文件列表:

LICENSE (1064, 2022-12-23)
OpenAI.Net.sln (7190, 2022-12-23)
icon.png (140883, 2022-12-23)
logo.png (93417, 2022-12-23)
samples (0, 2022-12-23)
samples\01_Completions (0, 2022-12-23)
samples\01_Completions\01_Completions.csproj (310, 2022-12-23)
samples\01_Completions\Program.cs (908, 2022-12-23)
samples\02_Searches (0, 2022-12-23)
samples\02_Searches\02_Searches.csproj (304, 2022-12-23)
samples\02_Searches\Program.cs (1170, 2022-12-23)
samples\03_Classifications (0, 2022-12-23)
samples\03_Classifications\03_Classifications.csproj (311, 2022-12-23)
samples\03_Classifications\Program.cs (1401, 2022-12-23)
samples\04_Answers (0, 2022-12-23)
samples\04_Answers\04_Answers.csproj (303, 2022-12-23)
samples\04_Answers\Program.cs (1692, 2022-12-23)
samples\FactualAnswering (0, 2022-12-23)
samples\FactualAnswering\FactualAnswering.csproj (256, 2022-12-23)
samples\FactualAnswering\Program.cs (3057, 2022-12-23)
samples\MoodToColor (0, 2022-12-23)
samples\MoodToColor\MoodToColor.csproj (256, 2022-12-23)
samples\MoodToColor\Program.cs (1379, 2022-12-23)
samples\MovieToEmoji (0, 2022-12-23)
samples\MovieToEmoji\MovieToEmoji.csproj (256, 2022-12-23)
samples\MovieToEmoji\Program.cs (2078, 2022-12-23)
samples\NotesToSummary (0, 2022-12-23)
samples\NotesToSummary\NotesToSummary.csproj (256, 2022-12-23)
samples\NotesToSummary\Program.cs (1918, 2022-12-23)
samples\SQLTranslate (0, 2022-12-23)
samples\SQLTranslate\Program.cs (2336, 2022-12-23)
samples\SQLTranslate\SQLTranslate.csproj (256, 2022-12-23)
src (0, 2022-12-23)
src\APIAuthentication.cs (4951, 2022-12-23)
src\Answers (0, 2022-12-23)
src\Answers\AnswerEndpoint.cs (2042, 2022-12-23)
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OpenAI.Net

An unofficial .NET API wrapper for OpenAI.

