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README.md

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Revert "Add support for fine-tunning and files using the Azure API. (#76)" (#79)718e7a4

4 years ago

1	`# OpenAI Python Library`
2
3	`The OpenAI Python library provides convenient access to the OpenAI API`
4	`from applications written in the Python language. It includes a`
5	`pre-defined set of classes for API resources that initialize`
6	`themselves dynamically from API responses which makes it compatible`
7	`with a wide range of versions of the OpenAI API.`
8
9	`## Documentation`
10
11	`See the [OpenAI API docs](https://beta.openai.com/docs/api-reference?lang=python).`
12
13	`## Installation`
14
15	`You don't need this source code unless you want to modify the package. If you just`
16	`want to use the package, just run:`
17
18	```sh
19	`pip install --upgrade openai`
20	```
21
22	`Install from source with:`
23
24	```sh
25	`python setup.py install`
26	```
27
28	`## Usage`
29
30	The library needs to be configured with your account's secret key which is available on the [website](https://beta.openai.com/account/api-keys). Either set it as the `OPENAI_API_KEY` environment variable before using the library:
31
32	```bash
33	`export OPENAI_API_KEY='sk-...'`
34	```
35
36	Or set `openai.api_key` to its value:
37
38	```python
39	`import openai`
40	`openai.api_key = "sk-..."`
41
42	`# list engines`
43	`engines = openai.Engine.list()`
44
45	`# print the first engine's id`
46	`print(engines.data[0].id)`
47
48	`# create a completion`
49	`completion = openai.Completion.create(engine="ada", prompt="Hello world")`
50
51	`# print the completion`
52	`print(completion.choices[0].text)`
53	```
54
55	`### Microsoft Azure Endpoints`
56
57	`In order to use the library with Microsoft Azure endpoints, you need to set the api_type, api_base and api_version in addition to the api_key. The api_type must be set to 'azure' and the others correspond to the properites of your endpoint.`
58	`In addition, the deployment name must be passed as the engine parameter.`
59
60	```python
61	`import openai`
62	`openai.api_type = "azure"`
63	`openai.api_key = "..."`
64	`openai.api_base = "https://example-endpoint.openai.azure.com"`
65	`openai.api_version = "2021-11-01-preview"`
66
67	`# create a completion`
68	`completion = openai.Completion.create(engine="deployment-namme", prompt="Hello world")`
69
70	`# print the completion`
71	`print(completion.choices[0].text)`
72
73	`# create a search and pass the deployment-name as the engine Id.`
74	`search = openai.Engine(id="deployment-namme").search(documents=["White House", "hospital", "school"], query ="the president")`
75
76	`# print the search`
77	`print(search)`
78	```
79
80	`Please note that for the moment, the Microsoft Azure endpoints can only be used for completion and search operations.`
81
82	`### Command-line interface`
83
84	This library additionally provides an `openai` command-line utility
85	`which makes it easy to interact with the API from your terminal. Run`
86	`openai api -h` for usage.
87
88	```sh
89	`# list engines`
90	`openai api engines.list`
91
92	`# create a completion`
93	`openai api completions.create -e ada -p "Hello world"`
94	```
95
96	`## Example code`
97
98	`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).`
99
100	`### Embeddings`
101
102	`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.`
103
104	`To get an embedding for a text string, you can use the embeddings method as follows in Python:`
105
106	```python
107	`import openai`
108	`openai.api_key = "sk-..." # supply your API key however you choose`
109
110	`# choose text to embed`
111	`text_string = "sample text"`
112
113	`# choose an embedding`
114	`model_id = "text-similarity-davinci-001"`
115
116	`# compute the embedding of the text`
117	`embedding = openai.Embedding.create(input=text_string, engine=model_id)['data'][0]['embedding']`
118	```
119
120	`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).`
121
122	`Examples of how to use embeddings are shared in the following Jupyter notebooks:`
123
124	`- [Classification using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Classification.ipynb)`
125	`- [Clustering using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Clustering.ipynb)`
126	`- [Code search using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Code_search.ipynb)`
127	`- [Semantic text search using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Semantic_text_search_using_embeddings.ipynb)`
128	`- [User and product embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/User_and_product_embeddings.ipynb)`
129	`- [Zero-shot classification using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Zero-shot_classification.ipynb)`
130	`- [Recommendation using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Recommendation.ipynb)`
131
132	`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.`
133
134	`### Fine tuning`
135
136	`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).`
137
138	`Examples of fine tuning are shared in the following Jupyter notebooks:`
139
140	`- [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)`
141	`- Fine tuning a model that answers questions about the 2020 Olympics`
142	`- [Step 1: Collecting data](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-1-collect-data.ipynb)`
143	`- [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-2-create-qa.ipynb)`
144	`- [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)`
145
146	`Sync your fine-tunes to [Weights & Biases](https://wandb.me/openai-docs) to track experiments, models, and datasets in your central dashboard with:`
147
148	```bash
149	`openai wandb sync`
150	```
151
152	`For more information on fine tuning, read the [fine-tuning guide](https://beta.openai.com/docs/guides/fine-tuning) in the OpenAI documentation.`
153
154	`## Requirements`
155
156	`- Python 3.7.1+`
157
158	`In general we want to support the versions of Python that our`
159	`customers are using, so if you run into issues with any version`
160	`issues, please let us know at support@openai.com.`
161
162	`## Credit`
163
164	`This library is forked from the [Stripe Python Library](https://github.com/stripe/stripe-python).`

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