mirror of
https://github.com/openai/openai-cookbook
synced 2024-11-17 15:29:46 +00:00
32 lines
1.5 KiB
Markdown
32 lines
1.5 KiB
Markdown
|
# Elasticsearch
|
||
|
|
|
||
|
|
Elasticsearch is a popular search/analytics engine and [vector database](https://www.elastic.co/elasticsearch/vector-database).
|
||
|
|
Elasticsearch offers an efficient way to create, store, and search vector embeddings at scale.
|
||
|
|
|
||
|
|
For technical details, refer to the [Elasticsearch documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html).
|
||
|
|
|
||
|
|
The [`elasticsearch-labs`](https://github.com/elastic/elasticsearch-labs) repo contains executable Python notebooks, sample apps, and resources for testing out the Elastic platform.
|
||
|
|
|
||
|
|
## OpenAI cookbook notebooks 📒
|
||
|
|
|
||
|
|
Check out our notebooks in this repo for working with OpenAI, using Elasticsearch as your vector database.
|
||
|
|
|
||
|
|
### [Semantic search](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb)
|
||
|
|
|
||
|
|
In this notebook you'll learn how to:
|
||
|
|
|
||
|
|
- Index the OpenAI Wikipedia embeddings dataset into Elasticsearch
|
||
|
|
- Encode a question with the `openai ada-02` model
|
||
|
|
- Perform a semantic search
|
||
|
|
|
||
|
|
<hr>
|
||
|
|
|
||
|
|
|
||
|
|
### [Retrieval augmented generation](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-retrieval-augmented-generation.ipynb)
|
||
|
|
|
||
|
|
This notebooks builds on the semantic search notebook by:
|
||
|
|
|
||
|
|
- Selecting the top hit from a semantic search
|
||
|
|
- Sending that result to the OpenAI [Chat Completions](https://platform.openai.com/docs/guides/gpt/chat-completions-api) API endpoint for retrieval augmented generation (RAG)
|
||
|
|
|