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Monocle S3 Langchain Lambda

Prerequisites

  1. Update the credentials in template.yaml before deploying:
    • BUCKET_NAME: Your S3 bucket name
    • OPENAI_API_KEY: Your OpenAI API key
    • MONOCLE_AWS_ACCESS_KEY_ID: AWS access key for S3
    • MONOCLE_AWS_SECRET_ACCESS_KEY: AWS secret key for S3

Vector Store Setup

  1. Add your coffee-related text content in coffee_text.py.
  2. Generate embeddings using OpenAI's Embedding API:
from openai import OpenAI
import json

# Initialize OpenAI client
client = OpenAI(api_key='your-api-key')

# Get your text from coffee_text.py
from coffee_text import coffee_text

# Generate embeddings
response = client.embeddings.create(
    input=coffee_text,
    model="text-embedding-3-small"
)

# Extract the embeddings
embeddings = response.data[0].embedding

# Save to coffee_embedding.py
with open('coffee_embedding.py', 'w') as f:
    f.write(f'embedding_json = {json.dumps(embeddings, indent=4)}')

Deployment

Deploy the application using AWS SAM:

  1. Build the application:

    sam build

  2. Deploy to AWS:

    sam deploy --guided

  3. Follow the prompts to configure your deployment:

    • Stack Name: Choose a name for your CloudFormation stack
    • AWS Region: Choose your target region
    • Confirm changes before deploy: Yes
    • Allow SAM CLI IAM role creation: Yes
    • Save arguments to configuration file: Yes

  4. Wait for the deployment to complete. SAM will provide the Lambda function's ARN in the outputs.