opensearch-project / opensearch-project/data-prepper
Vector embedding processor
Nobody has claimed this yet.
- Dominant language
- Java
- Stars
- 374
- Forks
- 354
- Avg merge
- 3d 18h
- Merged PRs (30d)
- 8
Description
Vector search is feature of OpenSearch that is gaining prominence with recent emergence of generative AI and other ML use cases. Currently users of OpenSearch either use external embedding methods or use OpenSearch ingest pipelines to generate text embedding. Other processor currently present in the ingest pipelines are Append, Bytes Convert, CSV, Date, IP2Geo, Lowercase Text embedding. However using these processors especially text embedding causes additional CPU usage on OpenSearch. Data Prepper helps with preparing and ingesting data into OpenSearch. Having a text embedding processor in Data Prepper will help reduce this CPU need on OpenSearch and can help with the emerging use cases for OpenSearch
Contributor 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.
Research direction
The issue points to Data Prepper and existing OpenSearch ingest pipeline processors, but names no target files, tests, or entry point. Start by reviewing the linked ingest-pipeline documentation and the repository’s processor structure. The work is ready when the processor’s scope, embedding integration, and completion criteria are defined and validated by tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java
- Domain
- data-engineering, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100