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| 1 | +// Copyright 2025 Muvon Un Limited |
| 2 | +// |
| 3 | +// Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +// you may not use this file except in compliance with the License. |
| 5 | +// You may obtain a copy of the License at |
| 6 | +// |
| 7 | +// http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +// |
| 9 | +// Unless required by applicable law or agreed to in writing, software |
| 10 | +// distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +// See the License for the specific language governing permissions and |
| 13 | +// limitations under the License. |
| 14 | + |
| 15 | +//! OpenAI embedding provider implementation |
| 16 | +
|
| 17 | +use anyhow::{Context, Result}; |
| 18 | +use serde_json::{json, Value}; |
| 19 | + |
| 20 | +use super::super::types::InputType; |
| 21 | +use super::{EmbeddingProvider, HTTP_CLIENT}; |
| 22 | + |
| 23 | +/// OpenAI provider implementation for trait |
| 24 | +pub struct OpenAIProviderImpl { |
| 25 | + model_name: String, |
| 26 | + dimension: usize, |
| 27 | +} |
| 28 | + |
| 29 | +impl OpenAIProviderImpl { |
| 30 | + pub fn new(model: &str) -> Result<Self> { |
| 31 | + // Validate model first - fail fast if unsupported |
| 32 | + let supported_models = [ |
| 33 | + "text-embedding-3-small", |
| 34 | + "text-embedding-3-large", |
| 35 | + "text-embedding-ada-002", |
| 36 | + ]; |
| 37 | + |
| 38 | + if !supported_models.contains(&model) { |
| 39 | + return Err(anyhow::anyhow!( |
| 40 | + "Unsupported OpenAI model: '{}'. Supported models: {:?}", |
| 41 | + model, |
| 42 | + supported_models |
| 43 | + )); |
| 44 | + } |
| 45 | + |
| 46 | + let dimension = Self::get_model_dimension(model); |
| 47 | + Ok(Self { |
| 48 | + model_name: model.to_string(), |
| 49 | + dimension, |
| 50 | + }) |
| 51 | + } |
| 52 | + |
| 53 | + fn get_model_dimension(model: &str) -> usize { |
| 54 | + match model { |
| 55 | + "text-embedding-3-small" => 1536, |
| 56 | + "text-embedding-3-large" => 3072, |
| 57 | + "text-embedding-ada-002" => 1536, |
| 58 | + _ => { |
| 59 | + // This should never be reached due to validation in new() |
| 60 | + panic!( |
| 61 | + "Invalid OpenAI model '{}' passed to get_model_dimension", |
| 62 | + model |
| 63 | + ); |
| 64 | + } |
| 65 | + } |
| 66 | + } |
| 67 | +} |
| 68 | + |
| 69 | +#[async_trait::async_trait] |
| 70 | +impl EmbeddingProvider for OpenAIProviderImpl { |
| 71 | + async fn generate_embedding(&self, text: &str) -> Result<Vec<f32>> { |
| 72 | + OpenAIProvider::generate_embeddings(text, &self.model_name).await |
| 73 | + } |
| 74 | + |
| 75 | + async fn generate_embeddings_batch( |
| 76 | + &self, |
| 77 | + texts: Vec<String>, |
| 78 | + input_type: InputType, |
| 79 | + ) -> Result<Vec<Vec<f32>>> { |
| 80 | + OpenAIProvider::generate_embeddings_batch(texts, &self.model_name, input_type).await |
| 81 | + } |
| 82 | + |
| 83 | + fn get_dimension(&self) -> usize { |
| 84 | + self.dimension |
| 85 | + } |
| 86 | + |
| 87 | + fn is_model_supported(&self) -> bool { |
| 88 | + // REAL validation - only support actual OpenAI models, NO HALLUCINATIONS |
| 89 | + matches!( |
| 90 | + self.model_name.as_str(), |
| 91 | + "text-embedding-3-small" | "text-embedding-3-large" | "text-embedding-ada-002" |
| 92 | + ) |
| 93 | + } |
| 94 | +} |
| 95 | + |
| 96 | +/// OpenAI provider implementation |
| 97 | +pub struct OpenAIProvider; |
| 98 | + |
| 99 | +impl OpenAIProvider { |
| 100 | + pub async fn generate_embeddings(contents: &str, model: &str) -> Result<Vec<f32>> { |
| 101 | + let result = |
| 102 | + Self::generate_embeddings_batch(vec![contents.to_string()], model, InputType::None) |
| 103 | + .await?; |
| 104 | + result |
| 105 | + .first() |
| 106 | + .cloned() |
| 107 | + .ok_or_else(|| anyhow::anyhow!("No embeddings found")) |
