Add Generate Text Embeddings With AWS Bedrock as an LLM TIL

README.md

@@ -10,7 +10,7 @@ working across different projects via [VisualMode](https://www.visualmode.dev/).
 
 For a steady stream of TILs, [sign up for my newsletter](https://visualmode.kit.com/newsletter).
 
-_1898 TILs and counting..._
+_1899 TILs and counting..._
 
 See some of the other learning resources I work on:
 
@@ -750,6 +750,7 @@ If you've learned something here, support my efforts writing daily TILs by
 
 - [Count Number Of Tokens In A File](llm/count-number-of-tokens-in-a-file.md)
 - [Generate Image Alt Text Across Claude Models](llm/generate-image-alt-text-across-claude-models.md)
+- [Generate Text Embeddings With AWS Bedrock](llm/generate-text-embeddings-with-aws-bedrock.md)
 - [Include A File With Message To `ant`](llm/include-a-file-with-message-to-ant.md)
 - [Send cURL To Claude Text Completion API](llm/send-curl-to-claude-text-completion-api.md)
 - [Translate To Canonical Tags With Small Model](llm/translate-to-canonical-tags-with-small-model.md)

llm/generate-text-embeddings-with-aws-bedrock.md

@@ -0,0 +1,89 @@
+# Generate Text Embeddings With AWS Bedrock
+
+An embedding model is a special kind of model built on the same transformer
+architecture as LLMs. It is trained in a way that contrasts inputs such that
+related ones have more similar vectors than unrelated ones. This is useful for a
+variety of things including classification and measuring text similarity. I find
+the text similarity use case interesting because I'm trialing an approach that
+combs through all my [TILs](https://github.com/jbranchaud/til) to find the most
+similar posts for each.
+
+There are a number of production-grade providers that host API access to
+embedding models. One of those providers is AWS Bedrock.
+
+Here are all the embedding models it offers. Notice it's not just text. There
+are image, multilingual, and multimodal models as well.
+
+```bash
+❯ aws bedrock list-foundation-models --by-output-modality EMBEDDING \
+  --query 'modelSummaries[].modelId'
+[
+    "amazon.nova-2-multimodal-embeddings-v1:0",
+    "twelvelabs.marengo-embed-3-0-v1:0",
+    "twelvelabs.marengo-embed-2-7-v1:0",
+    "cohere.embed-v4:0",
+    "amazon.titan-embed-g1-text-02",
+    "amazon.titan-embed-text-v1:2:8k",
+    "amazon.titan-embed-text-v1",
+    "amazon.titan-embed-text-v2:0:8k",
+    "amazon.titan-embed-text-v2:0",
+    "amazon.titan-embed-image-v1:0",
+    "amazon.titan-embed-image-v1",
+    "cohere.embed-english-v3:0:512",
+    "cohere.embed-english-v3",
+    "cohere.embed-multilingual-v3:0:512",
+    "cohere.embed-multilingual-v3"
+]
+```
+
+Here I run one of my TIL posts through [the `amazon.titan-embed-text-v2:0`
+model](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-amazon-titan-text-embeddings-v2.html).
+
+```bash
+❯ jq -n --rawfile body ~/dev/jbranchaud/til/llm/count-number-of-tokens-in-a-file.md \
+    '{inputText: ("llm: Count Number Of Tokens In A File\n\n" + $body)[:8000],
+      dimensions: 1024,
+      normalize: true}' > body.json
+
+❯ aws bedrock-runtime invoke-model \
+  --region us-east-1 \
+  --model-id amazon.titan-embed-text-v2:0 \
+  --content-type application/json --accept application/json \
+  --body fileb://body.json \
+  out.json
+{
+    "contentType": "application/json"
+}
+```
+
+I can then use `jq` to inspect some properties of the output file:
+
+```bash
+❯ jq '.embedding | length' out.json
+1024
+
+❯ jq '.inputTextTokenCount' out.json
+326
+```
+
+It doesn't matter whether I passed it a few tokens or hundreds of tokens, it is
+going to produce an embedding that is 1024 dimensions.
+
+Here is a peek at what is in the file:
+
+```bash
+❯ jq '.' out.json | head -n 8
+{
+  "embedding": [
+    -0.039784759283065796,
+    -0.014103486202657223,
+    0.0918450877070427,
+    0.024694688618183136,
+    -0.007761063985526562,
+    -0.013740465976297855,
+```
+
+Where this gets interesting is when I store embeddings like this for a bunch of
+content and then use that to do _cosine similarity_ searches. Note that because
+I normalized the embedding, the cosine similarity is just the dot product of two
+embeddings.