octolib 0.4.2

Self-sufficient AI provider library with multi-provider support, embedding models, model validation, and cost tracking
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

// Copyright 2025 Muvon Un Limited
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

//! Google AI embedding provider implementation

use anyhow::{Context, Result};
use serde_json::{json, Value};

use super::super::types::InputType;
use super::{EmbeddingProvider, HTTP_CLIENT};

/// Google provider implementation for trait
pub struct GoogleProviderImpl {
    model_name: String,
    dimension: usize,
}

impl GoogleProviderImpl {
    pub fn new(model: &str) -> Result<Self> {
        let dimension = Self::get_model_dimension(model)?;
        Ok(Self {
            model_name: model.to_string(),
            dimension,
        })
    }

    fn get_model_dimension(model: &str) -> Result<usize> {
        match model {
			"gemini-embedding-001" => Ok(3072),  // Up to 3072 dimensions, state-of-the-art performance
			"text-embedding-005" => Ok(768),     // Specialized in English and code tasks
			"text-multilingual-embedding-002" => Ok(768), // Specialized in multilingual tasks
			_ => Err(anyhow::anyhow!(
				"Unsupported Google model: '{}'. Supported models: gemini-embedding-001 (3072d), text-embedding-005 (768d), text-multilingual-embedding-002 (768d)",
				model
			)),
		}
    }
}

#[async_trait::async_trait]
impl EmbeddingProvider for GoogleProviderImpl {
    async fn generate_embedding(&self, text: &str) -> Result<Vec<f32>> {
        GoogleProvider::generate_embeddings(text, &self.model_name).await
    }

    async fn generate_embeddings_batch(
        &self,
        texts: Vec<String>,
        input_type: InputType,
    ) -> Result<Vec<Vec<f32>>> {
        // Apply prefix manually for Google (doesn't support input_type API)
        let processed_texts: Vec<String> = texts
            .into_iter()
            .map(|text| input_type.apply_prefix(&text))
            .collect();
        GoogleProvider::generate_embeddings_batch(processed_texts, &self.model_name).await
    }

    fn get_dimension(&self) -> usize {
        self.dimension
    }

    fn is_model_supported(&self) -> bool {
        matches!(
            self.model_name.as_str(),
            "gemini-embedding-001" | "text-embedding-005" | "text-multilingual-embedding-002"
        )
    }
}

/// Google provider implementation
pub struct GoogleProvider;

impl GoogleProvider {
    /// Get list of supported models for dynamic discovery
    pub fn get_supported_models() -> Vec<&'static str> {
        vec![
            "gemini-embedding-001",
            "text-embedding-005",
            "text-multilingual-embedding-002",
        ]
    }
    pub async fn generate_embeddings(contents: &str, model: &str) -> Result<Vec<f32>> {
        let result = Self::generate_embeddings_batch(vec![contents.to_string()], model).await?;
        result
            .first()
            .cloned()
            .ok_or_else(|| anyhow::anyhow!("No embeddings found"))
    }

    pub async fn generate_embeddings_batch(
        texts: Vec<String>,
        model: &str,
    ) -> Result<Vec<Vec<f32>>> {
        let google_api_key = std::env::var("GOOGLE_API_KEY")
            .context("GOOGLE_API_KEY environment variable not set")?;

        // For batch processing, we'll need to send individual requests as Google's API structure is different
        let mut all_embeddings = Vec::new();

        for text in texts {
            let response = HTTP_CLIENT
				.post(format!("https://generativelanguage.googleapis.com/v1beta/models/{}:embedContent?key={}", model, google_api_key))
				.header("Content-Type", "application/json")
				.json(&json!({
					"content": {
						"parts": [{
							"text": text
						}]
					}
				}))
				.send()
			.await?;

            if !response.status().is_success() {
                let error_text = response.text().await?;
                return Err(anyhow::anyhow!("Google API error: {}", error_text));
            }

            let response_json: Value = response.json().await?;

            let embedding = response_json["embedding"]["values"]
                .as_array()
                .context("Failed to get embedding values")?
                .iter()
                .map(|v| v.as_f64().unwrap_or_default() as f32)
                .collect();

            all_embeddings.push(embedding);
        }

        Ok(all_embeddings)
    }
}