Examples of how to use [embeddings](https://github.com/openai/openai-python/tree/main/examples/embeddings), [fine tuning](https://github.com/openai/openai-python/tree/main/examples/finetuning), [semantic search](https://github.com/openai/openai-python/tree/main/examples/semanticsearch), and [codex](https://github.com/openai/openai-python/tree/main/examples/codex) can be found in the [examples folder](https://github.com/openai/openai-python/tree/main/examples).

In the OpenAI Python library, an embedding represents a text string as a fixed-length vector of floating point numbers. Embeddings are designed to measure the similarity or relevance between text strings.

To get an embedding for a text string, you can use the embeddings method as follows in Python:

import openai

openai.api_key = "sk-..." # supply your API key however you choose

text_string = "sample text"

model_id = "davinci-similarity"

embedding = openai.Engine(id=model_id).embeddings(input=text_string)['data'][0]['embedding']

An example of how to call the embeddings method is shown in the [get embeddings notebook](https://github.com/openai/openai-python/blob/main/examples/embeddings/Get_embeddings.ipynb).

Examples of how to use embeddings are shared in the following Jupyter notebooks:

- [Classification using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Classification.ipynb)

- [Clustering using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Clustering.ipynb)

- [Code search using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Code_search.ipynb)

- [Semantic text search using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Semantic_text_search_using_embeddings.ipynb)

- [User and product embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/User_and_product_embeddings.ipynb)

- [Zero-shot classification using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Zero-shot_classification.ipynb)

For more information on embeddings and the types of embeddings OpenAI offers, read the [embeddings guide](https://beta.openai.com/docs/guides/embeddings) in the OpenAI documentation.

Fine tuning a model on training data can both improve the results (by giving the model more examples to learn from) and reduce the cost & latency of API calls (by reducing the need to include training examples in prompts).

Examples of fine tuning are shared in the following Jupyter notebooks:

- [Classification with fine tuning](https://github.com/openai/openai-python/blob/main/examples/finetuning/finetuning-classification.ipynb) (a simple notebook that shows the steps required for fine tuning)

- Fine tuning a model that answers questions about the 2020 Olympics

- [Step 1: Collecting data](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-1-collect-data.ipynb)

- [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-2-create-qa.ipynb)

- [Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-3-train-qa.ipynb)

For more information on fine tuning, read the [fine-tuning guide](https://beta.openai.com/docs/guides/fine-tuning) in the OpenAI documentation.