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+# Serverless LanceDB
+
+## Store your data on S3 and use Lambda to compute embeddings and retrieve queries in production easily.
+
+<img id="splash" width="400" alt="s3-lambda" src="https://user-images.githubusercontent.com/917119/234653050-305a1e90-9305-40ab-b014-c823172a948c.png">
+
+This is a great option if you're wanting to scale with your use case and save effort and costs of maintenance.
+
+Let's walk through how to get a simple Lambda function that queries the SIFT dataset on S3.
+
+Before we start, you'll need to ensure you create a secure account access to AWS. We recommend using user policies, as this way AWS can share credentials securely without you having to pass around environment variables into Lambda.
+
+We'll also use a container to ship our Lambda code. This is a good option for Lambda as you don't have the space limits that you would otherwise by building a package yourself.
+
+# Initial setup: creating a LanceDB Table and storing it remotely on S3
+
+We'll use the SIFT vector dataset as an example. To make it easier, we've already made a Lance-format SIFT dataset publically available, which we can access and use to populate our LanceDB Table. 
+
+To do this, download the Lance files locally first from:
+
+```
+s3://eto-public/datasets/sift/vec_data.lance
+```
+
+Then, we can write a quick Python script to populate our LanceDB Table:
+
+```python
+import pylance
+sift_dataset = pylance.dataset("/path/to/local/vec_data.lance")
+df = sift_dataset.to_table().to_pandas()
+
+import lancedb
+db = lancedb.connect(".")
+table = db.create_table("vector_example", df)
+```
+
+Once we've created our Table, we are free to move this data over to S3 so we can remotely host it.
+
+# Building our Lambda app: a simple event handler for vector search
+
+Now that we've got a remotely hosted LanceDB Table, we'll want to be able to query it from Lambda. To do so, let's create a new `Dockerfile` using the AWS python container base:
+
+```docker
+FROM public.ecr.aws/lambda/python:3.10
+
+RUN pip3 install --upgrade pip
+RUN pip3 install --no-cache-dir -U numpy --target "${LAMBDA_TASK_ROOT}"
+RUN pip3 install --no-cache-dir -U lancedb --target "${LAMBDA_TASK_ROOT}"
+
+COPY app.py ${LAMBDA_TASK_ROOT}
+
+CMD [ "app.handler" ]
+```
+
+Now let's make a simple Lambda function that queries the SIFT dataset in `app.py`.
+
+```python    
+import json
+import numpy as np
+import lancedb
+
+db = lancedb.connect("s3://eto-public/tables")
+table = db.open_table("vector_example")
+
+def handler(event, context):
+    status_code = 200
+
+    if event['query_vector'] is None:
+        status_code = 404
+        return {
+            "statusCode": status_code,
+            "headers": {
+                "Content-Type": "application/json"
+            },
+            "body": json.dumps({
+                "Error ": "No vector to query was issued"
+            })
+        }
+    
+    # Shape of SIFT is (128,1M), d=float32
+    query_vector = np.array(event['query_vector'], dtype=np.float32)
+
+    rs = table.search(query_vector).limit(2).to_df()
+
+    return {
+        "statusCode": status_code,
+        "headers": {
+            "Content-Type": "application/json"
+        },
+        "body": rs.to_json()
+    }
+``` 
+
+# Deploying the container to EKS
+
+The next step is to build and push the container to EKS, where it can then be used to create a new Lambda function. 
+
+It's best to follow the official AWS documentation for how to do this, which you can view here:
+
+```
+https://docs.aws.amazon.com/lambda/latest/dg/images-create.html#images-upload
+```
+
+# Final step: setting up your Lambda function
+
+Once the container is pushed, you can create a Lambda function by selecting the container.