gpt3_rs 0.3.0

An API wrapper for OpenAi's gpt-3
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160

//! Classifies a query from provided context
//! # Builder
//! Use the [`classifications::Builder`][struct@Builder] to construct a [`classifications::Request`][Request] struct
use std::collections::HashMap;

use derive_builder::Builder;
use serde::{Deserialize, Serialize};

use crate::into_vec::IntoVec;
use crate::model::Model;
use crate::OPENAI_URL;

use super::{LogProbs, RequestInfo};
/// Classifies a query from provided context
///
/// # OpenAi documentation
/// Classifies the specified query using provided examples.
///
/// The endpoint first searches over the labeled examples to select the ones most relevant for the particular query.
/// Then, the relevant examples are combined with the query to construct a prompt to produce the final label via the completions endpoint.
/// Labeled examples can be provided via an uploaded file, or explicitly listed in the request using the examples parameter for quick tests and small scale use cases.
///
/// # Example
/// ```ignore
/// let request = classifications::Builder::default()
///     .model(Model::Curie)
///     .search_model(Model::Ada)
///     .query("It is a rainy day :(")
///     .examples(&[
///         &["A happy moment", "Positive"],
///         &["I am sad.", "Negative"],
///         &["I am feeling awesome", "Positive"]
///      ])
///     .labels(&["Positive", "Negative", "Neutral"])
///     .build()
///     .unwrap();
/// ```
/// # Required
/// ```ignore
/// model, query
/// ```
#[derive(Debug, Clone, PartialEq, Serialize, Builder)]
#[builder_struct_attr(doc = "# Required")]
#[builder_struct_attr(doc = "[`model`](Self::model())")]
#[builder_struct_attr(doc = "[`query`](Self::query())")]
#[builder_struct_attr(doc = "")]
#[builder(name = "Builder")]
pub struct Request {
    /// ID of the engine to use for completion. You can select one of ada, babbage, curie, or davinci.
    pub model: Model,
    /// Query to be classified.
    #[builder(setter(into))]
    pub query: String,
    /// A list of examples with labels, in the following format:
    /// `[["The movie is so interesting.", "Positive"], ["It is quite boring.", "Negative"], ...]`
    /// All the label strings will be normalized to be capitalized.
    /// You should specify either examples or file, but not both.
    #[builder(default, setter(strip_option, into))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub examples: Option<IntoVec<Vec<String>>>,
    /// The ID of the uploaded file that contains training examples. See upload file for how to upload a file of the desired format and purpose.
    /// You should specify either examples or file, but not both.
    #[builder(default, setter(strip_option, into))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub file: Option<String>,
    /// The set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide.
    /// All the label strings will be normalized to be capitalized.
    #[builder(default, setter(strip_option, into))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub labels: Option<IntoVec<String>>,
    /// ID of the engine to use for Search. You can select one of ada, babbage, curie, or davinci.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub search_model: Option<Model>,
    /// What sampling temperature to use. Higher values mean the model will take more risks.
    /// Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub temperature: Option<f64>,
    /// Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens.
    ///  The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.
    /// The maximum value for logprobs is 5. If you need more than this, please contact support@openai.com and describe your use case.
    /// When logprobs is set, completion will be automatically added into expand to get the logprobs.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub logprobs: Option<u8>,
    /// The maximum number of examples to be ranked by Search when using file.
    /// Setting it to a higher value leads to improved accuracy but with increased latency and cost.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub max_examples: Option<u64>,
    /// Modify the likelihood of specified tokens appearing in the completion.
    /// Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100.
    /// You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs.
    /// Mathematically, the bias is added to the logits generated by the model prior to sampling.
    /// The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection;
    /// values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
    /// As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub logit_bias: Option<HashMap<String, i8>>,
    /// If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion.
    /// This is mainly useful for debugging purposes.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub return_prompt: Option<bool>,
    /// A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field.
    /// This flag only takes effect when file is set.
    #[builder(default, setter(strip_option))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub return_metadata: Option<bool>,
    /// If an object name is in the list, we provide the full information of the object;
    /// otherwise, we only provide the object ID. Currently we support completion and file objects for expansion.
    #[builder(default, setter(strip_option, into))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub expand: Option<IntoVec<String>>,
    /// A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
    #[builder(default, setter(strip_option, into))]
    #[serde(skip_serializing_if = "Option::is_none")]
    pub user: Option<String>,
}

/// A response corresponding to a [`Request`]
#[derive(Default, Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct Response {
    /// completion id
    pub completion: String,
    /// The chosen label for the query
    pub label: String,
    /// The model used for the completion of the request
    pub model: String,
    /// The requested action
    pub object: String,
    /// The model used for the search
    pub search_model: String,
    /// The examples used to judge the query
    pub selected_examples: Vec<SelectedExample>,
}

#[derive(Default, Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct SelectedExample {
    /// The document the example is in
    pub document: usize,
    /// The label of the example
    pub label: String,
    /// The text of the example
    pub text: String,
    /// A list of the n most likely tokens
    pub logpropbs: Option<LogProbs>,
}

impl RequestInfo for Request {
    fn url(&self) -> String {
        format!("{OPENAI_URL}/classifications")
    }
}
#[cfg_attr(not(feature = "blocking"), async_trait::async_trait)]
impl crate::client::Request for Request {
    type Response = Response;
}