diff --git a/docs/src/examples/s3_lambda.md b/docs/src/examples/s3_lambda.md index b63201e4..7467d943 100644 --- a/docs/src/examples/s3_lambda.md +++ b/docs/src/examples/s3_lambda.md @@ -14,7 +14,7 @@ We'll also use a container to ship our Lambda code. This is a good option for La # 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. +We'll use the SIFT vector dataset as an example. To make it easier, we've already made a Lance-format SIFT dataset publicly available, which we can access and use to populate our LanceDB Table. To do this, download the Lance files locally first from: @@ -91,9 +91,9 @@ def handler(event, context): } ``` -# Deploying the container to EKS +# Deploying the container to ECR -The next step is to build and push the container to EKS, where it can then be used to create a new Lambda function. +The next step is to build and push the container to ECR, 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: diff --git a/docs/src/examples/youtube_transcript_search.md b/docs/src/examples/youtube_transcript_search.md new file mode 100644 index 00000000..82d71b8b --- /dev/null +++ b/docs/src/examples/youtube_transcript_search.md @@ -0,0 +1,7 @@ +# YouTube transcript search + +## Search through youtube transcripts using natural language with LanceDB + +youtube transcript search + +This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/notebooks/youtube_transcript_search.ipynb)