New pattern submission - bedrock-semantic-cache-s3vectors-sam
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
Research direction
Review pull request #3262 and the included example-pattern.json, which contains the pattern metadata required by the submission process. Check that the Bedrock, Lambda, S3 Vectors, and AWS SAM template details are complete and that the submitted pattern meets the repository’s collection requirements.
Written by the indexing model from the issue text.
Description
To submit a template to the Serverless Patterns Collection, per the simplified process all pattern metadata (description, language, level, framework, intro, resources, deploy/test/cleanup, author) is provided in the example-pattern.json file included in the PR.
Pattern: bedrock-semantic-cache-s3vectors-sam
Summary: Serverless semantic cache for Amazon Bedrock using AWS Lambda and Amazon S3 Vectors. Lambda embeds the incoming prompt (Titan Text Embeddings v2), queries an S3 Vectors index by cosine similarity, and returns a cached answer on a semantic hit (threshold + freshness TTL + SSM epoch force-invalidation + negation-parity guard), skipping the Bedrock LLM call. On a miss it calls Bedrock, stores the embedding + answer in vector metadata, and returns the fresh result. Fully serverless (S3 Vectors scales to zero; Lambda is stateless).
Language: Python
Framework: AWS SAM
Level: 300 (Advanced)
GitHub PR for template:
https://github.com/aws-samples/serverless-patterns/pull/3262
- Dominant language
- Python
- Stars
- 1.8k
- Forks
- 1.1k
- Avg merge
- 4d 9h
- Merged PRs (30d)
- 6
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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