langchainrust 0.2.15

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

// src/core/language_models/chat.rs
//! Chat model base trait.

use async_trait::async_trait;
use futures_util::Stream;
use serde::{Deserialize, Serialize};
use std::pin::Pin;
use crate::schema::Message;
use crate::RunnableConfig;
use crate::core::tools::ToolCall;
use super::BaseLanguageModel;

/// LLM result containing response content and metadata.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LLMResult {
    pub content: String,
    pub model: String,
    pub token_usage: Option<TokenUsage>,
    pub tool_calls: Option<Vec<ToolCall>>,
}

/// Token usage statistics.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TokenUsage {
    /// Input token count.
    pub prompt_tokens: usize,
    
    /// Output token count.
    pub completion_tokens: usize,
    
    /// Total token count.
    pub total_tokens: usize,
}

/// Base trait for chat models.
///
/// Extends BaseLanguageModel for chat scenarios.
/// Accepts message list as input, returns AI message.
#[async_trait]
pub trait BaseChatModel: BaseLanguageModel<Vec<Message>, LLMResult> {
    /// Chat with the model.
    /// 
    /// # Arguments
    /// * `messages` - Message list.
    /// * `config` - Optional configuration.
    /// 
    /// # Returns
    /// LLM result.
    async fn chat(
        &self, 
        messages: Vec<Message>, 
        config: Option<RunnableConfig>
    ) -> Result<LLMResult, Self::Error>;
    
    /// Stream chat with the model.
    /// 
    /// # Arguments
    /// * `messages` - Message list.
    /// * `config` - Optional configuration.
    /// 
    /// # Returns
    /// Stream of output chunks.
    async fn stream_chat(
        &self,
        messages: Vec<Message>,
        config: Option<RunnableConfig>
    ) -> Result<Pin<Box<dyn Stream<Item = Result<String, Self::Error>> + Send>>, Self::Error>;
    
    /// Chat with system prompt.
    /// 
    /// # Arguments
    /// * `system` - System prompt.
    /// * `messages` - Message list.
    /// 
    /// # Returns
    /// LLM result.
    async fn chat_with_system(
        &self,
        system: String,
        messages: Vec<Message>
    ) -> Result<LLMResult, Self::Error> {
        let full_messages = vec![Message::system(system)]
            .into_iter()
            .chain(messages)
            .collect();
        
        self.chat(full_messages, None).await
    }
}