## Installation Stable builds are available through [NuGet](https://www.nuget.org/packages/OpenAI.Net). ``` Install-Package OpenAI.Net ``` ## Authentication There are 3 ways to provide your API keys, in order of precedence: 1. Pass keys directly to `APIAuthentication(string key)` constructor 2. Set environment var for OPENAI_KEY 3. Include a config file in the local directory or in your user directory named `.openai` and containing the line: ```shell OPENAI_KEY=sk-yourapikey ``` You use the `APIAuthentication` when you initialize the API as shown: ```csharp var api = new OpenAIAPI("YOUR_API_KEY_HERE"); // or var api = new OpenAIAPI(new APIAuthentication("sk-yourapikey")); // create object manually // or var api = new OpenAIAPI(APIAuthentication LoadFromEnv()); // use env vars // or var api = new OpenAIAPI(APIAuthentication LoadFromPath()); // use config file (optionally specify where to look) // or var api = new OpenAIAPI(); // uses default, env, or config file ``` ## Examples You can view built examples in the [samples](https://github.com/WilliamWelsh/OpenAI.Net/tree/main/samples) folder. These examples include very basic access of endpoints, as well as versions of [OpenAI's example applications](https://beta.openai.com/examples) using this wrapper. ### Completions Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. [View on OpenAI](https://beta.openai.com/docs/api-reference/completions). ```csharp var api = new OpenAIAPI(apiKeys: "YOUR_API_KEY_HERE", engine: Engine.Davinci); var request = new CompletionRequestBuilder() .WithPrompt("Once upon a time") .WithMaxTokens(5) .Build(); var result = await api.Completions.CreateCompletionAsync(request); Console.WriteLine(result.ToString()); // Should print something like ", there was a girl who" ``` ### Searches Given a query and a set of documents or labels, the model ranks each document based on its semantic similarity to the provided query. [View on OpenAI](https://beta.openai.com/docs/api-reference/searches). ```csharp var api = new OpenAIAPI(apiKeys: "YOUR_API_KEY_HERE", engine: Engine.Davinci); var request = new SearchRequestBuilder() .WithQuery("the president") .WithDocuments(new List { "White House", "hospital", "school" }) .Build(); var result = await api.Search.GetBestMatchAsync(request); Console.WriteLine(result); // Should print "White House" ``` ### Classifications Given a query and a set of labeled examples, the model will predict the most likely label for the query. Useful as a drop-in replacement for any ML classification or text-to-label task. [View on OpenAI](https://beta.openai.com/docs/api-reference/classifications). ```csharp var api = new OpenAIAPI(apiKeys: "YOUR_API_KEY_HERE", engine: Engine.Davinci); var request = new ClassificationRequestBuilder() .WithExamples(new List> { new List { "A happy moment", "Positive" }, new List { "I am sad.", "Negative" }, new List { "I am feeling awesome", "Positive" } }) .WithLabels(new List { "Positive", "Negative", "Neutral" }) .WithQuery("It is a raining day :(") .WithSearchModel(Engine.Ada) .WithModel(Engine.Curie) .Build(); var result = await api.Classifications.CreateClassificationAsync(request); Console.WriteLine(result.Label); // Should print "Negative" ``` ### Answers Given a question, a set of documents, and some examples, the API generates an answer to the question based on the information in the set of documents. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base. [View on OpenAI](https://beta.openai.com/docs/api-reference/answers). ```csharp var api = new OpenAIAPI(apiKeys: "YOUR_API_KEY_HERE", engine: Engine.Davinci); var request = new AnswerRequestBuilder() .WithDocuments(new List { "Puppy A is happy.", "Puppy B is sad." }) .WithQuestion("which puppy is happy?") .WithSearchModel(Engine.Ada) .WithModel(Engine.Curie) .WithExamplesContext("In 2017, U.S. life expectancy was 78.6 years.") .WithExamples(new List> { new List { "What is human life expectancy in the United States?", "78 years." } }) .WithMaxTokens(5) .WithStop(new List { "\n", "<|endoftext|>" }) .Build(); var result = await api.Answers.CreateAnswerAsync(request); Console.WriteLine(result.Answers[0]); // Should print something like "puppy A." ``` ### Fine-tunes Coming soon. Feel free to make a pull request. ## Streaming Streaming allows you to get results as they are generated, which can help your application feel more responsive, especially on slow models like Davinci. Using async iterators: ```csharp IAsyncEnumerable StreamCompletionEnumerableAsync(CompletionRequest request) // Example await foreach (var token in api.Completions.StreamCompletionEnumerableAsync(new CompletionRequest("My name is Roger and I am a principal software engineer at Salesforce. This is my resume:", 200, 0.5, presencePenalty: 0.1, frequencyPenalty: 0.1))) { Console.Write(token); } ``` Or if using .NET framework or C# <8.0: ```csharp StreamCompletionAsync(CompletionRequest request, Action resultHandler) // Example await api.Completions.StreamCompletionAsync( new CompletionRequest("My name is Roger and I am a principal software engineer at Salesforce. This is my resume:", 200, 0.5, presencePenalty: 0.1, frequencyPenalty: 0.1), res => ResumeTextbox.Text += res.ToString()); ``` ### Document Search The Search API is accessed via `OpenAIAPI.Search`: You can get all results as a dictionary using ```csharp GetSearchResultsAsync(SearchRequest request) // Example var request = new SearchRequest() { Query = "Washington DC", Documents = new List { "Canada", "***", "USA", "Spain" } }; var result = await api.Search.GetSearchResultsAsync(request); // result["USA"] == 294.22 // result["Spain"] == 73.81 ``` The returned dictionary maps documents to scores. You can create your `SearchRequest` ahead of time or use one of the helper overloads for convenience, such as ```csharp GetSearchResultsAsync(string query, params string[] documents) // Example var result = await api.Search.GetSearchResultsAsync("Washington DC", "Canada", "***", "USA", "Spain"); ``` You can get only the best match using ```csharp GetBestMatchAsync(request) ``` And if you only want the best match but still want to know the score, use ```csharp GetBestMatchWithScoreAsync(request) ``` Each of those methods has similar convenience overloads to specify the request inline. ## Documentation View the documentation on [OpenAI](https://beta.openai.com/docs/introduction/overview). Feel free to add me on Discord Reverse#0069 if you have any questions. Better documentation may come later. ## Credits OkGoDoIt - Original [fork](https://github.com/OkGoDoIt/OpenAI-API-dotnet/tree/e07bfe2ddeea40380beb923f36fca***53830d7d7) from December 22, 2020.

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