async-openai 0.18.3

Rust library for OpenAI
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

use crate::{
    config::Config,
    error::OpenAIError,
    types::{CreateEmbeddingRequest, CreateEmbeddingResponse},
    Client,
};

/// Get a vector representation of a given input that can be easily
/// consumed by machine learning models and algorithms.
///
/// Related guide: [Embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)
pub struct Embeddings<'c, C: Config> {
    client: &'c Client<C>,
}

impl<'c, C: Config> Embeddings<'c, C> {
    pub fn new(client: &'c Client<C>) -> Self {
        Self { client }
    }

    /// Creates an embedding vector representing the input text.
    pub async fn create(
        &self,
        request: CreateEmbeddingRequest,
    ) -> Result<CreateEmbeddingResponse, OpenAIError> {
        self.client.post("/embeddings", request).await
    }
}

#[cfg(test)]
mod tests {
    use crate::{types::CreateEmbeddingRequestArgs, Client};
    use crate::types::{CreateEmbeddingResponse, Embedding};

    #[tokio::test]
    async fn test_embedding_string() {
        let client = Client::new();

        let request = CreateEmbeddingRequestArgs::default()
            .model("text-embedding-ada-002")
            .input("The food was delicious and the waiter...")
            .build()
            .unwrap();

        let response = client.embeddings().create(request).await;

        assert!(response.is_ok());
    }

    #[tokio::test]
    async fn test_embedding_string_array() {
        let client = Client::new();

        let request = CreateEmbeddingRequestArgs::default()
            .model("text-embedding-ada-002")
            .input(["The food was delicious", "The waiter was good"])
            .build()
            .unwrap();

        let response = client.embeddings().create(request).await;

        assert!(response.is_ok());
    }

    #[tokio::test]
    async fn test_embedding_integer_array() {
        let client = Client::new();

        let request = CreateEmbeddingRequestArgs::default()
            .model("text-embedding-ada-002")
            .input([1, 2, 3])
            .build()
            .unwrap();

        let response = client.embeddings().create(request).await;

        assert!(response.is_ok());
    }

    #[tokio::test]
    async fn test_embedding_array_of_integer_array_matrix() {
        let client = Client::new();

        let request = CreateEmbeddingRequestArgs::default()
            .model("text-embedding-ada-002")
            .input([[1, 2, 3], [4, 5, 6], [7, 8, 10]])
            .build()
            .unwrap();

        let response = client.embeddings().create(request).await;

        assert!(response.is_ok());
    }

    #[tokio::test]
    async fn test_embedding_array_of_integer_array() {
        let client = Client::new();

        let request = CreateEmbeddingRequestArgs::default()
            .model("text-embedding-ada-002")
            .input([vec![1, 2, 3], vec![4, 5, 6, 7], vec![7, 8, 10, 11, 100257]])
            .build()
            .unwrap();

        let response = client.embeddings().create(request).await;

        assert!(response.is_ok());
    }

    #[tokio::test]
    async fn test_embedding_with_reduced_dimensions() {
        let client = Client::new();
        let dimensions = 256u32;
        let request = CreateEmbeddingRequestArgs::default()
            .model("text-embedding-3-small")
            .input("The food was delicious and the waiter...")
            .dimensions(dimensions)
            .build()
            .unwrap();

        let response = client.embeddings().create(request).await;

        assert!(response.is_ok());

        let CreateEmbeddingResponse { mut data, ..} = response.unwrap();
        assert_eq!(data.len(), 1);
        let Embedding { embedding, .. } = data.pop().unwrap();
        assert_eq!(embedding.len(), dimensions as usize);
    }
}