| 108 | + } |
| 109 | + |
| 110 | + pub async fn generate_embeddings_batch( |
| 111 | + texts: Vec<String>, |
| 112 | + model: &str, |
| 113 | + input_type: InputType, |
| 114 | + ) -> Result<Vec<Vec<f32>>> { |
| 115 | + let openai_api_key = std::env::var("OPENAI_API_KEY") |
| 116 | + .context("OPENAI_API_KEY environment variable not set")?; |
| 117 | + |
| 118 | + // Apply input type prefixes since OpenAI doesn't have native input_type support |
| 119 | + let processed_texts: Vec<String> = texts |
| 120 | + .into_iter() |
| 121 | + .map(|text| input_type.apply_prefix(&text)) |
| 122 | + .collect(); |
| 123 | + |
| 124 | + // Build request body |
| 125 | + let request_body = json!({ |
| 126 | + "input": processed_texts, |
| 127 | + "model": model, |
| 128 | + "encoding_format": "float" |
| 129 | + }); |
| 130 | + |
| 131 | + let response = HTTP_CLIENT |
| 132 | + .post("https://api.openai.com/v1/embeddings") |
| 133 | + .header("Authorization", format!("Bearer {}", openai_api_key)) |
| 134 | + .header("Content-Type", "application/json") |
| 135 | + .json(&request_body) |
| 136 | + .send() |
| 137 | + .await?; |
| 138 | + |
| 139 | + if !response.status().is_success() { |
| 140 | + let error_text = response.text().await?; |
| 141 | + return Err(anyhow::anyhow!("OpenAI API error: {}", error_text)); |
| 142 | + } |
| 143 | + |
| 144 | + let response_json: Value = response.json().await?; |
| 145 | + |
| 146 | + let embeddings = response_json["data"] |
| 147 | + .as_array() |
| 148 | + .context("Failed to get embeddings array")? |
| 149 | + .iter() |
| 150 | + .map(|data| { |
| 151 | + data["embedding"] |
| 152 | + .as_array() |
| 153 | + .unwrap_or(&Vec::new()) |
| 154 | + .iter() |
| 155 | + .map(|v| v.as_f64().unwrap_or_default() as f32) |
| 156 | + .collect() |
| 157 | + }) |
| 158 | + .collect(); |
| 159 | + |
| 160 | + Ok(embeddings) |
| 161 | + } |
| 162 | +} |
| 163 | + |
| 164 | +#[cfg(test)] |
| 165 | +mod tests { |
| 166 | + use super::*; |
| 167 | + |
| 168 | + #[test] |
| 169 | + fn test_openai_provider_creation() { |
| 170 | + // Test valid models |
| 171 | + assert!(OpenAIProviderImpl::new("text-embedding-3-small").is_ok()); |
| 172 | + assert!(OpenAIProviderImpl::new("text-embedding-3-large").is_ok()); |
| 173 | + assert!(OpenAIProviderImpl::new("text-embedding-ada-002").is_ok()); |
| 174 | + |
| 175 | + // Test invalid model |
| 176 | + assert!(OpenAIProviderImpl::new("invalid-model").is_err()); |
| 177 | + } |
| 178 | + |
| 179 | + #[test] |
| 180 | + fn test_model_dimensions() { |
| 181 | + let provider_small = OpenAIProviderImpl::new("text-embedding-3-small").unwrap(); |
| 182 | + assert_eq!(provider_small.get_dimension(), 1536); |
| 183 | + |
| 184 | + let provider_large = OpenAIProviderImpl::new("text-embedding-3-large").unwrap(); |
| 185 | + assert_eq!(provider_large.get_dimension(), 3072); |
| 186 | + |
| 187 | + let provider_ada = OpenAIProviderImpl::new("text-embedding-ada-002").unwrap(); |
| 188 | + assert_eq!(provider_ada.get_dimension(), 1536); |
| 189 | + } |
| 190 | + |
| 191 | + #[test] |
| 192 | + fn test_model_validation() { |
| 193 | + let provider_valid = OpenAIProviderImpl::new("text-embedding-3-small").unwrap(); |
| 194 | + assert!(provider_valid.is_model_supported()); |
| 195 | + |
| 196 | + // This would panic if we tried to create an invalid model, so we test indirectly |
| 197 | + let supported_models = [ |
| 198 | + "text-embedding-3-small", |
| 199 | + "text-embedding-3-large", |
| 200 | + "text-embedding-ada-002", |
| 201 | + ]; |
| 202 | + for model in supported_models { |
| 203 | + let provider = OpenAIProviderImpl::new(model).unwrap(); |
| 204 | + assert!(provider.is_model_supported()); |
| 205 | + } |
| 206 | + } |
| 207 | +} |
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