# 🦜️🔗LangChain Rust
[![Latest Version]][crates.io]
[Latest Version]: https://img.shields.io/crates/v/langchain-rust.svg
[crates.io]: https://crates.io/crates/langchain-rust
⚡ Building applications with LLMs through composability, with Rust! ⚡
[](https://discord.gg/JJFcTFbanu)
[](https://langchain-rust.sellie.tech/get-started/quickstart)
## 🤔 What is this?
This is the Rust language implementation of [LangChain](https://github.com/langchain-ai/langchain).
## Current Features
- LLMs
- [x] [OpenAi](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_openai.rs)
- [x] [Azure OpenAi](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_azure_open_ai.rs)
- [x] [Ollama](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
- [x] [Anthropic Claude](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_anthropic_claude.rs)
- Embeddings
- [x] [OpenAi](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/embedding_openai.rs)
- [x] [Azure OpenAi](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/embedding_azure_open_ai.rs)
- [x] [Ollama](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/embedding_ollama.rs)
- [x] [Local FastEmbed](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/embedding_fastembed.rs)
- VectorStores
- [x] [OpenSearch](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/vector_store_opensearch.rs)
- [x] [Postgres](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/vector_store_postgres.rs)
- [x] [Qdrant](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/vector_store_qdrant.rs)
- [x] [Sqlite](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/vector_store_sqlite.rs)
- [x] [SurrealDB](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/vector_store_surrealdb/src/main.rs)
- Chain
- [x] [LLM Chain](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_chain.rs)
- [x] [Conversational Chain](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/conversational_chain.rs)
- [x] [Conversational Retriver Simple](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/conversational_retriver_simple_chain.rs)
- [x] [Conversational Retriver With Vector Store](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/conversational_retriver_chain_with_vector_store.rs)
- [x] [Sequential Chain](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/sequential_chain.rs)
- [x] [Q&A Chain](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/qa_chain.rs)
- [x] [SQL Chain](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/sql_chain.rs)
- Agents
- [x] [Chat Agent with Tools](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/agent.rs)
- [x] [Open AI Compatible Tools Agent](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/open_ai_tools_agent.rs)
- Tools
- [x] Serpapi/Google
- [x] DuckDuckGo Search
- [x] [Wolfram/Math](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/wolfram_tool.rs)
- [x] Command line
- [x] [Text2Speech](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/speech2text_openai.rs)
- Semantic Routing
- [x] [Static Routing](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/semantic_routes.rs)
- [x] [Dynamic Routing](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/dynamic_semantic_routes.rs)
- Document Loaders
- [x] PDF
```rust
use futures_util::StreamExt;
async fn main() {
let path = "./src/document_loaders/test_data/sample.pdf";
let loader = LoPdfLoader::from_path(path).expect("Failed to create PdfLoader");
let docs = loader
.load()
.await
.unwrap()
.map(|d| d.unwrap())
.collect::<Vec<_>>()
.await;
}
```
- [x] Pandoc
```rust
use futures_util::StreamExt;
async fn main() {
let path = "./src/document_loaders/test_data/sample.docx";
let loader = PandocLoader::from_path(InputFormat::Docx.to_string(), path)
.await
.expect("Failed to create PandocLoader");
let docs = loader
.load()
.await
.unwrap()
.map(|d| d.unwrap())
.collect::<Vec<_>>()
.await;
}
```
- [x] HTML
```rust
use futures_util::StreamExt;
use url::Url;
async fn main() {
let path = "./src/document_loaders/test_data/example.html";
let html_loader = HtmlLoader::from_path(path, Url::parse("https://example.com/").unwrap())
.expect("Failed to create html loader");
let documents = html_loader
.load()
.await
.unwrap()
.map(|x| x.unwrap())
.collect::<Vec<_>>()
.await;
}
```
- [x] CSV
```rust
use futures_util::StreamExt;
async fn main() {
let path = "./src/document_loaders/test_data/test.csv";
let columns = vec![
"name".to_string(),
"age".to_string(),
"city".to_string(),
"country".to_string(),
];
let csv_loader = CsvLoader::from_path(path, columns).expect("Failed to create csv loader");
let documents = csv_loader
.load()
.await
.unwrap()
.map(|x| x.unwrap())
.collect::<Vec<_>>()
.await;
}
```
- [x] Git commits
```rust
use futures_util::StreamExt;
async fn main() {
let path = "/path/to/git/repo";
let git_commit_loader = GitCommitLoader::from_path(path).expect("Failed to create git commit loader");
let documents = csv_loader
.load()
.await
.unwrap()
.map(|x| x.unwrap())
.collect::<Vec<_>>()
.await;
}
```
- [x] Source code
```rust
let loader_with_dir =
SourceCodeLoader::from_path("./src/document_loaders/test_data".to_string())
.with_dir_loader_options(DirLoaderOptions {
glob: None,
suffixes: Some(vec!["rs".to_string()]),
exclude: None,
});
let stream = loader_with_dir.load().await.unwrap();
let documents = stream.map(|x| x.unwrap()).collect::<Vec<_>>().await;
```
## Installation
This library heavily relies on `serde_json` for its operation.
