langchainrust 0.2.13

A LangChain-inspired framework for building LLM applications in Rust. Supports OpenAI, Agents, Tools, Memory, Chains, RAG, BM25, Hybrid Retrieval, LangGraph, HyDE, Reranking, MultiQuery, and native Function Calling.
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

// src/embeddings/deepseek.rs
//! DeepSeek Embeddings 实现

use crate::embeddings::{Embeddings, EmbeddingError};
use crate::language_models::providers::DEEPSEEK_BASE_URL;
use async_trait::async_trait;
use serde::Deserialize;

pub const DEEPSEEK_EMBED_MODEL: &str = "deepseek-embedding";

#[derive(Debug, Clone)]
pub struct DeepSeekEmbeddingsConfig {
    pub api_key: String,
    pub base_url: String,
    pub model: String,
}

impl Default for DeepSeekEmbeddingsConfig {
    fn default() -> Self {
        Self {
            api_key: std::env::var("DEEPSEEK_API_KEY").unwrap_or_default(),
            base_url: DEEPSEEK_BASE_URL.to_string(),
            model: DEEPSEEK_EMBED_MODEL.to_string(),
        }
    }
}

impl DeepSeekEmbeddingsConfig {
    pub fn new(api_key: impl Into<String>) -> Self {
        Self {
            api_key: api_key.into(),
            ..Default::default()
        }
    }

    pub fn from_env() -> Self {
        Self::default()
    }

    pub fn with_model(mut self, model: impl Into<String>) -> Self {
        self.model = model.into();
        self
    }
}

pub struct DeepSeekEmbeddings {
    config: DeepSeekEmbeddingsConfig,
    client: reqwest::Client,
}

impl DeepSeekEmbeddings {
    pub fn new(config: DeepSeekEmbeddingsConfig) -> Self {
        Self {
            config,
            client: reqwest::Client::new(),
        }
    }

    pub fn from_env() -> Self {
        Self::new(DeepSeekEmbeddingsConfig::from_env())
    }
}

#[async_trait]
impl Embeddings for DeepSeekEmbeddings {
    async fn embed_query(&self, text: &str) -> Result<Vec<f32>, EmbeddingError> {
        if text.is_empty() {
            return Err(EmbeddingError::EmptyInput);
        }
        
        let url = format!("{}/embeddings", self.config.base_url);
        
        let body = serde_json::json!({
            "model": self.config.model,
            "input": text,
        });
        
        let response = self.client
            .post(&url)
            .header("Authorization", format!("Bearer {}", self.config.api_key))
            .header("Content-Type", "application/json")
            .json(&body)
            .send()
            .await
            .map_err(|e| EmbeddingError::HttpError(e.to_string()))?;
        
        let status = response.status();
        if !status.is_success() {
            let error_text = response.text().await.unwrap_or_default();
            return Err(EmbeddingError::ApiError(format!("HTTP {}: {}", status, error_text)));
        }
        
        let embedding_response: EmbeddingResponse = response
            .json()
            .await
            .map_err(|e| EmbeddingError::ParseError(e.to_string()))?;
        
        Ok(embedding_response.data[0].embedding.clone())
    }
    
    fn dimension(&self) -> usize {
        1536
    }
    
    fn model_name(&self) -> &str {
        &self.config.model
    }
}

#[derive(Debug, Deserialize)]
struct EmbeddingResponse {
    data: Vec<EmbeddingData>,
}

#[derive(Debug, Deserialize)]
struct EmbeddingData {
    embedding: Vec<f32>,
    index: i32,
}