb example.

Regarding your question about updating the index when new files are added to your S3 bucket, you're on the right track. You would need to create a process that triggers when a new file is added to your S3 bucket. This process would need to:

1. Download the new file from the S3 bucket.
2. Create embeddings for the new data using your model.
3. Add the new embeddings to your Pinecone index using the add method in the PineconeVectorStore class.

This process could be implemented using an AWS Lambda function that triggers on S3 bucket events, as you suggested. The Lambda function would need to be able to access your model for creating embeddings, which could be achieved by either including the model in the Lambda function package or loading the model from another S3 bucket.

Here's a rough example of what the Lambda function could look like:

import boto3
import pinecone
from llama_index import VectorStoreIndex, StorageContext
from llama_index.vector_stores import PineconeVectorStore

def lambda_handler(event, context):
    # Initialize Pinecone
    pinecone.init(environment="eu-west1-gcp")
    pinecone_index = pinecone.Index("quickstart-index")

    # Initialize PineconeVectorStore
    vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace="my_namespace")
    storage_context = StorageContext.from_defaults(vector_store=vector_store)

    # Load model (this could be from another S3 bucket)
    model = load_model()

    # Get new file from S3 bucket
    s3 = boto3.client('s3')
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']
    response = s3.get_object(Bucket=bucket, Key=key)
    file_content = response['Body'].read().decode('utf-8')

    # Create embeddings for new data
    embeddings = model.predict([file_content])

    # Add new embeddings to Pinecone index
    vector_store.add([(key, embeddings)])

    return {
        'statusCode': 200,
        'body': 'Index updated successfully'
    }

Please note that this is a simplified example and you would need to adapt it to your specific use case. For example, you might need to preprocess your data before creating embeddings, handle errors, and so on.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• docs/examples/vector_stores/PineconeIndexDemo-0.6.0.ipynb
• llama_index/readers/pinecone.py
• llama_index/vector_stores/pinecone.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[dosubot[bot]]

🤖

Hello,

Your approach to using Pinecone with LlamaIndex for indexing large amounts of data is correct. As you've mentioned, you can use the PineconeVectorStore in LlamaIndex to add, delete, and query nodes in your Pinecone index. This is demonstrated in the PineconeIndexDemo-0.6.0.ipynb example.

Regarding your question about updating the index when new files are added to your S3 bucket, you're on the right track. You would need to create a process that triggers when a new file is added to your S3 bucket. This process would need to:

1. Download the new file from the S3 bucket.
2. Create embeddings for the new data using your model.
3. Add the new embeddings to your Pinecone index using the add method in the PineconeVectorStore class.

This process could be implemented using an AWS Lambda function that triggers on S3 bucket events, as you suggested. The Lambda function would need to be able to access your model for creating embeddings, which could be achieved by either including the model in the Lambda function package or loading the model from another S3 bucket.

Here's a rough example of what the Lambda function could look like:

import boto3
import pinecone
from llama_index import VectorStoreIndex, StorageContext
from llama_index.vector_stores import PineconeVectorStore

def lambda_handler(event, context):
    # Initialize Pinecone
    pinecone.init(environment="eu-west1-gcp")
    pinecone_index = pinecone.Index("quickstart-index")

    # Initialize PineconeVectorStore
    vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace="my_namespace")
    storage_context = StorageContext.from_defaults(vector_store=vector_store)

    # Load model (this could be from another S3 bucket)
    model = load_model()

    # Get new file from S3 bucket
    s3 = boto3.client('s3')
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']
    response = s3.get_object(Bucket=bucket, Key=key)
    file_content = response['Body'].read().decode('utf-8')

    # Create embeddings for new data
    embeddings = model.predict([file_content])

    # Add new embeddings to Pinecone index
    vector_store.add([(key, embeddings)])

    return {
        'statusCode': 200,
        'body': 'Index updated successfully'
    }

Please note that this is a simplified example and you would need to adapt it to your specific use case. For example, you might need to preprocess your data before creating embeddings, handle errors, and so on.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• docs/examples/vector_stores/PineconeIndexDemo-0.6.0.ipynb
• llama_index/readers/pinecone.py
• llama_index/vector_stores/pinecone.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.