### Step 1: Add `serde_json`
First, ensure `serde_json` is added to your Rust project.
```bash
cargo add serde_json
```
### Step 2: Add `langchain-rust`
Then, you can add `langchain-rust` to your Rust project.
#### Simple install
```bash
cargo add langchain-rust
```
#### With Sqlite
```bash
cargo add langchain-rust --features sqlite
```
Download additional sqlite_vss libraries from <https://github.com/asg017/sqlite-vss>
#### With Postgres
```bash
cargo add langchain-rust --features postgres
```
#### With SurrialDB
```bash
cargo add langchain-rust --features surrealdb
```
#### With Qdrant
```bash
cargo add langchain-rust --features qdrant
```
Please remember to replace the feature flags `sqlite`, `postgres` or `surrealdb` based on your
specific use case.
This will add both `serde_json` and `langchain-rust` as dependencies in your `Cargo.toml`
file. Now, when you build your project, both dependencies will be fetched and compiled, and will be available for use in your project.
Remember, `serde_json` is a necessary dependencies, and `sqlite`, `postgres` and `surrealdb`
are optional features that may be added according to project needs.
### Quick Start Conversational Chain
```rust
use langchain_rust::{
chain::{Chain, LLMChainBuilder},
fmt_message, fmt_placeholder, fmt_template,
language_models::llm::LLM,
llm::openai::{OpenAI, OpenAIModel},
message_formatter,
prompt::HumanMessagePromptTemplate,
prompt_args,
schemas::messages::Message,
template_fstring,
};
#[tokio::main]
async fn main() {
//We can then initialize the model:
// If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
// let open_ai = OpenAI::default()
// .with_config(
// OpenAIConfig::default()
// .with_api_key("<your_key>"),
// ).with_model(OpenAIModel::Gpt35.to_string());
let open_ai = OpenAI::default().with_model(OpenAIModel::Gpt35.to_string());
//Once you've installed and initialized the LLM of your choice, we can try using it! Let's ask it what LangSmith is - this is something that wasn't present in the training data so it shouldn't have a very good response.
let resp = open_ai.invoke("What is rust").await.unwrap();
println!("{}", resp);
// We can also guide it's response with a prompt template. Prompt templates are used to convert raw user input to a better input to the LLM.
let prompt = message_formatter![
fmt_message!(Message::new_system_message(
"You are world class technical documentation writer."
)),
fmt_template!(HumanMessagePromptTemplate::new(template_fstring!(
"{input}", "input"
)))
];
//We can now combine these into a simple LLM chain:
let chain = LLMChainBuilder::new()
.prompt(prompt)
.llm(open_ai.clone())
.build()
.unwrap();
//We can now invoke it and ask the same question. It still won't know the answer, but it should respond in a more proper tone for a technical writer!
match chain
.invoke(prompt_args! {
"input" => "Quien es el escritor de 20000 millas de viaje submarino",
})
.await
{
Ok(result) => {
println!("Result: {:?}", result);
}
Err(e) => panic!("Error invoking LLMChain: {:?}", e),
}
//If you want to prompt to have a list of messages you could use the `fmt_placeholder` macro
let prompt = message_formatter![
fmt_message!(Message::new_system_message(
"You are world class technical documentation writer."
)),
fmt_placeholder!("history"),
fmt_template!(HumanMessagePromptTemplate::new(template_fstring!(
"{input}", "input"
))),
];
let chain = LLMChainBuilder::new()
.prompt(prompt)
.llm(open_ai)
.build()
.unwrap();
match chain
.invoke(prompt_args! {
"input" => "Who is the writer of 20,000 Leagues Under the Sea, and what is my name?",
"history" => vec![
Message::new_human_message("My name is: luis"),
Message::new_ai_message("Hi luis"),
],
})
.await
{
Ok(result) => {
println!("Result: {:?}", result);
}
Err(e) => panic!("Error invoking LLMChain: {:?}", e),
}
}
```