https://github.com/elastic/elasticsearch-labs
Notebooks & Example Apps for Search & AI Applications with Elasticsearch
https://github.com/elastic/elasticsearch-labs
ai applications chatgpt chatlog elastic elasticsearch genai genaistack langchain langchain-python openai openai-chatgpt python search vector vectordatabase
Last synced: 2 months ago
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Notebooks & Example Apps for Search & AI Applications with Elasticsearch
- Host: GitHub
- URL: https://github.com/elastic/elasticsearch-labs
- Owner: elastic
- License: apache-2.0
- Created: 2023-06-14T17:50:22.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-11T03:21:40.000Z (2 months ago)
- Last Synced: 2025-04-11T04:24:12.639Z (2 months ago)
- Topics: ai, applications, chatgpt, chatlog, elastic, elasticsearch, genai, genaistack, langchain, langchain-python, openai, openai-chatgpt, python, search, vector, vectordatabase
- Language: Jupyter Notebook
- Homepage: https://www.elastic.co/search-labs
- Size: 115 MB
- Stars: 817
- Watchers: 207
- Forks: 212
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Support: supporting-blog-content/Boston-Celtics-Demo/celtics-esql-demo.ipynb
Awesome Lists containing this project
- awesome-genai - Elasticsearch Labs - Notebooks & Example Apps for Search & AI Applications with Elasticsearch. [](https://github.com/elastic/elasticsearch-labs/network/members) [](https://github.com/elastic/elasticsearch-labs/stargazers) (Learning Resources / Tutorials)
- awesome-genai - Elasticsearch Labs - Notebooks & Example Apps for Search & AI Applications with Elasticsearch. [](https://github.com/elastic/elasticsearch-labs/network/members) [](https://github.com/elastic/elasticsearch-labs/stargazers) (Learning Resources / Tutorials)
README
# Elasticsearch Examples & Apps
**Visit [Search Labs](https://www.elastic.co/search-labs) for the latest articles and tutorials on using Elasticsearch for search and AI/ML-powered search experiences**
This repo contains executable Python notebooks, sample apps, and resources for testing out the Elastic platform:
- Learn how to use Elasticsearch as a vector database to store embeddings, power hybrid and semantic search experiences.
- Build use cases such as retrieval augmented generation (RAG), summarization, and question answering (QA).
- Test Elastic's leading-edge, out-of-the-box capabilities like the [Elastic Learned Sparse Encoder](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html) and [reciprocal rank fusion (RRF)](), which produce best-in-class results without training or tuning.
- Integrate with projects like OpenAI, Hugging Face, and LangChain, and use Elasticsearch as the backbone of your LLM-powered applications.
Elastic enables all modern search experiences powered by AI/ML.
- Bookmark or subscribe to [Elasticsearch Labs on Github](https://github.com/elastic/elasticsearch-labs)
- Read our latest articles at [elastic.co/search-labs](https://www.elastic.co/search-labs)
# Apps
- [Chatbot RAG App](./example-apps/chatbot-rag-app/)
- [Internal Knowledge Search](./example-apps/internal-knowledge-search)
- [Relevance Workbench](./example-apps/relevance-workbench)
# Python notebooks 📒
The [`notebooks`](notebooks/README.md) folder contains a range of executable Python notebooks, so you can test these features out for yourself. Colab provides an easy-to-use Python virtual environment in the browser.
### Generative AI
- [`question-answering.ipynb`](./notebooks/generative-ai/question-answering.ipynb)
- [`chatbot.ipynb`](./notebooks/generative-ai/chatbot.ipynb)
### Playground RAG Notebooks
Try out Playground in Kibana with the following notebooks:
- [`OpenAI Example`](./notebooks/playground-examples/openai-elasticsearch-client.ipynb)
- [`Anthropic Claude 3 Example`](./notebooks/playground-examples/bedrock-anthropic-elasticsearch-client.ipynb)
### LangChain
- [`question-answering.ipynb`](./notebooks/generative-ai/question-answering.ipynb)
- [`langchain-self-query-retriever.ipynb`](./notebooks/langchain/self-query-retriever-examples/langchain-self-query-retriever.ipynb)
- [`Question Answering with Self Query Retriever`](./notebooks/langchain/self-query-retriever-examples/chatbot-example.ipynb)
- [`BM25 and Self-querying retriever with elasticsearch and LangChain`](./notebooks/langchain/self-query-retriever-examples/chatbot-with-bm25-only-example.ipynb)
- [`langchain-vector-store.ipynb`](./notebooks/langchain/langchain-vector-store.ipynb)
- [`langchain-vector-store-using-elser.ipynb`](./notebooks/langchain/langchain-vector-store-using-elser.ipynb)
- [`langchain-using-own-model.ipynb`](./notebooks/langchain/langchain-using-own-model.ipynb)
### Document Chunking
- [`Document Chunking with Ingest Pipelines`](./notebooks/document-chunking/with-index-pipelines.ipynb)
- [`Document Chunking with LangChain Splitters`](./notebooks/document-chunking/with-langchain-splitters.ipynb)
- [`Calculating tokens for Semantic Search (ELSER and E5)`](./notebooks/document-chunking/tokenization.ipynb)
- [`Fetch surrounding chunks`](./supporting-blog-content/fetch-surrounding-chunks/fetch-surrounding-chunks.ipynb)
### Search
- [`00-quick-start.ipynb`](./notebooks/search/00-quick-start.ipynb)
- [`01-keyword-querying-filtering.ipynb`](./notebooks/search/01-keyword-querying-filtering.ipynb)
- [`02-hybrid-search.ipynb`](./notebooks/search/02-hybrid-search.ipynb)
- [`03-ELSER.ipynb`](./notebooks/search/03-ELSER.ipynb)
- [`04-multilingual.ipynb`](./notebooks/search/04-multilingual.ipynb)
- [`05-query-rules.ipynb`](./notebooks/search/05-query-rules.ipynb)
- [`06-synonyms-api.ipynb`](./notebooks/search/06-synonyms-api.ipynb)
- [`07-inference.ipynb`](./notebooks/search/07-inference.ipynb)
- [`08-learning-to-rank.ipynb`](./notebooks/search/08-learning-to-rank.ipynb)
- [`09-semantic-text.ipynb`](./notebooks/search/09-semantic-text.ipynb)
#### Semantic reranking
- [`10-semantic-reranking-retriever-cohere.ipynb`](./notebooks/search/10-semantic-reranking-retriever-cohere.ipynb)
- [`11-semantic-reranking-hugging-face.ipynb`](./notebooks/search/11-semantic-reranking-hugging-face.ipynb)
### Integrations
- [`loading-model-from-hugging-face.ipynb`](./notebooks/integrations/hugging-face/loading-model-from-hugging-face.ipynb)
- [`openai-semantic-search-RAG.ipynb`](./notebooks/integrations/openai/openai-KNN-RAG.ipynb)
- [`amazon-bedrock-langchain-qa-example.ipynb`](notebooks/integrations/amazon-bedrock/langchain-qa-example.ipynb)
- [`Semantic Search using the Inference API with the Cohere Service`](/notebooks/integrations/cohere/inference-cohere.ipynb)
### Model Upgrades
- [`upgrading-index-to-use-elser.ipynb`](notebooks/model-upgrades/upgrading-index-to-use-elser.ipynb)
# Contributing 🎁
See [contributing guidelines](CONTRIBUTING.md).
# Support 🛟
The Search team at Elastic maintains this repository and is happy to help.
### Official Support Services
If you have an Elastic subscription, you are entitled to Support services for your Elasticsearch deployment. See our welcome page for [working with our support team](https://www.elastic.co/support/welcome).
These services do not apply to the sample application code contained in this repository.
### Discuss Forum
Try posting your question to the [Elastic discuss forums](https://discuss.elastic.co/) and tag it with [#esre-elasticsearch-relevance-engine](https://discuss.elastic.co/tag/esre-elasticsearch-relevance-engine)
### Elastic Slack
You can also find us in the [#search-esre-relevance-engine](https://elasticstack.slack.com/archives/C05CED61S9J) channel of the [Elastic Community Slack](http://elasticstack.slack.com)
# License ⚖️
This software is licensed under the [Apache License, version 2 ("ALv2")](https://github.com/elastic/elasticsearch-labs/blob/main/LICENSE).